#! /usr/bin/env python3 """ function collection for plotting """ # matplotlib don't use Xwindows backend (must be before pyplot import) import matplotlib matplotlib.use('Agg') import numpy as np from matplotlib.backends.backend_pdf import PdfPages from pyulog import ULog from analysis.post_processing import magnetic_field_estimates_from_states, get_gps_check_fail_flags from plotting.data_plots import TimeSeriesPlot, InnovationPlot, ControlModeSummaryPlot, \ CheckFlagsPlot from analysis.detectors import PreconditionError import analysis.data_version_handler as dvh def create_pdf_report(ulog: ULog, multi_instance: int, output_plot_filename: str) -> None: """ creates a pdf report of the ekf analysis. :param ulog: :param output_plot_filename: :return: """ # create summary plots # save the plots to PDF try: estimator_status = ulog.get_dataset('estimator_status', multi_instance).data except: raise PreconditionError('could not find estimator_status instance', multi_instance) try: estimator_status_flags = ulog.get_dataset('estimator_status_flags', multi_instance).data except: raise PreconditionError('could not find estimator_status_flags instance', multi_instance) try: estimator_states = ulog.get_dataset('estimator_states', multi_instance).data except: raise PreconditionError('could not find estimator_states instance', multi_instance) try: estimator_sensor_bias = ulog.get_dataset('estimator_sensor_bias', multi_instance).data except: raise PreconditionError('could not find estimator_sensor_bias instance', multi_instance) try: estimator_innovations = ulog.get_dataset('estimator_innovations', multi_instance).data estimator_innovation_variances = ulog.get_dataset('estimator_innovation_variances', multi_instance).data innovation_data = estimator_innovations for key in estimator_innovation_variances: # append 'var' to the field name such that we can distingush between innov and innov_var innovation_data.update({str('var_'+key): estimator_innovation_variances[key]}) innovations_min_length = float('inf') for key in estimator_innovations: if len(estimator_innovations[key]) < innovations_min_length: innovations_min_length = len(estimator_innovations[key]) variances_min_length = float('inf') for key in estimator_innovation_variances: if len(estimator_innovation_variances[key]) < variances_min_length: variances_min_length = len(estimator_innovation_variances[key]) # ensure consistent sizing for plots if (innovations_min_length != variances_min_length): print("estimator_innovations and estimator_innovation_variances are different sizes, adjusting") innovation_data_min_length = min(innovations_min_length, variances_min_length) for key in innovation_data: innovation_data[key] = innovation_data[key][0:innovation_data_min_length] print('found innovation data (merged estimator_innovations + estimator_innovation_variances) instance', multi_instance) except: raise PreconditionError('could not find innovation data') gps_fail_flags = get_gps_check_fail_flags(estimator_status) status_time = 1e-6 * estimator_status['timestamp'] status_flags_time = 1e-6 * estimator_status_flags['timestamp'] b_finishes_in_air, b_starts_in_air, in_air_duration, in_air_transition_time, \ on_ground_transition_time = detect_airtime(estimator_status_flags, status_flags_time) with PdfPages(output_plot_filename) as pdf_pages: # vertical velocity and position innovations data_plot = InnovationPlot( innovation_data, [('gps_vpos', 'var_gps_vpos'), ('gps_vvel', 'var_gps_vvel')], x_labels=['time (sec)', 'time (sec)'], y_labels=['Down Vel (m/s)', 'Down Pos (m)'], plot_title='Vertical Innovations', pdf_handle=pdf_pages) data_plot.save() data_plot.close() # horizontal velocity innovations data_plot = InnovationPlot( innovation_data, [('gps_hvel[0]', 