forked from Archive/PX4-Autopilot
1434 lines
74 KiB
Python
Executable File
1434 lines
74 KiB
Python
Executable File
#! /usr/bin/env python
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from __future__ import print_function
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import argparse
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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from pyulog import *
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"""
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Performs a health assessment on the ecl EKF navigation estimator data contained in a an ULog file
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Outputs a health assessment summary in a csv file named <inputfilename>.mdat.csv
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Outputs summary plots in a pdf file named <inputfilename>.pdf
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"""
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parser = argparse.ArgumentParser(description='Analyse the estimator_status and ekf2_innovation message data')
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parser.add_argument('filename', metavar='file.ulg', help='ULog input file')
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def is_valid_directory(parser, arg):
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if os.path.isdir(arg):
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# Directory exists so return the directory
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return arg
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else:
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parser.error('The directory {} does not exist'.format(arg))
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args = parser.parse_args()
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ulog_file_name = args.filename
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ulog = ULog(ulog_file_name, None)
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data = ulog.data_list
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# extract data from innovations and status messages
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for d in data:
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if d.name == 'estimator_status':
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estimator_status = d.data
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print('found estimator_status data')
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for d in data:
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if d.name == 'ekf2_innovations':
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ekf2_innovations = d.data
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print('found ekf2_innovation data')
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# extract data from sensor reflight check message
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sensor_preflight = {}
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for d in data:
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if d.name == 'sensor_preflight':
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sensor_preflight = d.data
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print('found sensor_preflight data')
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# create summary plots
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# save the plots to PDF
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from matplotlib.backends.backend_pdf import PdfPages
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output_plot_filename = ulog_file_name + ".pdf"
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pp = PdfPages(output_plot_filename)
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# plot IMU consistency data
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if ('accel_inconsistency_m_s_s' in sensor_preflight.keys()) and ('gyro_inconsistency_rad_s' in sensor_preflight.keys()):
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plt.figure(0,figsize=(20,13))
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plt.subplot(2,1,1)
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plt.plot(sensor_preflight['accel_inconsistency_m_s_s'],'b')
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plt.title('IMU Consistency Check Levels')
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plt.ylabel('acceleration (m/s/s)')
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plt.xlabel('data index')
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plt.grid()
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plt.subplot(2,1,2)
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plt.plot(sensor_preflight['gyro_inconsistency_rad_s'],'b')
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plt.ylabel('angular rate (rad/s)')
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plt.xlabel('data index')
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pp.savefig()
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plt.close(0)
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# vertical velocity and position innovations
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plt.figure(1,figsize=(20,13))
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# generate max, min and 1-std metadata
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innov_time = 1e-6*ekf2_innovations['timestamp']
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status_time = 1e-6*estimator_status['timestamp']
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# generate metadata for velocity innovations
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innov_2_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[2]'])
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innov_2_max_time = innov_time[innov_2_max_arg]
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innov_2_max = np.amax(ekf2_innovations['vel_pos_innov[2]'])
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innov_2_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[2]'])
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innov_2_min_time = innov_time[innov_2_min_arg]
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innov_2_min = np.amin(ekf2_innovations['vel_pos_innov[2]'])
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s_innov_2_max = str(round(innov_2_max,2))
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s_innov_2_min = str(round(innov_2_min,2))
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#s_innov_2_std = str(round(np.std(ekf2_innovations['vel_pos_innov[2]']),2))
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# generate metadata for position innovations
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innov_5_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[5]'])
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innov_5_max_time = innov_time[innov_5_max_arg]
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innov_5_max = np.amax(ekf2_innovations['vel_pos_innov[5]'])
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innov_5_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[5]'])
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innov_5_min_time = innov_time[innov_5_min_arg]
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innov_5_min = np.amin(ekf2_innovations['vel_pos_innov[5]'])
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s_innov_5_max = str(round(innov_5_max,2))
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s_innov_5_min = str(round(innov_5_min,2))
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#s_innov_5_std = str(round(np.std(ekf2_innovations['vel_pos_innov[5]']),2))
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# generate plot for vertical velocity innovations
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plt.subplot(2,1,1)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['vel_pos_innov[2]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['vel_pos_innov_var[2]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['vel_pos_innov_var[2]']),'r')
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plt.title('Vertical Innovations')
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plt.ylabel('Down Vel (m/s)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_2_max_time, innov_2_max, 'max='+s_innov_2_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_2_min_time, innov_2_min, 'min='+s_innov_2_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_2_std],loc='upper left',frameon=False)
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# generate plot for vertical position innovations
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plt.subplot(2,1,2)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['vel_pos_innov[5]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['vel_pos_innov_var[5]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['vel_pos_innov_var[5]']),'r')
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plt.ylabel('Down Pos (m)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_5_max_time, innov_5_max, 'max='+s_innov_5_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_5_min_time, innov_5_min, 'min='+s_innov_5_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_5_std],loc='upper left',frameon=False)
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pp.savefig()
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plt.close(1)
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# horizontal velocity innovations
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plt.figure(2,figsize=(20,13))
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# generate North axis metadata
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innov_0_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[0]'])
