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1382 lines
88 KiB
1382 lines
88 KiB
import numpy as np |
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# matplotlib don't use Xwindows backend (must be before pyplot import) |
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import matplotlib |
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matplotlib.use('Agg') |
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import matplotlib.pyplot as plt |
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from matplotlib.backends.backend_pdf import PdfPages |
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def analyse_ekf(estimator_status, ekf2_innovations, sensor_preflight, check_levels, |
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plot=False, output_plot_filename=None, late_start_early_ending=True): |
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if plot: |
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# create summary plots |
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# save the plots to 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 ( |
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'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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# 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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if plot: |
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# vertical velocity and position innovations |
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plt.figure(1, figsize=(20, 13)) |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_2_min_time, innov_2_min, 'min=' + s_innov_2_min, fontsize=12, horizontalalignment='left', |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_5_min_time, innov_5_min, 'min=' + s_innov_5_min, fontsize=12, horizontalalignment='left', |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_0_min_time, innov_0_min, 'min=' + s_innov_0_min, fontsize=12, horizontalalignment='left', |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_1_min_time, innov_1_min, 'min=' + s_innov_1_min, fontsize=12, horizontalalignment='left', |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_3_min_time, innov_3_min, 'min=' + s_innov_3_min, fontsize=12, horizontalalignment='left', |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_4_min_time, innov_4_min, 'min=' + s_innov_4_min, fontsize=12, horizontalalignment='left', |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_0_min_time, innov_0_min, 'min=' + s_innov_0_min, fontsize=12, horizontalalignment='left', |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_1_min_time, innov_1_min, 'min=' + s_innov_1_min, fontsize=12, horizontalalignment='left', |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_2_min_time, innov_2_min, 'min=' + s_innov_2_min, fontsize=12, horizontalalignment='left', |
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verticalalignment='top') |
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# plt.legend(['std='+s_innov_2_std],loc='upper left',frameon=False) |
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pp.savefig() |
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plt.close(4) |
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# magnetic heading innovations |
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plt.figure(5, figsize=(20, 13)) |
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# generate metadata |
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innov_0_max_arg = np.argmax(ekf2_innovations['heading_innov']) |
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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['heading_innov']) |
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innov_0_min_arg = np.argmin(ekf2_innovations['heading_innov']) |
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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['heading_innov']) |
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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['heading_innov']),3)) |
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# draw plot |
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plt.plot(1e-6 * ekf2_innovations['timestamp'], ekf2_innovations['heading_innov'], 'b') |
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plt.plot(1e-6 * ekf2_innovations['timestamp'], np.sqrt(ekf2_innovations['heading_innov_var']), 'r') |
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plt.plot(1e-6 * ekf2_innovations['timestamp'], -np.sqrt(ekf2_innovations['heading_innov_var']), 'r') |
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plt.title('Magnetic Heading Innovations') |
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plt.ylabel('Heaing (rad)') |
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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', |
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verticalalignment='bottom') |
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plt.text(innov_0_min_time, innov_0_min, 'min=' + s_innov_0_min, fontsize=12, horizontalalignment='left', |
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verticalalignment='top') |
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# plt.legend(['std='+s_innov_0_std],loc='upper left',frameon=False) |
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pp.savefig() |
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plt.close(5) |
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# air data innovations |