'var_gps_hvel[0]'), ('gps_hvel[1]', 'var_gps_hvel[1]')], x_labels=['time (sec)', 'time (sec)'], y_labels=['North Vel (m/s)', 'East Vel (m/s)'], plot_title='Horizontal Velocity Innovations', pdf_handle=pdf_pages) data_plot.save() data_plot.close() # horizontal position innovations data_plot = InnovationPlot( innovation_data, [('gps_hpos[0]', 'var_gps_hpos[0]'), ('gps_hpos[1]', 'var_gps_hpos[1]')], x_labels=['time (sec)', 'time (sec)'], y_labels=['North Pos (m)', 'East Pos (m)'], plot_title='Horizontal Position Innovations', pdf_handle=pdf_pages) data_plot.save() data_plot.close() # magnetometer innovations data_plot = InnovationPlot( innovation_data, [('mag_field[0]', 'var_mag_field[0]'), ('mag_field[1]', 'var_mag_field[1]'), ('mag_field[2]', 'var_mag_field[2]')], x_labels=['time (sec)', 'time (sec)', 'time (sec)'], y_labels=['X (Gauss)', 'Y (Gauss)', 'Z (Gauss)'], plot_title='Magnetometer Innovations', pdf_handle=pdf_pages) data_plot.save() data_plot.close() # magnetic heading innovations data_plot = InnovationPlot( innovation_data, [('heading', 'var_heading')], x_labels=['time (sec)'], y_labels=['Heading (rad)'], plot_title='Magnetic Heading Innovations', pdf_handle=pdf_pages) data_plot.save() data_plot.close() # air data innovations data_plot = InnovationPlot( innovation_data, [('airspeed', 'var_airspeed'), ('beta', 'var_beta')], x_labels=['time (sec)', 'time (sec)'], y_labels=['innovation (m/sec)', 'innovation (rad)'], sub_titles=['True Airspeed Innovations', 'Synthetic Sideslip Innovations'], pdf_handle=pdf_pages) data_plot.save() data_plot.close() # optical flow innovations data_plot = InnovationPlot( innovation_data, [('flow[0]', 'var_flow[0]'), ('flow[1]', 'var_flow[1]')], x_labels=['time (sec)', 'time (sec)'], y_labels=['X (rad/sec)', 'Y (rad/sec)'], plot_title='Optical Flow Innovations', pdf_handle=pdf_pages) data_plot.save() data_plot.close() # plot normalised innovation test levels # define variables to plot variables = [['mag_test_ratio'], ['vel_test_ratio', 'pos_test_ratio'], ['hgt_test_ratio']] y_labels = ['mag', 'vel, pos', 'hgt'] legend = [['mag'], ['vel', 'pos'], ['hgt']] if np.amax(estimator_status['hagl_test_ratio']) > 0.0: # plot hagl test ratio, if applicable variables[-1].append('hagl_test_ratio') y_labels[-1] += ', hagl' legend[-1].append('hagl') if np.amax(estimator_status[ 'tas_test_ratio']) > 0.0: # plot airspeed sensor test ratio, if applicable variables.append(['tas_test_ratio']) y_labels.append('TAS') legend.append(['airspeed']) data_plot = CheckFlagsPlot( status_time, estimator_status, variables, x_label='time (sec)', y_labels=y_labels, plot_title='Normalised Innovation Test Levels', pdf_handle=pdf_pages, annotate=True, legend=legend ) data_plot.save() data_plot.close() # plot control mode summary A data_plot = ControlModeSummaryPlot( status_flags_time, estimator_status_flags, [['cs_tilt_align', 'cs_yaw_align'], ['cs_gps', 'cs_opt_flow', 'cs_ev_pos'], ['cs_baro_hgt', 'cs_gps_hgt', 'cs_rng_hgt', 'cs_ev_hgt'], ['cs_mag_hdg', 'cs_mag_3d', 'cs_mag_dec']], x_label='time (sec)', y_labels=['aligned', 'pos aiding', 'hgt aiding', 'mag aiding'], annotation_text=[['tilt alignment', 'yaw alignment'], ['GPS aiding', 'optical flow aiding', 'external vision aiding'], ['Baro aiding', 'GPS aiding', 'rangefinder aiding', 'external vision aiding'], ['magnetic yaw aiding', '3D magnetoemter aiding', 'magnetic declination aiding']], plot_title='EKF Control Status - Figure A', pdf_handle=pdf_pages) data_plot.save() data_plot.close() # plot control mode summary B # construct additional annotations for the airborne plot airborne_annotations = list() if np.amin(np.diff(estimator_status_flags['cs_in_air'])) > -0.5: airborne_annotations.append( (on_ground_transition_time, 