def generate_plots(ulog, px4_ulog, db_data, vehicle_data):
""" create a list of bokeh plots (and widgets) to show """
plots = []
data = ulog.data_list
# initialize flight mode changes
try:
cur_dataset = ulog.get_dataset('vehicle_status')
flight_mode_changes = cur_dataset.list_value_changes('nav_state')
flight_mode_changes.append((ulog.last_timestamp, -1))
except (KeyError, IndexError) as error:
flight_mode_changes = []
# VTOL state changes
vtol_states = None
try:
cur_dataset = ulog.get_dataset('vehicle_status')
if np.amax(cur_dataset.data['is_vtol']) == 1:
vtol_states = cur_dataset.list_value_changes('in_transition_mode')
# find mode after transitions (states: 1=transition, 2=FW, 3=MC)
for i in range(len(vtol_states)):
if vtol_states[i][1] == 0:
t = vtol_states[i][0]
idx = np.argmax(cur_dataset.data['timestamp'] >= t) + 1
vtol_states[i] = (t, 2 + cur_dataset.data['is_rotary_wing'][idx])
vtol_states.append((ulog.last_timestamp, -1))
except (KeyError, IndexError) as error:
vtol_states = None
# heading & all the text info on top (logging duration, max speed, ...)
plots.append(get_heading_and_info(ulog, px4_ulog, plot_width,
db_data, vehicle_data, vtol_states))
# hardfault
if 'hardfault_plain' in ulog.msg_info_multiple_dict:
hardfault_html = (
'<p><b> <font color="#e0212d">This log contains hardfault data from a software crash'
'</font></b> (see <a '
'href="https://dev.px4.io/en/debug/gdb_debugging.html#debugging-hard-faults-in-nuttx">'
'here</a> how to debug):</p>')
counter = 1
for hardfault in ulog.msg_info_multiple_dict['hardfault_plain']:
hardfault_text = cgi.escape(''.join(hardfault)).replace('\n', '<br/>')
hardfault_html += ('<p>Hardfault #'+str(counter)+':<br/><pre>'+
hardfault_text+'</pre></p>')
counter += 1
hardfault_div = Div(text=hardfault_html, width=int(plot_width*0.9))
plots.append(widgetbox(hardfault_div, width=int(plot_width*0.9)))
# FIXME: for now, we use Google maps directly without bokeh, because it's not working reliably
# GPS map
# gps_plots = []
# gps_titles = []
# plot = plot_map(ulog, plot_config, map_type='google', api_key =
# get_google_maps_api_key(), setpoints=False)
# if plot is not None:
# gps_plots.append(plot)
# gps_titles.append('GPS Map: Satellite')
#
# plot = plot_map(ulog, plot_config, map_type='plain', setpoints=True)
# if plot is not None:
# gps_plots.append(plot)
# gps_titles.append('GPS Map: Plain')
#
# data_plot = DataPlot2D(data, plot_config, 'vehicle_local_position',
# x_axis_label = '[m]', y_axis_label='[m]', plot_height='large')
# data_plot.add_graph('y', 'x', colors2[0], 'Estimated')
# data_plot.change_dataset('vehicle_local_position_setpoint')
# data_plot.add_graph('y', 'x', colors2[1], 'Setpoint')
# if data_plot.finalize() is not None:
# gps_plots.append(data_plot.bokeh_plot)
# gps_titles.append('Local Position')
#
# if len(gps_plots) >= 2:
# tabs = []
# for i in range(len(gps_plots)):
# tabs.append(Panel(child=gps_plots[i], title=gps_titles[i]))
# gps_plot_height=plot_config['plot_height']['large'] + 30
# plots.append(Tabs(tabs=tabs, width=plot_width, height=gps_plot_height))
# elif len(gps_plots) == 1:
# plots.extend(gps_plots)
if is_running_locally():
# show the google maps plot via Bokeh, since the one in the html
# template does not work locally (we disable it further down)
map_plot = plot_map(ulog, plot_config, map_type='google', api_key=
get_google_maps_api_key(), setpoints=False)
if map_plot is not None:
plots.append(map_plot)
# Position plot
data_plot = DataPlot2D(data, plot_config, 'vehicle_local_position',
x_axis_label='[m]', y_axis_label='[m]', plot_height='large')
data_plot.add_graph('y', 'x', colors2[0], 'Estimated',
check_if_all_zero=True)
if not data_plot.had_error: # vehicle_local_position is required