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plt.figure(6, figsize=(20, 13)) |
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# generate airspeed metadata |
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airspeed_innov_max_arg = np.argmax(ekf2_innovations['airspeed_innov']) |
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airspeed_innov_max_time = innov_time[airspeed_innov_max_arg] |
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airspeed_innov_max = np.amax(ekf2_innovations['airspeed_innov']) |
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airspeed_innov_min_arg = np.argmin(ekf2_innovations['airspeed_innov']) |
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airspeed_innov_min_time = innov_time[airspeed_innov_min_arg] |
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airspeed_innov_min = np.amin(ekf2_innovations['airspeed_innov']) |
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s_airspeed_innov_max = str(round(airspeed_innov_max, 3)) |
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s_airspeed_innov_min = str(round(airspeed_innov_min, 3)) |
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# generate sideslip metadata |
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beta_innov_max_arg = np.argmax(ekf2_innovations['beta_innov']) |
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beta_innov_max_time = innov_time[beta_innov_max_arg] |
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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('Synthetic 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']) |
|
|
|
if plot: |
|
# 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 |
|
|
|
# define flags for starting and finishing in air |
|
b_starts_in_air = False |
|
b_finishes_in_air = False |
|
|
|
# calculate in-air transition time |
|
if (np.amin(airborne) < 0.5) and (np.amax(airborne) > 0.5): |
|
in_air_transtion_time_arg = np.argmax(np.diff(airborne)) |
|
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') |
|
b_starts_in_air = True |
|
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') |
|
b_finishes_in_air = True |
|
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] |
|
|
|
if plot: |
|
# 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 |
|
# 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 synthetic sideslip observation has been rejected |
|
# 9 - true if the height above ground observation has been rejected |
|
# 10 - true if the X optical flow observation has been rejected |
|
# 11 - 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 |
|
sli_innov_fail = ((2 ** 8 & estimator_status['innovation_check_flags']) > 0) * 1 |
|
hagl_innov_fail = ((2 ** 9 & estimator_status['innovation_check_flags']) > 0) * 1 |
|
ofx_innov_fail = ((2 ** 10 & estimator_status['innovation_check_flags']) > 0) * 1 |
|
ofy_innov_fail = ((2 ** 11 & estimator_status['innovation_check_flags']) > 0) * 1 |
|
|
|
if plot: |
|
# plot innovation_check_flags summary |
|
plt.figure(11, figsize=(20, 13)) |
|
plt.subplot(6, 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(6, 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(6, 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(6, 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(6, 1, 5) |
|
plt.plot(status_time, sli_innov_fail, 'b', label='sideslip') |
|
plt.ylim(-0.1, 1.1) |
|
plt.ylabel('failed') |
|
plt.legend(loc='upper left') |
|
plt.grid() |
|
plt.subplot(6, 1, 6) |
|
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 : insufficient fix type (no 3D solution) |
|
# 1 : minimum required sat count fail |
|
# 2 : minimum required GDoP fail |
|
# 3 : maximum allowed horizontal position error fail |
|
# 4 : maximum allowed vertical position error fail |
|
# 5 : maximum allowed speed error fail |
|
# 6 : maximum allowed horizontal position drift fail |
|
# 7 : maximum allowed vertical position drift fail |
|
# 8 : maximum allowed horizontal speed fail |
|
# 9 : maximum allowed vertical velocity discrepancy fail |
|
gfix_fail = ((2 ** 0 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
nsat_fail = ((2 ** 1 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
gdop_fail = ((2 ** 2 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
herr_fail = ((2 ** 3 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
verr_fail = ((2 ** 4 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
serr_fail = ((2 ** 5 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
hdrift_fail = ((2 ** 6 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
vdrift_fail = ((2 ** 7 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
hspd_fail = ((2 ** 8 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
veld_diff_fail = ((2 ** 9 & estimator_status['gps_check_fail_flags']) > 0) * 1 |
|
plt.subplot(2, 1, 1) |
|
plt.title('GPS Direct Output Check Failures') |
|
plt.plot(status_time, gfix_fail, 'k', label='fix type') |
|
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]'] / np.maximum(strength, np.finfo(np.float32).eps) ) |
|
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 |
|
# normal index range is defined by the flight duration |
|
start_index = np.amin(np.where(status_time > in_air_transition_time)) |
|
end_index = np.amax(np.where(status_time <= on_ground_transition_time)) |
|
num_valid_values = (end_index - start_index + 1) |
|
# find a late/early index range from 5 sec after in_air_transtion_time to 5 sec before on-ground transition time for mag and optical flow checks to avoid false positives |
|