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innov_0_max_time = innov_time[innov_0_max_arg]
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innov_0_max = np.amax(ekf2_innovations['vel_pos_innov[0]'])
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innov_0_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[0]'])
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innov_0_min_time = innov_time[innov_0_min_arg]
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innov_0_min = np.amin(ekf2_innovations['vel_pos_innov[0]'])
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s_innov_0_max = str(round(innov_0_max,2))
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s_innov_0_min = str(round(innov_0_min,2))
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#s_innov_0_std = str(round(np.std(ekf2_innovations['vel_pos_innov[0]']),2))
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# Generate East axis metadata
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innov_1_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[1]'])
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innov_1_max_time = innov_time[innov_1_max_arg]
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innov_1_max = np.amax(ekf2_innovations['vel_pos_innov[1]'])
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innov_1_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[1]'])
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innov_1_min_time = innov_time[innov_1_min_arg]
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innov_1_min = np.amin(ekf2_innovations['vel_pos_innov[1]'])
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s_innov_1_max = str(round(innov_1_max,2))
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s_innov_1_min = str(round(innov_1_min,2))
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#s_innov_1_std = str(round(np.std(ekf2_innovations['vel_pos_innov[1]']),2))
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# draw plots
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plt.subplot(2,1,1)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['vel_pos_innov[0]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['vel_pos_innov_var[0]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['vel_pos_innov_var[0]']),'r')
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plt.title('Horizontal Velocity Innovations')
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plt.ylabel('North Vel (m/s)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_0_max_time, innov_0_max, 'max='+s_innov_0_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_0_min_time, innov_0_min, 'min='+s_innov_0_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_0_std],loc='upper left',frameon=False)
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plt.subplot(2,1,2)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['vel_pos_innov[1]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['vel_pos_innov_var[1]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['vel_pos_innov_var[1]']),'r')
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plt.ylabel('East Vel (m/s)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_1_max_time, innov_1_max, 'max='+s_innov_1_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_1_min_time, innov_1_min, 'min='+s_innov_1_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_1_std],loc='upper left',frameon=False)
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pp.savefig()
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plt.close(2)
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# horizontal position innovations
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plt.figure(3,figsize=(20,13))
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# generate North axis metadata
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innov_3_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[3]'])
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innov_3_max_time = innov_time[innov_3_max_arg]
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innov_3_max = np.amax(ekf2_innovations['vel_pos_innov[3]'])
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innov_3_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[3]'])
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innov_3_min_time = innov_time[innov_3_min_arg]
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innov_3_min = np.amin(ekf2_innovations['vel_pos_innov[3]'])
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s_innov_3_max = str(round(innov_3_max,2))
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s_innov_3_min = str(round(innov_3_min,2))
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#s_innov_3_std = str(round(np.std(ekf2_innovations['vel_pos_innov[3]']),2))
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# generate East axis metadata
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innov_4_max_arg = np.argmax(ekf2_innovations['vel_pos_innov[4]'])
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innov_4_max_time = innov_time[innov_4_max_arg]
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innov_4_max = np.amax(ekf2_innovations['vel_pos_innov[4]'])
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innov_4_min_arg = np.argmin(ekf2_innovations['vel_pos_innov[4]'])
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innov_4_min_time = innov_time[innov_4_min_arg]
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innov_4_min = np.amin(ekf2_innovations['vel_pos_innov[4]'])
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s_innov_4_max = str(round(innov_4_max,2))
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s_innov_4_min = str(round(innov_4_min,2))
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#s_innov_4_std = str(round(np.std(ekf2_innovations['vel_pos_innov[4]']),2))
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# generate plots
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plt.subplot(2,1,1)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['vel_pos_innov[3]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['vel_pos_innov_var[3]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['vel_pos_innov_var[3]']),'r')
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plt.title('Horizontal Position Innovations')
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plt.ylabel('North Pos (m)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_3_max_time, innov_3_max, 'max='+s_innov_3_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_3_min_time, innov_3_min, 'min='+s_innov_3_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_3_std],loc='upper left',frameon=False)
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plt.subplot(2,1,2)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['vel_pos_innov[4]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['vel_pos_innov_var[4]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['vel_pos_innov_var[4]']),'r')
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plt.ylabel('East Pos (m)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_4_max_time, innov_4_max, 'max='+s_innov_4_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_4_min_time, innov_4_min, 'min='+s_innov_4_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_4_std],loc='upper left',frameon=False)
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pp.savefig()
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plt.close(3)
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# manetometer innovations
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plt.figure(4,figsize=(20,13))
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# generate X axis metadata
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innov_0_max_arg = np.argmax(ekf2_innovations['mag_innov[0]'])
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innov_0_max_time = innov_time[innov_0_max_arg]
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innov_0_max = np.amax(ekf2_innovations['mag_innov[0]'])
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innov_0_min_arg = np.argmin(ekf2_innovations['mag_innov[0]'])
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innov_0_min_time = innov_time[innov_0_min_arg]
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innov_0_min = np.amin(ekf2_innovations['mag_innov[0]'])
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s_innov_0_max = str(round(innov_0_max,3))
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s_innov_0_min = str(round(innov_0_min,3))
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#s_innov_0_std = str(round(np.std(ekf2_innovations['mag_innov[0]']),3))
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# generate Y axis metadata
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innov_1_max_arg = np.argmax(ekf2_innovations['mag_innov[1]'])
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innov_1_max_time = innov_time[innov_1_max_arg]
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innov_1_max = np.amax(ekf2_innovations['mag_innov[1]'])
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innov_1_min_arg = np.argmin(ekf2_innovations['mag_innov[1]'])
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innov_1_min_time = innov_time[innov_1_min_arg]
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innov_1_min = np.amin(ekf2_innovations['mag_innov[1]'])
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s_innov_1_max = str(round(innov_1_max,3))
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s_innov_1_min = str(round(innov_1_min,3))
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#s_innov_1_std = str(round(np.std(ekf2_innovations['mag_innov[1]']),3))
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# generate Z axis metadata