data_plot.change_dataset('vehicle_local_position_setpoint')
data_plot.add_graph('y', 'x', colors2[1], 'Setpoint')
# groundtruth (SITL only)
data_plot.change_dataset('vehicle_local_position_groundtruth')
data_plot.add_graph('y', 'x', color_gray, 'Groundtruth')
# GPS + position setpoints
plot_map(ulog, plot_config, map_type='plain', setpoints=True,
bokeh_plot=data_plot.bokeh_plot)
if data_plot.finalize() is not None:
plots.append(data_plot.bokeh_plot)
if not is_running_locally(): # do not enable Google Map if running locally
curdoc().template_variables['has_position_data'] = True
# initialize parameter changes
changed_params = None
if not 'replay' in ulog.msg_info_dict: # replay can have many param changes
if len(ulog.changed_parameters) > 0:
changed_params = ulog.changed_parameters
plots.append(None) # save space for the param change button
### Add all data plots ###
x_range_offset = (ulog.last_timestamp - ulog.start_timestamp) * 0.05
x_range = Range1d(ulog.start_timestamp - x_range_offset, ulog.last_timestamp + x_range_offset)
# Altitude estimate
data_plot = DataPlot(data, plot_config, 'vehicle_gps_position',
y_axis_label='[m]', title='Altitude Estimate',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph([lambda data: ('alt', data['alt']*0.001)],
colors8[0:1], ['GPS Altitude'])
data_plot.change_dataset('sensor_combined')
data_plot.add_graph(['baro_alt_meter'], colors8[1:2], ['Barometer Altitude'])
data_plot.change_dataset('vehicle_global_position')
data_plot.add_graph(['alt'], colors8[2:3], ['Fused Altitude Estimation'])
data_plot.change_dataset('position_setpoint_triplet')
data_plot.add_circle(['current.alt'], [plot_config['mission_setpoint_color']],
['Altitude Setpoint'])
data_plot.change_dataset('actuator_controls_0')
data_plot.add_graph([lambda data: ('thrust', data['control[3]']*100)],
colors8[6:7], ['Thrust [0, 100]'])
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# Roll/Pitch/Yaw angle & angular rate
for axis in ['roll', 'pitch', 'yaw']:
# angle
axis_name = axis.capitalize()
data_plot = DataPlot(data, plot_config, 'vehicle_attitude',
y_axis_label='[deg]', title=axis_name+' Angle',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([lambda data: (axis, np.rad2deg(data[axis]))],
colors2[0:1], [axis_name+' Estimated'], mark_nan=True)
data_plot.change_dataset('vehicle_attitude_setpoint')
data_plot.add_graph([lambda data: (axis+'_d', np.rad2deg(data[axis+'_d']))],
colors2[1:2], [axis_name+' Setpoint'], mark_nan=True)
data_plot.change_dataset('vehicle_attitude_groundtruth')
data_plot.add_graph([lambda data: (axis, np.rad2deg(data[axis]))],
[color_gray], [axis_name+' Groundtruth'])
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# rate
data_plot = DataPlot(data, plot_config, 'vehicle_attitude',
y_axis_label='[deg/s]', title=axis_name+' Angular Rate',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([lambda data: (axis+'speed', np.rad2deg(data[axis+'speed']))],
colors2[0:1], [axis_name+' Rate Estimated'], mark_nan=True)
data_plot.change_dataset('vehicle_rates_setpoint')
data_plot.add_graph([lambda data: (axis, np.rad2deg(data[axis]))],
colors2[1:2], [axis_name+' Rate Setpoint'], mark_nan=True)
data_plot.change_dataset('vehicle_attitude_groundtruth')
data_plot.add_graph([lambda data: (axis+'speed', np.rad2deg(data[axis+'speed']))],
[color_gray], [axis_name+' Rate Groundtruth'])
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# Local position
for axis in ['x', 'y', 'z']:
data_plot = DataPlot(data, plot_config, 'vehicle_local_position',
y_axis_label='[m]', title='Local Position '+axis.upper(),
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([axis], colors2[0:1], [axis.upper()+' Estimated'], mark_nan=True)