# this can be used to prevent false positives for sensors adversely affected by close proximity to the ground |
|
# don't do this if the log starts or finishes in air or if it is shut off by flag |
|
late_start_index = np.amin(np.where(status_time > (in_air_transition_time + 5.0)))\ |
|
if (late_start_early_ending and not b_starts_in_air) else start_index |
|
early_end_index = np.amax(np.where(status_time <= (on_ground_transition_time - 5.0))) \ |
|
if (late_start_early_ending and not b_finishes_in_air) else end_index |
|
num_valid_values_trimmed = (early_end_index - late_start_index + 1) |
|
# also find the start and finish indexes for the innovation data |
|
innov_start_index = np.amin(np.where(innov_time > in_air_transition_time)) |
|
innov_end_index = np.amax(np.where(innov_time <= on_ground_transition_time)) |
|
innov_num_valid_values = (innov_end_index - innov_start_index + 1) |
|
innov_late_start_index = np.amin(np.where(innov_time > (in_air_transition_time + 5.0))) \ |
|
if (late_start_early_ending and not b_starts_in_air) else innov_start_index |
|
innov_early_end_index = np.amax(np.where(innov_time <= (on_ground_transition_time - 5.0))) \ |
|
if (late_start_early_ending and not b_finishes_in_air) else innov_end_index |
|
innov_num_valid_values_trimmed = (innov_early_end_index - innov_late_start_index + 1) |
|
# define dictionary of test results and descriptions |
|
test_results = { |
|
'master_status': ['Pass', |
|
'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 or GPS or range finder 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 anomalies 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'], |
|
'imu_vibration_check': ['Pass', |
|
'IMU vibration check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'], |
|
'imu_bias_check': ['Pass', |
|
'IMU bias check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'], |
|
'imu_output_predictor_check': ['Pass', |
|
'IMU output predictor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'], |
|
'flow_sensor_status': ['Pass', |
|
'Optical Flow sensor check summary. A Fail result indicates a significant error that caused a significant reduction in vehicle navigation performance was detected. A Warning result indicates that error levels higher than normal were detected but these errors did not significantly impact navigation performance. A Pass result indicates that no amonalies were detected and no further investigation is required'], |
|
'filter_fault_status': ['Pass', |
|
'Internal Filter 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 + 50)): |
|
# 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 + 1]) |
|
test_results['output_obs_vel_err_median'][0] = np.median( |
|
ekf2_innovations['output_tracking_error[1]'][innov_late_start_index:innov_early_end_index + 1]) |
|
test_results['output_obs_pos_err_median'][0] = np.median( |
|
ekf2_innovations['output_tracking_error[2]'][innov_late_start_index:innov_early_end_index + 1]) |
|
# reduction of status message data |
|
if (early_end_index > (late_start_index + 50)): |
|
# 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 + 1]) |
|
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 + 1]) |
|
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 + 1]) |
|
|
|
# Magnetometer Sensor Checks |
|
if (np.amax(yaw_aligned) > 0.5): |
|
mag_num_red = (estimator_status['mag_test_ratio'][start_index:end_index + 1] > 1.0).sum() |
|
mag_num_amber = (estimator_status['mag_test_ratio'][start_index:end_index + 1] > 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 + 1]) |
|
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 + 1] > 0.5).sum() / num_valid_values_trimmed |
|
test_results['magy_fail_percentage'][0] = 100.0 * ( |
|
magy_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed |
|
test_results['magz_fail_percentage'][0] = 100.0 * ( |
|
magz_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed |
|
test_results['yaw_fail_percentage'][0] = 100.0 * ( |
|
yaw_innov_fail[late_start_index:early_end_index + 1] > 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] > 1.0).sum() |
|
vel_num_amber = (estimator_status['vel_test_ratio'][start_index:end_index + 1] > 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 + 1]) |
|
test_results['vel_test_mean'][0] = np.mean(estimator_status['vel_test_ratio'][start_index:end_index + 1]) |
|
test_results['vel_fail_percentage'][0] = 100.0 * ( |
|
vel_innov_fail[start_index:end_index + 1] > 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] > 1.0).sum() |
|
pos_num_amber = (estimator_status['pos_test_ratio'][start_index:end_index + 1] > 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 + 1]) |
|
test_results['pos_test_mean'][0] = np.mean(estimator_status['pos_test_ratio'][start_index:end_index + 1]) |
|
test_results['pos_fail_percentage'][0] = 100.0 * ( |
|
posh_innov_fail[start_index:end_index + 1] > 0.5).sum() / num_valid_values |
|
|
|
# Height Sensor Checks |
|
hgt_num_red = (estimator_status['hgt_test_ratio'][late_start_index:early_end_index + 1] > 1.0).sum() |
|
hgt_num_amber = (estimator_status['hgt_test_ratio'][late_start_index:early_end_index + 1] > 0.5).sum() - hgt_num_red |
|
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 + 1]) |
|
test_results['hgt_test_mean'][0] = np.mean(estimator_status['hgt_test_ratio'][late_start_index:early_end_index + 1]) |
|
test_results['hgt_fail_percentage'][0] = 100.0 * ( |
|
posv_innov_fail[late_start_index:early_end_index + 1] > 0.5).sum() / num_valid_values_trimmed |
|
|
|
# Airspeed Sensor Checks |
|
if (tas_test_max > 0.0): |
|