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innov_2_max_arg = np.argmax(ekf2_innovations['mag_innov[2]'])
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innov_2_max_time = innov_time[innov_2_max_arg]
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innov_2_max = np.amax(ekf2_innovations['mag_innov[2]'])
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innov_2_min_arg = np.argmin(ekf2_innovations['mag_innov[2]'])
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innov_2_min_time = innov_time[innov_2_min_arg]
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innov_2_min = np.amin(ekf2_innovations['mag_innov[2]'])
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s_innov_2_max = str(round(innov_2_max,3))
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s_innov_2_min = str(round(innov_2_min,3))
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#s_innov_2_std = str(round(np.std(ekf2_innovations['mag_innov[0]']),3))
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# draw plots
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plt.subplot(3,1,1)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['mag_innov[0]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['mag_innov_var[0]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['mag_innov_var[0]']),'r')
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plt.title('Magnetometer Innovations')
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plt.ylabel('X (Gauss)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_0_max_time, innov_0_max, 'max='+s_innov_0_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_0_min_time, innov_0_min, 'min='+s_innov_0_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_0_std],loc='upper left',frameon=False)
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plt.subplot(3,1,2)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['mag_innov[1]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['mag_innov_var[1]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['mag_innov_var[1]']),'r')
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plt.ylabel('Y (Gauss)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_1_max_time, innov_1_max, 'max='+s_innov_1_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_1_min_time, innov_1_min, 'min='+s_innov_1_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_1_std],loc='upper left',frameon=False)
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plt.subplot(3,1,3)
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plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['mag_innov[2]'],'b')
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plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['mag_innov_var[2]']),'r')
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plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['mag_innov_var[2]']),'r')
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plt.ylabel('Z (Gauss)')
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plt.xlabel('time (sec)')
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plt.grid()
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plt.text(innov_2_max_time, innov_2_max, 'max='+s_innov_2_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
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plt.text(innov_2_min_time, innov_2_min, 'min='+s_innov_2_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
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#plt.legend(['std='+s_innov_2_std],loc='upper left',frameon=False)
|
|
|
|
pp.savefig()
|
|
plt.close(4)
|
|
|
|
# magnetic heading innovations
|
|
plt.figure(5,figsize=(20,13))
|
|
|
|
# generate metadata
|
|
innov_0_max_arg = np.argmax(ekf2_innovations['heading_innov'])
|
|
innov_0_max_time = innov_time[innov_0_max_arg]
|
|
innov_0_max = np.amax(ekf2_innovations['heading_innov'])
|
|
|
|
innov_0_min_arg = np.argmin(ekf2_innovations['heading_innov'])
|
|
innov_0_min_time = innov_time[innov_0_min_arg]
|
|
innov_0_min = np.amin(ekf2_innovations['heading_innov'])
|
|
|
|
s_innov_0_max = str(round(innov_0_max,3))
|
|
s_innov_0_min = str(round(innov_0_min,3))
|
|
#s_innov_0_std = str(round(np.std(ekf2_innovations['heading_innov']),3))
|
|
|
|
# draw plot
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['heading_innov'],'b')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['heading_innov_var']),'r')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['heading_innov_var']),'r')
|
|
plt.title('Magnetic Heading Innovations')
|
|
plt.ylabel('Heaing (rad)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(innov_0_max_time, innov_0_max, 'max='+s_innov_0_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
|
|
plt.text(innov_0_min_time, innov_0_min, 'min='+s_innov_0_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
#plt.legend(['std='+s_innov_0_std],loc='upper left',frameon=False)
|
|
|
|
pp.savefig()
|
|
plt.close(5)
|
|
|
|
# air data innovations
|
|
plt.figure(6,figsize=(20,13))
|
|
|
|
# generate airspeed metadata
|
|
airspeed_innov_max_arg = np.argmax(ekf2_innovations['airspeed_innov'])
|
|
airspeed_innov_max_time = innov_time[airspeed_innov_max_arg]
|
|
airspeed_innov_max = np.amax(ekf2_innovations['airspeed_innov'])
|
|
|
|
airspeed_innov_min_arg = np.argmin(ekf2_innovations['airspeed_innov'])
|
|
airspeed_innov_min_time = innov_time[airspeed_innov_min_arg]
|
|
airspeed_innov_min = np.amin(ekf2_innovations['airspeed_innov'])
|
|
|
|
s_airspeed_innov_max = str(round(airspeed_innov_max,3))
|
|
s_airspeed_innov_min = str(round(airspeed_innov_min,3))
|
|
|
|
# generate sideslip metadata
|
|
beta_innov_max_arg = np.argmax(ekf2_innovations['beta_innov'])
|
|
beta_innov_max_time = innov_time[beta_innov_max_arg]
|
|
beta_innov_max = np.amax(ekf2_innovations['beta_innov'])
|
|
|
|
beta_innov_min_arg = np.argmin(ekf2_innovations['beta_innov'])
|
|
beta_innov_min_time = innov_time[beta_innov_min_arg]
|
|
beta_innov_min = np.amin(ekf2_innovations['beta_innov'])
|
|
|
|
s_beta_innov_max = str(round(beta_innov_max,3))
|
|
s_beta_innov_min = str(round(beta_innov_min,3))
|
|
|
|
# draw plots
|
|
plt.subplot(2,1,1)
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['airspeed_innov'],'b')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['airspeed_innov_var']),'r')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['airspeed_innov_var']),'r')
|
|
plt.title('True Airspeed Innovations')
|
|
plt.ylabel('innovation (m/sec)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(airspeed_innov_max_time, airspeed_innov_max, 'max='+s_airspeed_innov_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
|
|
plt.text(airspeed_innov_min_time, airspeed_innov_min, 'min='+s_airspeed_innov_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
|
|
plt.subplot(2,1,2)
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['beta_innov'],'b')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['beta_innov_var']),'r')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['beta_innov_var']),'r')
|
|
plt.title('Sythetic Sideslip Innovations')
|
|
plt.ylabel('innovation (rad)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(beta_innov_max_time, beta_innov_max, 'max='+s_beta_innov_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
|
|
plt.text(beta_innov_min_time, beta_innov_min, 'min='+s_beta_innov_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
|
|
pp.savefig()
|
|
plt.close(6)
|
|
|
|
# optical flow innovations
|
|
plt.figure(7,figsize=(20,13))
|
|
|
|
# generate X axis metadata
|
|
flow_innov_x_max_arg = np.argmax(ekf2_innovations['flow_innov[0]'])
|
|
flow_innov_x_max_time = innov_time[flow_innov_x_max_arg]
|
|
flow_innov_x_max = np.amax(ekf2_innovations['flow_innov[0]'])
|
|
|
|
flow_innov_x_min_arg = np.argmin(ekf2_innovations['flow_innov[0]'])
|
|
flow_innov_x_min_time = innov_time[flow_innov_x_min_arg]
|
|
flow_innov_x_min = np.amin(ekf2_innovations['flow_innov[0]'])
|
|
|
|
s_flow_innov_x_max = str(round(flow_innov_x_max,3))
|
|
s_flow_innov_x_min = str(round(flow_innov_x_min,3))
|
|
#s_flow_innov_x_std = str(round(np.std(ekf2_innovations['flow_innov[0]']),3))
|
|
|
|
# generate Y axis metadata
|
|
flow_innov_y_max_arg = np.argmax(ekf2_innovations['flow_innov[1]'])
|
|
flow_innov_y_max_time = innov_time[flow_innov_y_max_arg]
|
|
flow_innov_y_max = np.amax(ekf2_innovations['flow_innov[1]'])
|
|
|
|
flow_innov_y_min_arg = np.argmin(ekf2_innovations['flow_innov[1]'])
|
|
flow_innov_y_min_time = innov_time[flow_innov_y_min_arg]
|
|
flow_innov_y_min = np.amin(ekf2_innovations['flow_innov[1]'])
|
|
|
|
s_flow_innov_y_max = str(round(flow_innov_y_max,3))
|
|
s_flow_innov_y_min = str(round(flow_innov_y_min,3))
|
|
#s_flow_innov_y_std = str(round(np.std(ekf2_innovations['flow_innov[1]']),3))
|
|
|
|
# draw plots
|
|
plt.subplot(2,1,1)
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['flow_innov[0]'],'b')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['flow_innov_var[0]']),'r')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['flow_innov_var[0]']),'r')
|
|
plt.title('Optical Flow Innovations')
|
|
plt.ylabel('X (rad/sec)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(flow_innov_x_max_time, flow_innov_x_max, 'max='+s_flow_innov_x_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
|
|
plt.text(flow_innov_x_min_time, flow_innov_x_min, 'min='+s_flow_innov_x_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
#plt.legend(['std='+s_flow_innov_x_std],loc='upper left',frameon=False)
|
|
|
|
plt.subplot(2,1,2)
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],ekf2_innovations['flow_innov[1]'],'b')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],np.sqrt(ekf2_innovations['flow_innov_var[1]']),'r')
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'],-np.sqrt(ekf2_innovations['flow_innov_var[1]']),'r')
|
|