data_plot.change_dataset('vehicle_local_position_setpoint')
data_plot.add_graph([axis], colors2[1:2], [axis.upper()+' Setpoint'], mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# Velocity
data_plot = DataPlot(data, plot_config, 'vehicle_local_position',
y_axis_label='[m/s]', title='Velocity',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['vx', 'vy', 'vz'], colors3, ['X', 'Y', 'Z'])
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# Vision position (only if topic found)
if any(elem.name == 'vehicle_vision_position' for elem in data):
data_plot = DataPlot(data, plot_config, 'vehicle_vision_position',
y_axis_label='[m]', title='Vision Position',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['x', 'y', 'z'], colors3, ['X', 'Y', 'Z'], mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
data_plot.change_dataset('vehicle_local_position_groundtruth')
data_plot.add_graph(['x', 'y', 'z'], colors8[2:5],
['Groundtruth X', 'Groundtruth Y', 'Groundtruth Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# Vision velocity
data_plot = DataPlot(data, plot_config, 'vehicle_vision_position',
y_axis_label='[m]', title='Vision Velocity',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['vx', 'vy', 'vz'], colors3, ['X', 'Y', 'Z'], mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
data_plot.change_dataset('vehicle_local_position_groundtruth')
data_plot.add_graph(['vx', 'vy', 'vz'], colors8[2:5],
['Groundtruth X', 'Groundtruth Y', 'Groundtruth Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# Vision attitude
if any(elem.name == 'vehicle_vision_attitude' for elem in data):
data_plot = DataPlot(data, plot_config, 'vehicle_vision_attitude',
y_axis_label='[deg]', title='Vision Attitude',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([lambda data: ('roll', np.rad2deg(data['roll'])),
lambda data: ('pitch', np.rad2deg(data['pitch'])),
lambda data: ('yaw', np.rad2deg(data['yaw']))],
colors3, ['Roll', 'Pitch', 'Yaw'], mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
data_plot.change_dataset('vehicle_attitude_groundtruth')
data_plot.add_graph([lambda data: ('roll', np.rad2deg(data['roll'])),
lambda data: ('pitch', np.rad2deg(data['pitch'])),
lambda data: ('yaw', np.rad2deg(data['yaw']))],
colors8[2:5],
['Roll Groundtruth', 'Pitch Groundtruth', 'Yaw Groundtruth'])
if data_plot.finalize() is not None: plots.append(data_plot)
# Airspeed vs Ground speed: but only if there's valid airspeed data
try:
cur_dataset = ulog.get_dataset('airspeed')
if np.amax(cur_dataset.data['indicated_airspeed_m_s']) > 0.1:
data_plot = DataPlot(data, plot_config, 'vehicle_global_position',
y_axis_label='[m/s]', title='Airspeed',
plot_height='small',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph([lambda data: ('groundspeed_estimated',
np.sqrt(data['vel_n']**2 + data['vel_e']**2))],
colors3[2:3], ['Ground Speed Estimated'])
data_plot.change_dataset('airspeed')
data_plot.add_graph(['indicated_airspeed_m_s'], colors2[0:1], ['Airspeed Indicated'])
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
except (KeyError, IndexError) as error:
pass
# manual control inputs
# prefer the manual_control_setpoint topic. Old logs do not contain it
if any(elem.name == 'manual_control_setpoint' for elem in data):
data_plot = DataPlot(data, plot_config, 'manual_control_setpoint',
title='Manual Control Inputs (Radio or Joystick)',
plot_height='small', y_range=Range1d(-1.1, 1.1),
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(['y', 'x', 'r', 'z',
lambda data: ('mode_slot', data['mode_slot']/6),
'aux1', 'aux2',
lambda data: ('kill_switch', data['kill_switch'] == 1)],
colors8,
['Y / Roll', 'X / Pitch', 'Yaw', 'Throttle [0, 1]',
'Flight Mode', 'Aux1', 'Aux2', 'Kill Switch'])