'air to ground transition not detected')) else: airborne_annotations.append((on_ground_transition_time, 'on-ground at {:.1f} sec'.format( on_ground_transition_time))) if in_air_duration > 0.0: airborne_annotations.append(((in_air_transition_time + on_ground_transition_time) / 2, 'duration = {:.1f} sec'.format(in_air_duration))) if np.amax(np.diff(estimator_status_flags['cs_in_air'])) < 0.5: airborne_annotations.append( (in_air_transition_time, 'ground to air transition not detected')) else: airborne_annotations.append( (in_air_transition_time, 'in-air at {:.1f} sec'.format(in_air_transition_time))) data_plot = ControlModeSummaryPlot( status_flags_time, estimator_status_flags, [['cs_in_air'], ['cs_wind']], x_label='time (sec)', y_labels=['airborne', 'estimating wind'], annotation_text=[[], []], additional_annotation=[airborne_annotations, []], plot_title='EKF Control Status - Figure B', pdf_handle=pdf_pages) data_plot.save() data_plot.close() # plot innovation_check_flags summary data_plot = CheckFlagsPlot( status_flags_time, estimator_status_flags, [['reject_hor_vel', 'reject_hor_pos'], ['reject_ver_vel', 'reject_ver_pos', 'reject_hagl'], ['fs_bad_mag_x', 'fs_bad_mag_y', 'fs_bad_mag_z', 'reject_yaw'], ['reject_airspeed'], ['reject_sideslip'], ['reject_optflow_x', 'reject_optflow_y']], x_label='time (sec)', y_labels=['failed', 'failed', 'failed', 'failed', 'failed', 'failed'], y_lim=(-0.1, 1.1), legend=[['vel NE', 'pos NE'], ['vel D', 'hgt absolute', 'hgt above ground'], ['mag_x', 'mag_y', 'mag_z', 'yaw'], ['airspeed'], ['sideslip'], ['flow X', 'flow Y']], plot_title='EKF Innovation Test Fails', annotate=False, pdf_handle=pdf_pages) data_plot.save() data_plot.close() # gps_check_fail_flags summary data_plot = CheckFlagsPlot( status_time, gps_fail_flags, [['nsat_fail', 'pdop_fail', 'herr_fail', 'verr_fail', 'gfix_fail', 'serr_fail'], ['hdrift_fail', 'vdrift_fail', 'hspd_fail', 'veld_diff_fail']], x_label='time (sec)', y_lim=(-0.1, 1.1), y_labels=['failed', 'failed'], sub_titles=['GPS Direct Output Check Failures', 'GPS Derived Output Check Failures'], legend=[['N sats', 'PDOP', 'horiz pos error', 'vert pos error', 'fix type', 'speed error'], ['horiz drift', 'vert drift', 'horiz speed', 'vert vel inconsistent']], annotate=False, pdf_handle=pdf_pages) data_plot.save() data_plot.close() # filter reported accuracy data_plot = CheckFlagsPlot( status_time, estimator_status, [['pos_horiz_accuracy', 'pos_vert_accuracy']], x_label='time (sec)', y_labels=['accuracy (m)'], plot_title='Reported Accuracy', legend=[['horizontal', 'vertical']], annotate=False, pdf_handle=pdf_pages) data_plot.save() data_plot.close() # Plot the EKF IMU vibration metrics for imu_status_instance in range(4): try: vehicle_imu_status_data = ulog.get_dataset('vehicle_imu_status', imu_status_instance).data imu_status_time = 1e-6 * vehicle_imu_status_data['timestamp'] if vehicle_imu_status_data['accel_device_id'][0] == estimator_status['accel_device_id'][0]: scaled_estimator_status = {'delta_angle_coning_metric': 1000. * vehicle_imu_status_data['delta_angle_coning_metric'], 'gyro_vibration_metric': vehicle_imu_status_data['gyro_vibration_metric'], 'accel_vibration_metric': vehicle_imu_status_data['accel_vibration_metric'] } data_plot = CheckFlagsPlot( imu_status_time, scaled_estimator_status, [['delta_angle_coning_metric'], ['gyro_vibration_metric'], ['accel_vibration_metric']], x_label='time (sec)', y_labels=['Del Ang Coning (mrad^2)', 'HF Gyro (rad/s)', 'HF accel (m/s/s)'], plot_title='IMU Vibration Metrics', pdf_handle=pdf_pages, annotate=True) data_plot.save() data_plot.close() except: pass # Plot the EKF output observer tracking errors scaled_innovations = { 'output_tracking_error[0]': 