plt.ylabel('Y (rad/sec)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(flow_innov_y_max_time, flow_innov_y_max, 'max='+s_flow_innov_y_max, fontsize=12, horizontalalignment='left', verticalalignment='bottom')
|
|
plt.text(flow_innov_y_min_time, flow_innov_y_min, 'min='+s_flow_innov_y_min, fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
#plt.legend(['std='+s_flow_innov_y_std],loc='upper left',frameon=False)
|
|
|
|
pp.savefig()
|
|
plt.close(7)
|
|
|
|
# generate metadata for the normalised innovation consistency test levels
|
|
# a value > 1.0 means the measurement data for that test has been rejected by the EKF
|
|
|
|
# magnetometer data
|
|
mag_test_max_arg = np.argmax(estimator_status['mag_test_ratio'])
|
|
mag_test_max_time = status_time[mag_test_max_arg]
|
|
mag_test_max = np.amax(estimator_status['mag_test_ratio'])
|
|
mag_test_mean = np.mean(estimator_status['mag_test_ratio'])
|
|
|
|
# velocity data (GPS)
|
|
vel_test_max_arg = np.argmax(estimator_status['vel_test_ratio'])
|
|
vel_test_max_time = status_time[vel_test_max_arg]
|
|
vel_test_max = np.amax(estimator_status['vel_test_ratio'])
|
|
vel_test_mean = np.mean(estimator_status['vel_test_ratio'])
|
|
|
|
# horizontal position data (GPS or external vision)
|
|
pos_test_max_arg = np.argmax(estimator_status['pos_test_ratio'])
|
|
pos_test_max_time = status_time[pos_test_max_arg]
|
|
pos_test_max = np.amax(estimator_status['pos_test_ratio'])
|
|
pos_test_mean = np.mean(estimator_status['pos_test_ratio'])
|
|
|
|
# height data (Barometer, GPS or rangefinder)
|
|
hgt_test_max_arg = np.argmax(estimator_status['hgt_test_ratio'])
|
|
hgt_test_max_time = status_time[hgt_test_max_arg]
|
|
hgt_test_max = np.amax(estimator_status['hgt_test_ratio'])
|
|
hgt_test_mean = np.mean(estimator_status['hgt_test_ratio'])
|
|
|
|
# airspeed data
|
|
tas_test_max_arg = np.argmax(estimator_status['tas_test_ratio'])
|
|
tas_test_max_time = status_time[tas_test_max_arg]
|
|
tas_test_max = np.amax(estimator_status['tas_test_ratio'])
|
|
tas_test_mean = np.mean(estimator_status['tas_test_ratio'])
|
|
|
|
# height above ground data (rangefinder)
|
|
hagl_test_max_arg = np.argmax(estimator_status['hagl_test_ratio'])
|
|
hagl_test_max_time = status_time[hagl_test_max_arg]
|
|
hagl_test_max = np.amax(estimator_status['hagl_test_ratio'])
|
|
hagl_test_mean = np.mean(estimator_status['hagl_test_ratio'])
|
|
|
|
# plot normalised innovation test levels
|
|
plt.figure(8,figsize=(20,13))
|
|
|
|
if tas_test_max == 0.0:
|
|
n_plots = 3
|
|
else:
|
|
n_plots = 4
|
|
|
|
plt.subplot(n_plots,1,1)
|
|
plt.plot(status_time,estimator_status['mag_test_ratio'],'b')
|
|
plt.title('Normalised Innovation Test Levels')
|
|
plt.ylabel('mag')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(mag_test_max_time, mag_test_max, 'max='+str(round(mag_test_max,2))+' , mean='+str(round(mag_test_mean,2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom',color='b')
|
|
|
|
plt.subplot(n_plots,1,2)
|
|
plt.plot(status_time,estimator_status['vel_test_ratio'],'b')
|
|
plt.plot(status_time,estimator_status['pos_test_ratio'],'r')
|
|
plt.ylabel('vel,pos')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(vel_test_max_time, vel_test_max, 'vel max='+str(round(vel_test_max,2))+' , mean='+str(round(vel_test_mean,2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom',color='b')
|
|
plt.text(pos_test_max_time, pos_test_max, 'pos max='+str(round(pos_test_max,2))+' , mean='+str(round(pos_test_mean,2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom',color='r')
|
|
|
|
plt.subplot(n_plots,1,3)
|
|
plt.plot(status_time,estimator_status['hgt_test_ratio'],'b')
|
|
plt.ylabel('hgt')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(hgt_test_max_time, hgt_test_max, 'hgt max='+str(round(hgt_test_max,2))+' , mean='+str(round(hgt_test_mean,2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom',color='b')
|
|
|
|
if hagl_test_max > 0.0:
|
|
plt.plot(status_time,estimator_status['hagl_test_ratio'],'r')
|
|
plt.text(hagl_test_max_time, hagl_test_max, 'hagl max='+str(round(hagl_test_max,2))+' , mean='+str(round(hagl_test_mean,2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom',color='r')
|
|
plt.ylabel('hgt,HAGL')
|
|
|
|
if n_plots == 4:
|
|
plt.subplot(n_plots,1,4)
|
|
plt.plot(status_time,estimator_status['tas_test_ratio'],'b')
|
|
plt.ylabel('TAS')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(tas_test_max_time, tas_test_max, 'max='+str(round(tas_test_max,2))+' , mean='+str(round(tas_test_mean,2)), fontsize=12, horizontalalignment='left', verticalalignment='bottom',color='b')
|
|
|
|
pp.savefig()
|
|
plt.close(8)
|
|
|
|
# extract control mode metadata from estimator_status.control_mode_flags
|
|
# 0 - true if the filter tilt alignment is complete
|
|
# 1 - true if the filter yaw alignment is complete
|
|
# 2 - true if GPS measurements are being fused
|
|
# 3 - true if optical flow measurements are being fused
|
|
# 4 - true if a simple magnetic yaw heading is being fused
|
|
# 5 - true if 3-axis magnetometer measurement are being fused
|
|
# 6 - true if synthetic magnetic declination measurements are being fused
|
|
# 7 - true when the vehicle is airborne
|
|
# 8 - true when wind velocity is being estimated
|
|
# 9 - true when baro height is being fused as a primary height reference
|
|
# 10 - true when range finder height is being fused as a primary height reference
|
|
# 11 - true when range finder height is being fused as a primary height reference
|
|
# 12 - true when local position data from external vision is being fused
|
|
# 13 - true when yaw data from external vision measurements is being fused
|
|
# 14 - true when height data from external vision measurements is being fused
|
|
tilt_aligned = ((2**0 & estimator_status['control_mode_flags']) > 0)*1
|
|
yaw_aligned = ((2**1 & estimator_status['control_mode_flags']) > 0)*1
|
|
using_gps = ((2**2 & estimator_status['control_mode_flags']) > 0)*1
|
|
using_optflow = ((2**3 & estimator_status['control_mode_flags']) > 0)*1
|
|
using_magyaw = ((2**4 & estimator_status['control_mode_flags']) > 0)*1
|
|
using_mag3d = ((2**5 & estimator_status['control_mode_flags']) > 0)*1
|
|
using_magdecl = ((2**6 & estimator_status['control_mode_flags']) > 0)*1
|
|
airborne = ((2**7 & estimator_status['control_mode_flags']) > 0)*1
|
|
estimating_wind = ((2**8 & estimator_status['control_mode_flags']) > 0)*1
|
|
using_barohgt = ((2**9 & estimator_status['control_mode_flags']) > 0)*1
|
|
using_rnghgt = ((2**10 & estimator_status['control_mode_flags']) > 0)*1
|
|
using_gpshgt = ((2**11 & estimator_status['control_mode_flags']) > 0)*1
|
|
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
|
|
|
|
# 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))
|
|
in_air_transition_time = status_time[in_air_transtion_time_arg]
|
|
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')
|
|
else:
|
|
in_air_transition_time = float('NaN')
|
|
print('always on ground')
|
|
|
|
# calculate on-ground transition time
|
|
if (np.amin(np.diff(airborne)) < 0.0):
|
|
on_ground_transition_time_arg = np.argmin(np.diff(airborne))
|
|
on_ground_transition_time = status_time[on_ground_transition_time_arg]
|
|
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')
|
|
else:
|
|
on_ground_transition_time = float('NaN')
|
|
print('always on ground')
|
|
|
|
if (np.amax(np.diff(airborne)) > 0.5) and (np.amin(np.diff(airborne)) < -0.5):
|
|
if ((on_ground_transition_time - in_air_transition_time) > 0.0):
|
|
in_air_duration = on_ground_transition_time - in_air_transition_time;
|
|
else:
|
|
in_air_duration = float('NaN')
|
|
else:
|
|
in_air_duration = float('NaN')
|
|
|
|
# calculate alignment completion times
|
|
tilt_align_time_arg = np.argmax(np.diff(tilt_aligned))
|
|
tilt_align_time = status_time[tilt_align_time_arg]
|
|
yaw_align_time_arg = np.argmax(np.diff(yaw_aligned))
|
|
yaw_align_time = status_time[yaw_align_time_arg]
|
|
|
|
# calculate position aiding start times
|
|
gps_aid_time_arg = np.argmax(np.diff(using_gps))
|
|
gps_aid_time = status_time[gps_aid_time_arg]
|
|
optflow_aid_time_arg = np.argmax(np.diff(using_optflow))
|
|
optflow_aid_time = status_time[optflow_aid_time_arg]
|
|
evpos_aid_time_arg = np.argmax(np.diff(using_evpos))
|
|
evpos_aid_time = status_time[evpos_aid_time_arg]
|
|
|
|
# calculate height aiding start times
|
|
barohgt_aid_time_arg = np.argmax(np.diff(using_barohgt))
|
|
barohgt_aid_time = status_time[barohgt_aid_time_arg]
|
|
gpshgt_aid_time_arg = np.argmax(np.diff(using_gpshgt))
|
|
gpshgt_aid_time = status_time[gpshgt_aid_time_arg]
|
|
rnghgt_aid_time_arg = np.argmax(np.diff(using_rnghgt))
|
|
rnghgt_aid_time = status_time[rnghgt_aid_time_arg]
|
|
evhgt_aid_time_arg = np.argmax(np.diff(using_evhgt))
|
|
evhgt_aid_time = status_time[evhgt_aid_time_arg]
|
|
|
|
# calculate magnetometer aiding start times
|
|
using_magyaw_time_arg = np.argmax(np.diff(using_magyaw))
|
|
using_magyaw_time = status_time[using_magyaw_time_arg]
|
|
using_mag3d_time_arg = np.argmax(np.diff(using_mag3d))
|
|
using_mag3d_time = status_time[using_mag3d_time_arg]
|
|
using_magdecl_time_arg = np.argmax(np.diff(using_magdecl))
|
|
using_magdecl_time = status_time[using_magdecl_time_arg]
|
|
|
|
# control mode summary plot A
|
|
plt.figure(9,figsize=(20,13))
|
|
|
|
# subplot for alignment completion
|
|
plt.subplot(4,1,1)
|
|
plt.title('EKF Control Status - Figure A')
|
|
plt.plot(status_time,tilt_aligned,'b')
|
|
plt.plot(status_time,yaw_aligned,'r')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('aligned')
|
|
plt.grid()
|
|
if np.amin(tilt_aligned) > 0:
|
|
plt.text(tilt_align_time, 0.5, 'no pre-arm data - cannot calculate alignment completion times', fontsize=12, horizontalalignment='left', verticalalignment='center',color='black')
|
|
else:
|
|
plt.text(tilt_align_time, 0.33, 'tilt alignment at '+str(round(tilt_align_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