# TODO: add RTL switch and others? Look at params which functions are mapped?
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
else: # it's an old log
data_plot = DataPlot(data, plot_config, 'rc_channels',
title='Raw Radio Control Inputs',
plot_height='small', y_range=Range1d(-1.1, 1.1),
changed_params=changed_params, x_range=x_range)
num_rc_channels = 8
if data_plot.dataset:
max_channels = np.amax(data_plot.dataset.data['channel_count'])
if max_channels < num_rc_channels: num_rc_channels = max_channels
legends = []
for i in range(num_rc_channels):
channel_names = px4_ulog.get_configured_rc_input_names(i)
if channel_names is None:
legends.append('Channel '+str(i))
else:
legends.append('Channel '+str(i)+' ('+', '.join(channel_names)+')')
data_plot.add_graph(['channels['+str(i)+']' for i in range(num_rc_channels)],
colors8[0:num_rc_channels], legends, mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator controls 0
data_plot = DataPlot(data, plot_config, 'actuator_controls_0',
y_start=0, title='Actuator Controls 0', plot_height='small',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(['control[0]', 'control[1]', 'control[2]', 'control[3]'],
colors8[0:4], ['Roll', 'Pitch', 'Yaw', 'Thrust'], mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator controls 1
# (only present on VTOL, Fixed-wing config)
data_plot = DataPlot(data, plot_config, 'actuator_controls_1',
y_start=0, title='Actuator Controls 1 (VTOL in Fixed-Wing mode)',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['control[0]', 'control[1]', 'control[2]', 'control[3]'],
colors8[0:4], ['Roll', 'Pitch', 'Yaw', 'Thrust'], mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator outputs 0: Main
data_plot = DataPlot(data, plot_config, 'actuator_outputs',
y_start=0, title='Actuator Outputs (Main)', plot_height='small',
changed_params=changed_params, x_range=x_range)
num_actuator_outputs = 8
if data_plot.dataset:
max_outputs = np.amax(data_plot.dataset.data['noutputs'])
if max_outputs < num_actuator_outputs: num_actuator_outputs = max_outputs
data_plot.add_graph(['output['+str(i)+']' for i in
range(num_actuator_outputs)], colors8[0:num_actuator_outputs],
['Output '+str(i) for i in range(num_actuator_outputs)], mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator outputs 1: AUX
data_plot = DataPlot(data, plot_config, 'actuator_outputs',
y_start=0, title='Actuator Outputs (AUX)', plot_height='small',
changed_params=changed_params, topic_instance=1,
x_range=x_range)
num_actuator_outputs = 8
# only plot if at least one of the outputs is not constant
all_constant = True
if data_plot.dataset:
max_outputs = np.amax(data_plot.dataset.data['noutputs'])
if max_outputs < num_actuator_outputs: num_actuator_outputs = max_outputs
for i in range(num_actuator_outputs):
output_data = data_plot.dataset.data['output['+str(i)+']']
if not np.all(output_data == output_data[0]):
all_constant = False
if not all_constant:
data_plot.add_graph(['output['+str(i)+']' for i in
range(num_actuator_outputs)], colors8[0:num_actuator_outputs],
['Output '+str(i) for i in range(num_actuator_outputs)], mark_nan=True)
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# raw acceleration
data_plot = DataPlot(data, plot_config, 'sensor_combined',
y_axis_label='[m/s^2]', title='Raw Acceleration',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['accelerometer_m_s2[0]', 'accelerometer_m_s2[1]',
'accelerometer_m_s2[2]'], colors3, ['X', 'Y', 'Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# raw angular speed
data_plot = DataPlot(data, plot_config, 'sensor_combined',
y_axis_label='[deg/s]', title='Raw Angular Speed (Gyroscope)',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([
lambda data: ('gyro_rad[0]', np.rad2deg(data['gyro_rad[0]'])),
lambda data: ('gyro_rad[1]', np.rad2deg(data['gyro_rad[1]'])),
lambda data: ('gyro_rad[2]', np.rad2deg(data['gyro_rad[2]']))],
colors3, ['X', 'Y', 'Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# magnetic field strength
data_plot = DataPlot(data, plot_config, 'sensor_combined',
y_axis_label='[gauss]', title='Raw Magnetic Field Strength',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['magnetometer_ga[0]', 'magnetometer_ga[1]',