tas_num_red = (estimator_status['tas_test_ratio'][start_index:end_index + 1] > 1.0).sum() |
|
tas_num_amber = (estimator_status['tas_test_ratio'][start_index:end_index + 1] > 0.5).sum() - tas_num_red |
|
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 + 1]) |
|
test_results['tas_test_mean'][0] = np.mean(estimator_status['tas_test_ratio'][start_index:end_index + 1]) |
|
test_results['tas_fail_percentage'][0] = 100.0 * ( |
|
tas_innov_fail[start_index:end_index + 1] > 0.5).sum() / num_valid_values |
|
|
|
# HAGL Sensor Checks |
|
if (hagl_test_max > 0.0): |
|
hagl_num_red = (estimator_status['hagl_test_ratio'][start_index:end_index + 1] > 1.0).sum() |
|
hagl_num_amber = (estimator_status['hagl_test_ratio'][start_index:end_index + 1] > 0.5).sum() - hagl_num_red |
|
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 + 1]) |
|
test_results['hagl_test_mean'][0] = np.mean(estimator_status['hagl_test_ratio'][start_index:end_index + 1]) |
|
test_results['hagl_fail_percentage'][0] = 100.0 * ( |
|
hagl_innov_fail[start_index:end_index + 1] > 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 + 1] > 0.5).sum() / num_valid_values_trimmed |
|
test_results['ofy_fail_percentage'][0] = 100.0 * ( |
|
ofy_innov_fail[late_start_index:early_end_index + 1] > 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 |
|
|
|
# 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')): |
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test_results['master_status'][0] = 'Warning' |
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test_results['mag_sensor_status'][0] = 'Warning' |
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if (test_results.get('vel_percentage_amber')[0] > check_levels.get('vel_amber_warn_pct')): |
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test_results['master_status'][0] = 'Warning' |
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test_results['vel_sensor_status'][0] = 'Warning' |
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if (test_results.get('pos_percentage_amber')[0] > check_levels.get('pos_amber_warn_pct')): |
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test_results['master_status'][0] = 'Warning' |
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test_results['pos_sensor_status'][0] = 'Warning' |
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if (test_results.get('hgt_percentage_amber')[0] > check_levels.get('hgt_amber_warn_pct')): |
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test_results['master_status'][0] = 'Warning' |
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test_results['hgt_sensor_status'][0] = 'Warning' |
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if (test_results.get('hagl_percentage_amber')[0] > check_levels.get('hagl_amber_warn_pct')): |
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test_results['master_status'][0] = 'Warning' |
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test_results['hagl_sensor_status'][0] = 'Warning' |
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if (test_results.get('tas_percentage_amber')[0] > check_levels.get('tas_amber_warn_pct')): |
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test_results['master_status'][0] = 'Warning' |
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test_results['tas_sensor_status'][0] = 'Warning' |
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# check for IMU sensor warnings |
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if ((test_results.get('imu_coning_peak')[0] > check_levels.get('imu_coning_peak_warn')) or |
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(test_results.get('imu_coning_mean')[0] > check_levels.get('imu_coning_mean_warn'))): |
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test_results['master_status'][0] = 'Warning' |
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test_results['imu_sensor_status'][0] = 'Warning' |
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test_results['imu_vibration_check'][0] = 'Warning' |
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print('IMU gyro coning check warning.') |
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if ((test_results.get('imu_hfdang_peak')[0] > check_levels.get('imu_hfdang_peak_warn')) or |
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(test_results.get('imu_hfdang_mean')[0] > check_levels.get('imu_hfdang_mean_warn'))): |
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test_results['master_status'][0] = 'Warning' |
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test_results['imu_sensor_status'][0] = 'Warning' |
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test_results['imu_vibration_check'][0] = 'Warning' |
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print('IMU gyro vibration check warning.') |
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if ((test_results.get('imu_hfdvel_peak')[0] > check_levels.get('imu_hfdvel_peak_warn')) or |
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(test_results.get('imu_hfdvel_mean')[0] > check_levels.get('imu_hfdvel_mean_warn'))): |
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test_results['master_status'][0] = 'Warning' |
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test_results['imu_sensor_status'][0] = 'Warning' |
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test_results['imu_vibration_check'][0] = 'Warning' |
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print('IMU accel vibration check warning.') |
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if ((test_results.get('imu_dang_bias_median')[0] > check_levels.get('imu_dang_bias_median_warn')) or |