plt.text(yaw_align_time, 0.67, 'yaw alignment at '+str(round(tilt_align_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='r')
|
|
|
|
# subplot for position aiding
|
|
plt.subplot(4,1,2)
|
|
plt.plot(status_time,using_gps,'b')
|
|
plt.plot(status_time,using_optflow,'r')
|
|
plt.plot(status_time,using_evpos,'g')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('pos aiding')
|
|
plt.grid()
|
|
|
|
if np.amin(using_gps) > 0:
|
|
plt.text(gps_aid_time, 0.25, 'no pre-arm data - cannot calculate GPS aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
elif np.amax(using_gps) > 0:
|
|
plt.text(gps_aid_time, 0.25, 'GPS aiding at '+str(round(gps_aid_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
|
|
if np.amin(using_optflow) > 0:
|
|
plt.text(optflow_aid_time, 0.50, 'no pre-arm data - cannot calculate optical flow aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='r')
|
|
elif np.amax(using_optflow) > 0:
|
|
plt.text(optflow_aid_time, 0.50, 'optical flow aiding at '+str(round(optflow_aid_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='r')
|
|
|
|
if np.amin(using_evpos) > 0:
|
|
plt.text(evpos_aid_time, 0.75, 'no pre-arm data - cannot calculate external vision aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='g')
|
|
elif np.amax(using_evpos) > 0:
|
|
plt.text(evpos_aid_time, 0.75, 'external vision aiding at '+str(round(evpos_aid_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='g')
|
|
|
|
# subplot for height aiding
|
|
plt.subplot(4,1,3)
|
|
plt.plot(status_time,using_barohgt,'b')
|
|
plt.plot(status_time,using_gpshgt,'r')
|
|
plt.plot(status_time,using_rnghgt,'g')
|
|
plt.plot(status_time,using_evhgt,'c')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('hgt aiding')
|
|
plt.grid()
|
|
|
|
if np.amin(using_barohgt) > 0:
|
|
plt.text(barohgt_aid_time, 0.2, 'no pre-arm data - cannot calculate Baro aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
elif np.amax(using_barohgt) > 0:
|
|
plt.text(barohgt_aid_time, 0.2, 'Baro aiding at '+str(round(gps_aid_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
|
|
if np.amin(using_gpshgt) > 0:
|
|
plt.text(gpshgt_aid_time, 0.4, 'no pre-arm data - cannot calculate GPS aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='r')
|
|
elif np.amax(using_gpshgt) > 0:
|
|
plt.text(gpshgt_aid_time, 0.4, 'GPS aiding at '+str(round(gpshgt_aid_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='r')
|
|
|
|
if np.amin(using_rnghgt) > 0:
|
|
plt.text(rnghgt_aid_time, 0.6, 'no pre-arm data - cannot calculate rangfinder aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='g')
|
|
elif np.amax(using_rnghgt) > 0:
|
|
plt.text(rnghgt_aid_time, 0.6, 'rangefinder aiding at '+str(round(rnghgt_aid_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='g')
|
|
|
|
if np.amin(using_evhgt) > 0:
|
|
plt.text(evhgt_aid_time, 0.8, 'no pre-arm data - cannot calculate external vision aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='c')
|
|
elif np.amax(using_evhgt) > 0:
|
|
plt.text(evhgt_aid_time, 0.8, 'external vision aiding at '+str(round(evhgt_aid_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='c')
|
|
|
|
# subplot for magnetometer aiding
|
|
plt.subplot(4,1,4)
|
|
plt.plot(status_time,using_magyaw,'b')
|
|
plt.plot(status_time,using_mag3d,'r')
|
|
plt.plot(status_time,using_magdecl,'g')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('mag aiding')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
if np.amin(using_magyaw) > 0:
|
|
plt.text(using_magyaw_time, 0.25, 'no pre-arm data - cannot calculate magnetic yaw aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
elif np.amax(using_magyaw) > 0:
|
|
plt.text(using_magyaw_time, 0.25, 'magnetic yaw aiding at '+str(round(using_magyaw_time,1))+' sec', fontsize=12, horizontalalignment='right', verticalalignment='center',color='b')
|
|
|
|
if np.amin(using_mag3d) > 0:
|
|
plt.text(using_mag3d_time, 0.50, 'no pre-arm data - cannot calculate 3D magnetoemter aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='r')
|
|
elif np.amax(using_mag3d) > 0:
|
|
plt.text(using_mag3d_time, 0.50, 'magnetometer 3D aiding at '+str(round(using_mag3d_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='r')
|
|
|
|
if np.amin(using_magdecl) > 0:
|
|
plt.text(using_magdecl_time, 0.75, 'no pre-arm data - cannot magnetic declination aiding start time', fontsize=12, horizontalalignment='left', verticalalignment='center',color='g')
|
|
elif np.amax(using_magdecl) > 0:
|
|
plt.text(using_magdecl_time, 0.75, 'magnetic declination aiding at '+str(round(using_magdecl_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='g')
|
|
|
|
pp.savefig()
|
|
plt.close(9)
|
|
|
|
# control mode summary plot B
|
|
plt.figure(10,figsize=(20,13))
|
|
|
|
# subplot for airborne status
|
|
plt.subplot(2,1,1)
|
|
plt.title('EKF Control Status - Figure B')
|
|
plt.plot(status_time,airborne,'b')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('airborne')
|
|
plt.grid()
|
|
|
|
if np.amax(np.diff(airborne)) < 0.5:
|
|
plt.text(in_air_transition_time, 0.67, 'ground to air transition not detected', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
else:
|
|
plt.text(in_air_transition_time, 0.67, 'in-air at '+str(round(in_air_transition_time,1))+' sec', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
|
|
if np.amin(np.diff(airborne)) > -0.5:
|
|
plt.text(on_ground_transition_time, 0.33, 'air to ground transition not detected', fontsize=12, horizontalalignment='left', verticalalignment='center',color='b')
|
|
else:
|
|
plt.text(on_ground_transition_time, 0.33, 'on-ground at '+str(round(on_ground_transition_time,1))+' sec', fontsize=12, horizontalalignment='right', verticalalignment='center',color='b')
|
|
|
|
if in_air_duration > 0.0:
|
|
plt.text((in_air_transition_time+on_ground_transition_time)/2, 0.5, 'duration = '+str(round(in_air_duration,1))+' sec', fontsize=12, horizontalalignment='center', verticalalignment='center',color='b')
|
|
|
|
# subplot for wind estimation status
|
|
plt.subplot(2,1,2)
|
|
plt.plot(status_time,estimating_wind,'b')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('estimating wind')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(10)
|
|
|
|
# innovation_check_flags summary
|
|
plt.figure(11,figsize=(20,13))
|
|
# 0 - true if velocity observations have been rejected
|
|
# 1 - true if horizontal position observations have been rejected
|
|
# 2 - true if true if vertical position observations have been rejected
|
|
# 3 - true if the X magnetometer observation has been rejected
|
|
# 4 - true if the Y magnetometer observation has been rejected
|
|
# 5 - true if the Z magnetometer observation has been rejected
|
|
# 6 - true if the yaw observation has been rejected
|
|
# 7 - true if the airspeed observation has been rejected
|
|
# 8 - true if the height above ground observation has been rejected
|
|
# 9 - true if the X optical flow observation has been rejected
|
|
# 10 - true if the Y optical flow observation has been rejected
|
|
vel_innov_fail = ((2**0 & estimator_status['innovation_check_flags']) > 0)*1
|
|
posh_innov_fail = ((2**1 & estimator_status['innovation_check_flags']) > 0)*1
|
|
posv_innov_fail = ((2**2 & estimator_status['innovation_check_flags']) > 0)*1
|
|
magx_innov_fail = ((2**3 & estimator_status['innovation_check_flags']) > 0)*1
|
|
magy_innov_fail = ((2**4 & estimator_status['innovation_check_flags']) > 0)*1
|
|
magz_innov_fail = ((2**5 & estimator_status['innovation_check_flags']) > 0)*1
|
|
yaw_innov_fail = ((2**6 & estimator_status['innovation_check_flags']) > 0)*1
|
|
tas_innov_fail = ((2**7 & estimator_status['innovation_check_flags']) > 0)*1
|
|
hagl_innov_fail = ((2**8 & estimator_status['innovation_check_flags']) > 0)*1
|
|
ofx_innov_fail = ((2**9 & estimator_status['innovation_check_flags']) > 0)*1
|
|
ofy_innov_fail = ((2**10 & estimator_status['innovation_check_flags']) > 0)*1
|
|
|
|
plt.subplot(5,1,1)
|
|
plt.title('EKF Innovation Test Fails')
|
|
plt.plot(status_time,vel_innov_fail,'b',label='vel NED')
|
|
plt.plot(status_time,posh_innov_fail,'r',label='pos NE')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('failed')
|
|
plt.legend(loc='upper left')
|
|
plt.grid()
|
|
|
|
plt.subplot(5,1,2)
|
|
plt.plot(status_time,posv_innov_fail,'b',label='hgt absolute')
|
|
plt.plot(status_time,hagl_innov_fail,'r',label='hgt above ground')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('failed')
|
|
plt.legend(loc='upper left')
|
|
plt.grid()
|
|
|
|
plt.subplot(5,1,3)
|
|
plt.plot(status_time,magx_innov_fail,'b',label='mag_x')
|
|
plt.plot(status_time,magy_innov_fail,'r',label='mag_y')
|
|
plt.plot(status_time,magz_innov_fail,'g',label='mag_z')
|
|
plt.plot(status_time,yaw_innov_fail,'c',label='yaw')
|
|
plt.legend(loc='upper left')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('failed')
|
|
plt.grid()
|
|
|
|
plt.subplot(5,1,4)
|
|
plt.plot(status_time,tas_innov_fail,'b',label='airspeed')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('failed')
|
|
plt.legend(loc='upper left')
|
|
plt.grid()
|
|
|
|
plt.subplot(5,1,5)
|
|
plt.plot(status_time,ofx_innov_fail,'b',label='flow X')
|
|
plt.plot(status_time,ofy_innov_fail,'r',label='flow Y')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('failed')
|
|
plt.xlabel('time (sec')
|
|
plt.legend(loc='upper left')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(11)
|
|
|
|
# gps_check_fail_flags summary
|
|
plt.figure(12,figsize=(20,13))
|
|
# 0 : minimum required sat count fail
|
|
# 1 : minimum required GDoP fail
|
|
# 2 : maximum allowed horizontal position error fail
|
|
# 3 : maximum allowed vertical position error fail
|
|
# 4 : maximum allowed speed error fail
|
|
# 5 : maximum allowed horizontal position drift fail
|
|
# 6 : maximum allowed vertical position drift fail
|