1000. * estimator_status['output_tracking_error[0]'], 'output_tracking_error[1]': estimator_status['output_tracking_error[1]'], 'output_tracking_error[2]': estimator_status['output_tracking_error[2]'] } data_plot = CheckFlagsPlot( 1e-6 * estimator_status['timestamp'], scaled_innovations, [['output_tracking_error[0]'], ['output_tracking_error[1]'], ['output_tracking_error[2]']], x_label='time (sec)', y_labels=['angles (mrad)', 'velocity (m/s)', 'position (m)'], plot_title='Output Observer Tracking Error Magnitudes', pdf_handle=pdf_pages, annotate=True) data_plot.save() data_plot.close() # Plot the gyro bias estimates data_plot = CheckFlagsPlot( 1e-6 * estimator_sensor_bias['timestamp'], estimator_sensor_bias, [['gyro_bias[0]'], ['gyro_bias[1]'], ['gyro_bias[2]']], x_label='time (sec)', y_labels=['X (rad/s)', 'Y (rad/s)', 'Z (rad/s)'], plot_title='Gyro Bias Estimates', annotate=False, pdf_handle=pdf_pages) data_plot.save() data_plot.close() # Plot the accel bias estimates data_plot = CheckFlagsPlot( 1e-6 * estimator_sensor_bias['timestamp'], estimator_sensor_bias, [['accel_bias[0]'], ['accel_bias[1]'], ['accel_bias[2]']], x_label='time (sec)', y_labels=['X (m/s^2)', 'Y (m/s^2)', 'Z (m/s^2)'], plot_title='Accel Bias Estimates', annotate=False, pdf_handle=pdf_pages) data_plot.save() data_plot.close() # Plot the earth frame magnetic field estimates declination, field_strength, inclination = magnetic_field_estimates_from_states( estimator_states) data_plot = CheckFlagsPlot( 1e-6 * estimator_states['timestamp'], {'strength': field_strength, 'declination': declination, 'inclination': inclination}, [['declination'], ['inclination'], ['strength']], x_label='time (sec)', y_labels=['declination (deg)', 'inclination (deg)', 'strength (Gauss)'], plot_title='Earth Magnetic Field Estimates', annotate=False, pdf_handle=pdf_pages) data_plot.save() data_plot.close() # Plot the body frame magnetic field estimates data_plot = CheckFlagsPlot( 1e-6 * estimator_states['timestamp'], estimator_states, [['states[19]'], ['states[20]'], ['states[21]']], x_label='time (sec)', y_labels=['X (Gauss)', 'Y (Gauss)', 'Z (Gauss)'], plot_title='Magnetometer Bias Estimates', annotate=False, pdf_handle=pdf_pages) data_plot.save() data_plot.close() # Plot the EKF wind estimates data_plot = CheckFlagsPlot( 1e-6 * estimator_states['timestamp'], estimator_states, [['states[22]'], ['states[23]']], x_label='time (sec)', y_labels=['North (m/s)', 'East (m/s)'], plot_title='Wind Velocity Estimates', annotate=False, pdf_handle=pdf_pages) data_plot.save() data_plot.close() def detect_airtime(estimator_status_flags, status_flags_time): # 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(estimator_status_flags['cs_in_air']) < 0.5) and (np.amax(estimator_status_flags['cs_in_air']) > 0.5): in_air_transtion_time_arg = np.argmax(np.diff(estimator_status_flags['cs_in_air'])) in_air_transition_time = status_flags_time[in_air_transtion_time_arg] elif (np.amax(estimator_status_flags['cs_in_air']) > 0.5): in_air_transition_time = np.amin(status_flags_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(estimator_status_flags['cs_in_air'])) < 0.0): on_ground_transition_time_arg = np.argmin(np.diff(estimator_status_flags['cs_in_air'])) on_ground_transition_time = status_flags_time[on_ground_transition_time_arg] elif (np.amax(estimator_status_flags['cs_in_air']) > 0.5): on_ground_transition_time = np.amax(status_flags_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(estimator_status_flags['cs_in_air'])) > 0.5) and (np.amin(np.diff(estimator_status_flags['cs_in_air'])) < -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') return b_finishes_in_air, b_starts_in_air, in_air_duration, in_air_transition_time, on_ground_transition_time