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(test_results.get('imu_dvel_bias_median')[0] > check_levels.get('imu_dvel_bias_median_warn'))): |
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test_results['master_status'][0] = 'Warning' |
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test_results['imu_sensor_status'][0] = 'Warning' |
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test_results['imu_bias_check'][0] = 'Warning' |
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print('IMU bias check warning.') |
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if ((test_results.get('output_obs_ang_err_median')[0] > check_levels.get('obs_ang_err_median_warn')) or |
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(test_results.get('output_obs_vel_err_median')[0] > check_levels.get('obs_vel_err_median_warn')) or |
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(test_results.get('output_obs_pos_err_median')[0] > check_levels.get('obs_pos_err_median_warn'))): |
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test_results['master_status'][0] = 'Warning' |
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test_results['imu_sensor_status'][0] = 'Warning' |
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test_results['imu_output_predictor_check'][0] = 'Warning' |
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print('IMU output predictor check warning.') |
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# check for failures |
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if ((test_results.get('magx_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or |
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(test_results.get('magy_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or |
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(test_results.get('magz_fail_percentage')[0] > check_levels.get('mag_fail_pct')) or |
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(test_results.get('mag_percentage_amber')[0] > check_levels.get('mag_amber_fail_pct'))): |
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test_results['master_status'][0] = 'Fail' |
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test_results['mag_sensor_status'][0] = 'Fail' |
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print('Magnetometer sensor check failure.') |
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if (test_results.get('yaw_fail_percentage')[0] > check_levels.get('yaw_fail_pct')): |
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test_results['master_status'][0] = 'Fail' |
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test_results['yaw_sensor_status'][0] = 'Fail' |
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print('Yaw sensor check failure.') |
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if ((test_results.get('vel_fail_percentage')[0] > check_levels.get('vel_fail_pct')) or |
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(test_results.get('vel_percentage_amber')[0] > check_levels.get('vel_amber_fail_pct'))): |
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test_results['master_status'][0] = 'Fail' |
|
test_results['vel_sensor_status'][0] = 'Fail' |
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print('Velocity sensor check failure.') |
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if ((test_results.get('pos_fail_percentage')[0] > check_levels.get('pos_fail_pct')) or |
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(test_results.get('pos_percentage_amber')[0] > check_levels.get('pos_amber_fail_pct'))): |
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test_results['master_status'][0] = 'Fail' |
|
test_results['pos_sensor_status'][0] = 'Fail' |
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print('Position sensor check failure.') |
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if ((test_results.get('hgt_fail_percentage')[0] > check_levels.get('hgt_fail_pct')) or |
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(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' |
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print('Height sensor check failure.') |
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if ((test_results.get('tas_fail_percentage')[0] > check_levels.get('tas_fail_pct')) or |
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(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' |
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print('Airspeed sensor check failure.') |
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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' |
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print('Height above ground sensor check failure.') |
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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'))): |
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test_results['master_status'][0] = 'Fail' |
|
test_results['flow_sensor_status'][0] = 'Fail' |
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print('Optical flow sensor check failure.') |
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if (test_results.get('filter_faults_max')[0] > 0): |
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test_results['master_status'][0] = 'Fail' |
|
test_results['filter_fault_status'][0] = 'Fail' |
|
|
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return test_results
|
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