|
# 7 : maximum allowed horizontal speed fail
|
|
# 8 : maximum allowed vertical velocity discrepancy fail
|
|
nsat_fail = ((2**0 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
gdop_fail = ((2**1 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
herr_fail = ((2**2 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
verr_fail = ((2**3 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
serr_fail = ((2**4 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
hdrift_fail = ((2**5 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
vdrift_fail = ((2**6 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
hspd_fail = ((2**7 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
veld_diff_fail = ((2**8 & estimator_status['gps_check_fail_flags']) > 0)*1
|
|
|
|
plt.subplot(2,1,1)
|
|
plt.title('GPS Direct Output Check Failures')
|
|
plt.plot(status_time,nsat_fail,'b',label='N sats')
|
|
plt.plot(status_time,gdop_fail,'r',label='GDOP')
|
|
plt.plot(status_time,herr_fail,'g',label='horiz pos error')
|
|
plt.plot(status_time,verr_fail,'c',label='vert pos error')
|
|
plt.plot(status_time,serr_fail,'m',label='speed error')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('failed')
|
|
plt.legend(loc='upper right')
|
|
plt.grid()
|
|
|
|
plt.subplot(2,1,2)
|
|
plt.title('GPS Derived Output Check Failures')
|
|
plt.plot(status_time,hdrift_fail,'b',label='horiz drift')
|
|
plt.plot(status_time,vdrift_fail,'r',label='vert drift')
|
|
plt.plot(status_time,hspd_fail,'g',label='horiz speed')
|
|
plt.plot(status_time,veld_diff_fail,'c',label='vert vel inconsistent')
|
|
plt.ylim(-0.1, 1.1)
|
|
plt.ylabel('failed')
|
|
plt.xlabel('time (sec')
|
|
plt.legend(loc='upper right')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(12)
|
|
|
|
# filter reported accuracy
|
|
plt.figure(13,figsize=(20,13))
|
|
plt.title('Reported Accuracy')
|
|
plt.plot(status_time,estimator_status['pos_horiz_accuracy'],'b',label='horizontal')
|
|
plt.plot(status_time,estimator_status['pos_vert_accuracy'],'r',label='vertical')
|
|
plt.ylabel('accuracy (m)')
|
|
plt.xlabel('time (sec')
|
|
plt.legend(loc='upper right')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(13)
|
|
|
|
# Plot the EKF IMU vibration metrics
|
|
plt.figure(14,figsize=(20,13))
|
|
|
|
vibe_coning_max_arg = np.argmax(estimator_status['vibe[0]'])
|
|
vibe_coning_max_time = status_time[vibe_coning_max_arg]
|
|
vibe_coning_max = np.amax(estimator_status['vibe[0]'])
|
|
|
|
vibe_hf_dang_max_arg = np.argmax(estimator_status['vibe[1]'])
|
|
vibe_hf_dang_max_time = status_time[vibe_hf_dang_max_arg]
|
|
vibe_hf_dang_max = np.amax(estimator_status['vibe[1]'])
|
|
|
|
vibe_hf_dvel_max_arg = np.argmax(estimator_status['vibe[2]'])
|
|
vibe_hf_dvel_max_time = status_time[vibe_hf_dvel_max_arg]
|
|
vibe_hf_dvel_max = np.amax(estimator_status['vibe[2]'])
|
|
|
|
plt.subplot(3,1,1)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , 1000.0 * estimator_status['vibe[0]'],'b')
|
|
plt.title('IMU Vibration Metrics')
|
|
plt.ylabel('Del Ang Coning (mrad)')
|
|
plt.grid()
|
|
plt.text(vibe_coning_max_time, 1000.0 * vibe_coning_max, 'max='+str(round(1000.0 * vibe_coning_max,5)), fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
|
|
plt.subplot(3,1,2)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , 1000.0 * estimator_status['vibe[1]'],'b')
|
|
plt.ylabel('HF Del Ang (mrad)')
|
|
plt.grid()
|
|
plt.text(vibe_hf_dang_max_time, 1000.0 * vibe_hf_dang_max, 'max='+str(round(1000.0 * vibe_hf_dang_max,3)), fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
|
|
plt.subplot(3,1,3)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['vibe[2]'],'b')
|
|
plt.ylabel('HF Del Vel (m/s)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(vibe_hf_dvel_max_time, vibe_hf_dvel_max, 'max='+str(round(vibe_hf_dvel_max,4)), fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
|
|
pp.savefig()
|
|
plt.close(14)
|
|
|
|
# Plot the EKF output observer tracking errors
|
|
plt.figure(15,figsize=(20,13))
|
|
|
|
ang_track_err_max_arg = np.argmax(ekf2_innovations['output_tracking_error[0]'])
|
|
ang_track_err_max_time = innov_time[ang_track_err_max_arg]
|
|
ang_track_err_max = np.amax(ekf2_innovations['output_tracking_error[0]'])
|
|
|
|
vel_track_err_max_arg = np.argmax(ekf2_innovations['output_tracking_error[1]'])
|
|
vel_track_err_max_time = innov_time[vel_track_err_max_arg]
|
|
vel_track_err_max = np.amax(ekf2_innovations['output_tracking_error[1]'])
|
|
|
|
pos_track_err_max_arg = np.argmax(ekf2_innovations['output_tracking_error[2]'])
|
|
pos_track_err_max_time = innov_time[pos_track_err_max_arg]
|
|
pos_track_err_max = np.amax(ekf2_innovations['output_tracking_error[2]'])
|
|
|
|
plt.subplot(3,1,1)
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'] , 1e3*ekf2_innovations['output_tracking_error[0]'],'b')
|
|
plt.title('Output Observer Tracking Error Magnitudes')
|
|
plt.ylabel('angles (mrad)')
|
|
plt.grid()
|
|
plt.text(ang_track_err_max_time, 1e3 * ang_track_err_max, 'max='+str(round(1e3 * ang_track_err_max,2)), fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
|
|
plt.subplot(3,1,2)
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'] , ekf2_innovations['output_tracking_error[1]'],'b')
|
|
plt.ylabel('velocity (m/s)')
|
|
plt.grid()
|
|
plt.text(vel_track_err_max_time, vel_track_err_max, 'max='+str(round(vel_track_err_max,2)), fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
|
|
plt.subplot(3,1,3)
|
|
plt.plot(1e-6*ekf2_innovations['timestamp'] , ekf2_innovations['output_tracking_error[2]'],'b')
|
|
plt.ylabel('position (m)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
plt.text(pos_track_err_max_time, pos_track_err_max, 'max='+str(round(pos_track_err_max,2)), fontsize=12, horizontalalignment='left', verticalalignment='top')
|
|
|
|
pp.savefig()
|
|
plt.close(15)
|
|
|
|
# Plot the delta angle bias estimates
|
|
plt.figure(16,figsize=(20,13))
|
|
|
|
plt.subplot(3,1,1)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[10]'],'b')
|
|
plt.title('Delta Angle Bias Estimates')
|
|
plt.ylabel('X (rad)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(3,1,2)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[11]'],'b')
|
|
plt.ylabel('Y (rad)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(3,1,3)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[12]'],'b')
|
|
plt.ylabel('Z (rad)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(16)
|
|
|
|
# Plot the delta velocity bias estimates
|
|
plt.figure(17,figsize=(20,13))
|
|
|
|
plt.subplot(3,1,1)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[13]'],'b')
|
|
plt.title('Delta Velocity Bias Estimates')
|
|
plt.ylabel('X (m/s)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(3,1,2)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[14]'],'b')
|
|
plt.ylabel('Y (m/s)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(3,1,3)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[15]'],'b')
|
|
plt.ylabel('Z (m/s)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(17)
|
|
|
|
# Plot the earth frame magnetic field estimates
|
|
|
|
plt.figure(18,figsize=(20,13))
|
|
plt.subplot(3,1,3)
|
|
strength = (estimator_status['states[16]']**2 + estimator_status['states[17]']**2 + estimator_status['states[18]']**2)**0.5
|
|
plt.plot(1e-6*estimator_status['timestamp'] , strength,'b')
|
|
plt.ylabel('strength (Gauss)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(3,1,1)
|
|
rad2deg = 57.2958
|
|
declination = rad2deg * np.arctan2(estimator_status['states[17]'],estimator_status['states[16]'])
|
|
plt.plot(1e-6*estimator_status['timestamp'] , declination,'b')
|
|
plt.title('Earth Magnetic Field Estimates')
|
|
plt.ylabel('declination (deg)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(3,1,2)
|
|
inclination = rad2deg * np.arcsin(estimator_status['states[18]'] / strength)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , inclination,'b')
|
|
plt.ylabel('inclination (deg)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(18)
|
|
|
|
# Plot the body frame magnetic field estimates
|
|
plt.figure(19,figsize=(20,13))
|
|
|
|
plt.subplot(3,1,1)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[19]'],'b')
|
|
plt.title('Magnetomer Bias Estimates')
|
|
plt.ylabel('X (Gauss)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(3,1,2)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[20]'],'b')
|
|
plt.ylabel('Y (Gauss)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(3,1,3)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[21]'],'b')
|
|
plt.ylabel('Z (Gauss)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(19)
|
|
|
|
# Plot the EKF wind estimates
|
|
plt.figure(20,figsize=(20,13))
|
|
|
|
plt.subplot(2,1,1)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[22]'],'b')
|
|
plt.title('Wind Velocity Estimates')
|
|
plt.ylabel('North (m/s)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
plt.subplot(2,1,2)
|
|
plt.plot(1e-6*estimator_status['timestamp'] , estimator_status['states[23]'],'b')
|
|
plt.ylabel('East (m/s)')
|
|
plt.xlabel('time (sec)')
|
|
plt.grid()
|
|
|
|
pp.savefig()
|
|
plt.close(20)
|
|
|
|
# close the pdf file
|
|
pp.close()
|
|
|
|
# don't display to screen
|
|
#plt.show()
|
|
|
|
# clase all figures
|
|
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.argmin(status_time[np.where(status_time > (in_air_transition_time+5.0))])
|
|
early_end_index = np.argmax(status_time[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.argmin(status_time[np.where(status_time > in_air_transition_time)])
|
|
end_index = np.argmax(status_time[np.where(status_time < on_ground_transition_time)])
|
|
num_valid_values = (end_index - start_index +1)
|
|
|
|
# also find the start and finish indexes for the innovation data
|
|
innov_late_start_index = np.argmin(innov_time[np.where(innov_time > (in_air_transition_time+5.0))])
|
|
innov_early_end_index = np.argmax(innov_time[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.argmin(innov_time[np.where(innov_time > in_air_transition_time)])
|
|
innov_end_index = np.argmax(innov_time[np.where(innov_time < on_ground_transition_time)])
|
|
innov_num_valid_values = (innov_end_index - innov_start_index +1)
|
|
|
|
# define dictionary of test results and descriptions
|
|
test_results = {
|
|
'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'],
|
|
'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'],
|
|
'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'],
|
|
'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'],
|
|
'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'],
|
|
'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'],
|
|
'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'],
|
|
'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'],
|
|
'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'],
|
|
'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'],
|
|
'mag_percentage_red':[float('NaN'),'The percentage of in-flight consolidated magnetic field sensor innovation consistency test values > 1.0.'],
|
|
'mag_percentage_amber':[float('NaN'),'The percentage of in-flight consolidated magnetic field sensor innovation consistency test values > 0.5.'],
|
|
'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.'],
|
|
'yaw_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the yaw sensor innovation consistency test.'],
|
|
'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.'],
|
|
'vel_percentage_red':[float('NaN'),'The percentage of in-flight velocity sensor consolidated innovation consistency test values > 1.0.'],
|
|
'vel_percentage_amber':[float('NaN'),'The percentage of in-flight velocity sensor consolidated innovation consistency test values > 0.5.'],
|
|
'vel_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'],
|
|
'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.'],
|
|
'pos_percentage_red':[float('NaN'),'The percentage of in-flight position sensor consolidated innovation consistency test values > 1.0.'],
|
|
'pos_percentage_amber':[float('NaN'),'The percentage of in-flight position sensor consolidated innovation consistency test values > 0.5.'],
|
|
'pos_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the velocity sensor consolidated innovation consistency test.'],
|
|
'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.'],
|
|
'hgt_percentage_red':[float('NaN'),'The percentage of in-flight height sensor innovation consistency test values > 1.0.'],
|
|
'hgt_percentage_amber':[float('NaN'),'The percentage of in-flight height sensor innovation consistency test values > 0.5.'],
|
|
'hgt_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the height sensor innovation consistency test.'],
|
|
'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.'],
|
|
'tas_percentage_red':[float('NaN'),'The percentage of in-flight airspeed sensor innovation consistency test values > 1.0.'],
|
|
'tas_percentage_amber':[float('NaN'),'The percentage of in-flight airspeed sensor innovation consistency test values > 0.5.'],
|
|
'tas_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the airspeed sensor innovation consistency test.'],
|
|
'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.'],
|
|
'hagl_percentage_red':[float('NaN'),'The percentage of in-flight height above ground sensor innovation consistency test values > 1.0.'],
|
|
'hagl_percentage_amber':[float('NaN'),'The percentage of in-flight height above ground sensor innovation consistency test values > 0.5.'],
|
|
'hagl_fail_percentage':[float('NaN'),'The percentage of in-flight recorded failure events for the height above ground sensor innovation consistency test.'],
|
|
'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.'],
|
|
'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.'],
|
|
'filter_faults_max':[float('NaN'),'Largest recorded value of the filter internal fault bitmask. Should always be zero.'],
|
|
'imu_coning_peak':[float('NaN'),'Peak in-flight value of the IMU delta angle coning vibration metric (rad)'],
|
|
'imu_coning_mean':[float('NaN'),'Mean in-flight value of the IMU delta angle coning vibration metric (rad)'],
|
|
'imu_hfdang_peak':[float('NaN'),'Peak in-flight value of the IMU delta angle high frequency vibration metric (rad)'],
|
|
'imu_hfdang_mean':[float('NaN'),'Mean in-flight value of the IMU delta angle high frequency vibration metric (rad)'],
|
|
'imu_hfdvel_peak':[float('NaN'),'Peak in-flight value of the IMU delta velocity high frequency vibration metric (m/s)'],
|
|
'imu_hfdvel_mean':[float('NaN'),'Mean in-flight value of the IMU delta velocity high frequency vibration metric (m/s)'],
|
|
'output_obs_ang_err_median':[float('NaN'),'Median in-flight value of the output observer angular error (rad)'],
|
|
'output_obs_vel_err_median':[float('NaN'),'Median in-flight value of the output observer velocity error (m/s)'],
|
|
'output_obs_pos_err_median':[float('NaN'),'Median in-flight value of the output observer position error (m)'],
|
|
'imu_dang_bias_median':[float('NaN'),'Median in-flight value of the delta angle bias vector length (rad)'],
|
|
'imu_dvel_bias_median':[float('NaN'),'Median in-flight value of the delta velocity bias vector length (m/s)'],
|
|
'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.'],
|
|
'yaw_align_time':[float('NaN'),'The time in seconds measured from startup that the EKF completed the yaw alignment.'],
|
|
'in_air_transition_time':[round(in_air_transition_time,1),'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.'],
|
|
'on_ground_transition_time':[round(on_ground_transition_time,1),'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.'],
|
|
}
|
|
|
|
# generate test metadata
|
|
|
|
# reduction of innovation message data
|
|
if (innov_early_end_index > (innov_late_start_index + 100)):
|
|
# Output Observer Tracking Errors
|
|
test_results['output_obs_ang_err_median'][0] = np.median(ekf2_innovations['output_tracking_error[0]'][innov_late_start_index:innov_early_end_index])
|
|
test_results['output_obs_vel_err_median'][0] = np.median(ekf2_innovations['output_tracking_error[1]'][innov_late_start_index:innov_early_end_index])
|
|
test_results['output_obs_pos_err_median'][0] = np.median(ekf2_innovations['output_tracking_error[2]'][innov_late_start_index:innov_early_end_index])
|
|
|
|
# reduction of status message data
|
|
if (early_end_index > (late_start_index + 100)):
|
|
# IMU vibration checks
|
|
temp = np.amax(estimator_status['vibe[0]'][late_start_index:early_end_index])
|
|
if (temp > 0.0):
|
|
test_results['imu_coning_peak'][0] = temp
|
|
test_results['imu_coning_mean'][0] = np.mean(estimator_status['vibe[0]'][late_start_index:early_end_index])
|
|
temp = np.amax(estimator_status['vibe[1]'][late_start_index:early_end_index])
|
|
if (temp > 0.0):
|
|
test_results['imu_hfdang_peak'][0] = temp
|
|
test_results['imu_hfdang_mean'][0] = np.mean(estimator_status['vibe[1]'][late_start_index:early_end_index])
|
|
temp = np.amax(estimator_status['vibe[2]'][late_start_index:early_end_index])
|
|
if (temp > 0.0):
|
|
test_results['imu_hfdvel_peak'][0] = temp
|
|
test_results['imu_hfdvel_mean'][0] = np.mean(estimator_status['vibe[2]'][late_start_index:early_end_index])
|
|
|
|
# Magnetometer Sensor Checks
|
|
if (np.amax(yaw_aligned) > 0.5):
|
|
mag_num_red = (estimator_status['mag_test_ratio'][start_index:end_index] > 1.0).sum()
|
|
mag_num_amber = (estimator_status['mag_test_ratio'][start_index:end_index] > 0.5).sum() - mag_num_red
|
|
test_results['mag_percentage_red'][0] = 100.0 * mag_num_red / num_valid_values_trimmed
|
|
test_results['mag_percentage_amber'][0] = 100.0 * mag_num_amber / num_valid_values_trimmed
|
|
test_results['mag_test_max'][0] = np.amax(estimator_status['mag_test_ratio'][late_start_index:early_end_index])
|
|
test_results['mag_test_mean'][0] = np.mean(estimator_status['mag_test_ratio'][start_index:end_index])
|
|
test_results['magx_fail_percentage'][0] = 100.0 * (magx_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
|
|
test_results['magy_fail_percentage'][0] = 100.0 * (magy_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
|
|
test_results['magz_fail_percentage'][0] = 100.0 * (magz_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
|
|
test_results['yaw_fail_percentage'][0] = 100.0 * (yaw_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
|
|
|
|
# Velocity Sensor Checks
|
|
if (np.amax(using_gps) > 0.5):
|
|
vel_num_red = (estimator_status['vel_test_ratio'][start_index:end_index] > 1.0).sum()
|
|
vel_num_amber = (estimator_status['vel_test_ratio'][start_index:end_index] > 0.5).sum() - vel_num_red
|
|
test_results['vel_percentage_red'][0] = 100.0 * vel_num_red / num_valid_values
|
|
test_results['vel_percentage_amber'][0] = 100.0 * vel_num_amber / num_valid_values
|
|
test_results['vel_test_max'][0] = np.amax(estimator_status['vel_test_ratio'][start_index:end_index])
|
|
test_results['vel_test_mean'][0] = np.mean(estimator_status['vel_test_ratio'][start_index:end_index])
|
|
test_results['vel_fail_percentage'][0] = 100.0 * (vel_innov_fail[start_index:end_index] > 0.5).sum() / num_valid_values
|
|
|
|
# Position Sensor Checks
|
|
if ((np.amax(using_gps) > 0.5) or (np.amax(using_evpos) > 0.5)):
|
|
pos_num_red = (estimator_status['pos_test_ratio'][start_index:end_index] > 1.0).sum()
|
|
pos_num_amber = (estimator_status['pos_test_ratio'][start_index:end_index] > 0.5).sum() - pos_num_red
|
|
test_results['pos_percentage_red'][0] = 100.0 * pos_num_red / num_valid_values
|
|
test_results['pos_percentage_amber'][0] = 100.0 * pos_num_amber / num_valid_values
|
|
test_results['pos_test_max'][0] = np.amax(estimator_status['pos_test_ratio'][start_index:end_index])
|
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test_results['pos_test_mean'][0] = np.mean(estimator_status['pos_test_ratio'][start_index:end_index])
|
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test_results['pos_fail_percentage'][0] = 100.0 * (posh_innov_fail[start_index:end_index] > 0.5).sum() / num_valid_values
|
|
|
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# Height Sensor Checks
|
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hgt_num_red = (estimator_status['hgt_test_ratio'][late_start_index:early_end_index] > 1.0).sum()
|
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hgt_num_amber = (estimator_status['hgt_test_ratio'][late_start_index:early_end_index] > 0.5).sum() - hgt_num_red
|
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test_results['hgt_percentage_red'][0] = 100.0 * hgt_num_red / num_valid_values_trimmed
|
|
test_results['hgt_percentage_amber'][0] = 100.0 * hgt_num_amber / num_valid_values_trimmed
|
|
test_results['hgt_test_max'][0] = np.amax(estimator_status['hgt_test_ratio'][late_start_index:early_end_index])
|
|
test_results['hgt_test_mean'][0] = np.mean(estimator_status['hgt_test_ratio'][late_start_index:early_end_index])
|
|
test_results['hgt_fail_percentage'][0] = 100.0 * (posv_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
|
|
|
|
# Airspeed Sensor Checks
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if (tas_test_max > 0.0):
|
|
tas_num_red = (estimator_status['tas_test_ratio'][start_index:end_index] > 1.0).sum()
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tas_num_amber = (estimator_status['tas_test_ratio'][start_index:end_index] > 0.5).sum() - tas_num_red
|
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test_results['tas_percentage_red'][0] = 100.0 * tas_num_red / num_valid_values
|
|
test_results['tas_percentage_amber'][0] = 100.0 * tas_num_amber / num_valid_values
|
|
test_results['tas_test_max'][0] = np.amax(estimator_status['tas_test_ratio'][start_index:end_index])
|
|
test_results['tas_test_mean'][0] = np.mean(estimator_status['tas_test_ratio'][start_index:end_index])
|
|
test_results['tas_fail_percentage'][0] = 100.0 * (tas_innov_fail[start_index:end_index] > 0.5).sum() / num_valid_values
|
|
|
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# HAGL Sensor Checks
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|
if (hagl_test_max > 0.0):
|
|
hagl_num_red = (estimator_status['hagl_test_ratio'][start_index:end_index] > 1.0).sum()
|
|
hagl_num_amber = (estimator_status['hagl_test_ratio'][start_index:end_index] > 0.5).sum() - hagl_num_red
|
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test_results['hagl_percentage_red'][0] = 100.0 * hagl_num_red / num_valid_values
|
|
test_results['hagl_percentage_amber'][0] = 100.0 * hagl_num_amber / num_valid_values
|
|
test_results['hagl_test_max'][0] = np.amax(estimator_status['hagl_test_ratio'][start_index:end_index])
|
|
test_results['hagl_test_mean'][0] = np.mean(estimator_status['hagl_test_ratio'][start_index:end_index])
|
|
test_results['hagl_fail_percentage'][0] = 100.0 * (hagl_innov_fail[start_index:end_index] > 0.5).sum() / num_valid_values
|
|
|
|
# optical flow sensor checks
|
|
if (np.amax(using_optflow) > 0.5):
|
|
test_results['ofx_fail_percentage'][0] = 100.0 * (ofx_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
|
|
test_results['ofy_fail_percentage'][0] = 100.0 * (ofy_innov_fail[late_start_index:early_end_index] > 0.5).sum() / num_valid_values_trimmed
|
|
|
|
# IMU bias checks
|
|
test_results['imu_dang_bias_median'][0] = (np.median(estimator_status['states[10]'])**2 + np.median(estimator_status['states[11]'])**2 + np.median(estimator_status['states[12]'])**2)**0.5
|
|
test_results['imu_dvel_bias_median'][0] = (np.median(estimator_status['states[13]'])**2 + np.median(estimator_status['states[14]'])**2 + np.median(estimator_status['states[15]'])**2)**0.5
|
|
|
|
# Check for internal filter nummerical faults
|
|
test_results['filter_faults_max'][0] = np.amax(estimator_status['filter_fault_flags'])
|
|
|
|
# TODO - process the following bitmask's when they have been properly documented in the uORB topic
|
|
#estimator_status['health_flags']
|
|
#estimator_status['timeout_flags']
|
|
|
|
# calculate a master status - Fail, Warning, Pass
|
|
# get the dictionary of fail and warning test thresholds from a csv file
|
|
filename = "check_level_dict.csv"
|
|
file = open(filename)
|
|
check_levels = { }
|
|
for line in file:
|
|
x = line.split(",")
|
|
a=x[0]
|
|
b=x[1]
|
|
check_levels[a]=float(b)
|
|
file.close()
|
|
|
|
# print out the check levels
|
|
print('Using test criteria loaded from '+filename)
|
|
#for N in check_levels:
|
|
# val = check_levels.get(N)
|
|
# print(N+' = '+str(val), end='\n')
|
|
|
|
# check test results against levels to provide a master status
|
|
|
|
# check for warnings
|
|
if (test_results.get('mag_percentage_amber')[0] > check_levels.get('mag_amber_warn_pct')):
|
|
test_results['master_status'][0] = 'Warning'
|
|
test_results['mag_sensor_status'][0] = 'Warning'
|
|
if (test_results.get('vel_percentage_amber')[0] > check_levels.get('vel_amber_warn_pct')):
|
|
test_results['master_status'][0] = 'Warning'
|
|
test_results['vel_sensor_status'][0] = 'Warning'
|
|
if (test_results.get('pos_percentage_amber')[0] > check_levels.get('pos_amber_warn_pct')):
|
|
test_results['master_status'][0] = 'Warning'
|
|
test_results['pos_sensor_status'][0] = 'Warning'
|
|
if (test_results.get('hgt_percentage_amber')[0] > check_levels.get('hgt_amber_warn_pct')):
|
|
test_results['master_status'][0] = 'Warning'
|
|
test_results['hgt_sensor_status'][0] = 'Warning'
|
|
if (test_results.get('hagl_percentage_amber')[0] > check_levels.get('hagl_amber_warn_pct')):
|
|
test_results['master_status'][0] = 'Warning'
|
|
test_results['hagl_sensor_status'][0] = 'Warning'
|
|
if (test_results.get('tas_percentage_amber')[0] > check_levels.get('tas_amber_warn_pct')):
|
|
test_results['master_status'][0] = 'Warning'
|
|
test_results['tas_sensor_status'][0] = 'Warning'
|
|
|
|
# check for IMU sensor warnings
|
|
if ((test_results.get('imu_coning_peak')[0] > check_levels.get('imu_coning_peak_warn')) or
|
|
(test_results.get('imu_coning_mean')[0] > check_levels.get('imu_coning_mean_warn')) or
|
|
(test_results.get('imu_hfdang_peak')[0] > check_levels.get('imu_hfdang_peak_warn')) or
|
|
(test_results.get('imu_hfdang_mean')[0] > check_levels.get('imu_hfdang_mean_warn')) or
|
|
(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'
|
|
|
|
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'
|
|
|
|
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'
|
|
|
|
# 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
|
|
(test_results.get('magz_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or
|
|
(test_results.get('mag_percentage_amber')[0] > check_levels.get('mag_amber_fail_pct'))):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['mag_sensor_status'][0] = 'Fail'
|
|
if (test_results.get('yaw_fail_percentage')[0] > check_levels.get('yaw_fail_pct')):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['yaw_sensor_status'][0] = 'Fail'
|
|
if ((test_results.get('vel_fail_percentage')[0] > check_levels.get('vel_fail_pct')) or
|
|
(test_results.get('vel_percentage_amber')[0] > check_levels.get('vel_amber_fail_pct'))):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['vel_sensor_status'][0] = 'Fail'
|
|
if ((test_results.get('pos_fail_percentage')[0] > check_levels.get('pos_fail_pct')) or
|
|
(test_results.get('pos_percentage_amber')[0] > check_levels.get('pos_amber_fail_pct'))):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['pos_sensor_status'][0] = 'Fail'
|
|
if ((test_results.get('hgt_fail_percentage')[0] > check_levels.get('hgt_fail_pct')) or
|
|
(test_results.get('hgt_percentage_amber')[0] > check_levels.get('hgt_amber_fail_pct'))):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['hgt_sensor_status'][0] = 'Fail'
|
|
if ((test_results.get('tas_fail_percentage')[0] > check_levels.get('tas_fail_pct')) or
|
|
(test_results.get('tas_percentage_amber')[0] > check_levels.get('tas_amber_fail_pct'))):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['tas_sensor_status'][0] = 'Fail'
|
|
if ((test_results.get('hagl_fail_percentage')[0] > check_levels.get('hagl_fail_pct')) or
|
|
(test_results.get('hagl_percentage_amber')[0] > check_levels.get('hagl_amber_fail_pct'))):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['hagl_sensor_status'][0] = 'Fail'
|
|
if ((test_results.get('ofx_fail_percentage')[0] > check_levels.get('flow_fail_pct')) or
|
|
(test_results.get('ofy_fail_percentage')[0] > check_levels.get('flow_fail_pct'))):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['flow_sensor_status'][0] = 'Fail'
|
|
if (test_results.get('filter_faults_max')[0] > 0):
|
|
test_results['master_status'][0] = 'Fail'
|
|
test_results['filter_fault_status'][0] = 'Fail'
|
|
|
|
# print master test status to console
|
|
if (test_results['master_status'][0] == 'Pass'):
|
|
print('No anomalies detected')
|
|
elif (test_results['master_status'][0] == 'Warning'):
|
|
print('Minor anomalies detected')
|
|
elif (test_results['master_status'][0] == 'Fail'):
|
|
print('Major anomalies detected')
|
|
|
|
# write metadata to a .csv file
|
|
test_results_filename = ulog_file_name + ".mdat.csv"
|
|
file = open(test_results_filename,"w")
|
|
|
|
file.write("name,value,description\n")
|
|
|
|
# loop through the test results dictionary and write each entry on a separate row, with data comma separated
|
|
# save data in alphabetical order
|
|
key_list = list(test_results.keys())
|
|
key_list.sort()
|
|
for key in key_list:
|
|
file.write(key+","+str(test_results[key][0])+","+test_results[key][1]+"\n")
|
|
|
|
file.close()
|
|
|
|
print('Test results written to ' + test_results_filename)
|
|
print('Plots saved to ' + output_plot_filename)
|