'magnetometer_ga[2]'], colors3,
['X', 'Y', 'Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# distance sensor
data_plot = DataPlot(data, plot_config, 'distance_sensor',
y_start=0, y_axis_label='[m]', title='Distance Sensor',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['current_distance', 'covariance'], colors3[0:2],
['Distance', 'Covariance'])
if data_plot.finalize() is not None: plots.append(data_plot)
# gps uncertainty
# the accuracy values can be really large if there is no fix, so we limit the
# y axis range to some sane values
data_plot = DataPlot(data, plot_config, 'vehicle_gps_position',
title='GPS Uncertainty', y_range=Range1d(0, 40),
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['eph', 'epv', 'satellites_used', 'fix_type'], colors8[::2],
['Horizontal position accuracy [m]', 'Vertical position accuracy [m]',
'Num Satellites used', 'GPS Fix'])
if data_plot.finalize() is not None: plots.append(data_plot)
# gps noise & jamming
data_plot = DataPlot(data, plot_config, 'vehicle_gps_position',
y_start=0, title='GPS Noise & Jamming',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['noise_per_ms', 'jamming_indicator'], colors3[0:2],
['Noise per ms', 'Jamming Indicator'])
if data_plot.finalize() is not None: plots.append(data_plot)
# thrust and magnetic field
data_plot = DataPlot(data, plot_config, 'sensor_combined',
y_start=0, title='Thrust and Magnetic Field', plot_height='small',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(
[lambda data: ('len_mag', np.sqrt(data['magnetometer_ga[0]']**2 +
data['magnetometer_ga[1]']**2 +
data['magnetometer_ga[2]']**2))],
colors2[0:1], ['Norm of Magnetic Field'])
data_plot.change_dataset('actuator_controls_0')
data_plot.add_graph([lambda data: ('thrust', data['control[3]'])],
colors2[1:2], ['Thrust'])
if data_plot.finalize() is not None: plots.append(data_plot)
# Acceleration Spectrogram
data_plot = DataPlotSpec(data, plot_config, 'sensor_combined',
y_axis_label='[Hz]', title='Acceleration Power Spectral Density',
plot_height='small', x_range=x_range)
data_plot.add_graph(['accelerometer_m_s2[0]', 'accelerometer_m_s2[1]', 'accelerometer_m_s2[2]'],
['X', 'Y', 'Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# power
data_plot = DataPlot(data, plot_config, 'battery_status',
y_start=0, title='Power',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['voltage_v', 'voltage_filtered_v',
'current_a', lambda data: ('discharged_mah', data['discharged_mah']/100),
lambda data: ('remaining', data['remaining']*10)],
colors8[::2]+colors8[1:2],
['Voltage [V]', 'Voltage filtered [V]', 'Current [A]',
'Discharged Amount [mAh / 100]', 'Battery remaining [0=empty, 10=full]'])
if data_plot.finalize() is not None: plots.append(data_plot)
# estimator watchdog
data_plot = DataPlot(data, plot_config, 'estimator_status',
y_start=0, title='Estimator Watchdog',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(
['nan_flags', 'health_flags', 'timeout_flags',
lambda data: ('innovation_check_flags_vel_pos', data['innovation_check_flags']&0x7),
lambda data: ('innovation_check_flags_mag', (data['innovation_check_flags']>>3)&0x7),
lambda data: ('innovation_check_flags_yaw', (data['innovation_check_flags']>>6)&0x3),
lambda data: ('innovation_check_flags_sideslip', (data['innovation_check_flags']>>8)&0x3),
lambda data: ('innovation_check_flags_flow', (data['innovation_check_flags']>>10)&0x3)],
colors8,
['NaN Flags', 'Health Flags (vel, pos, hgt)',
'Timeout Flags (vel, pos, hgt)',
'Innovation Check Bits (vel, hor pos, vert pos)',
'Innovation Check Bits (mag X, Y, Z)',
'Innovation Check Bits (yaw, airspeed)',
'Innovation Check Bits (synthetic sideslip, height to ground)',
'Innovation Check Bits (optical flow X, Y)'])
if data_plot.finalize() is not None: plots.append(data_plot)
# RC Quality
data_plot = DataPlot(data, plot_config, 'input_rc',
title='RC Quality', plot_height='small', y_range=Range1d(0, 1),
changed_params=changed_params, x_range=x_range)
data_plot.add_graph([lambda data: ('rssi', data['rssi']/100), 'rc_lost'],
colors3[0:2], ['RSSI [0, 1]', 'RC Lost (Indicator)'])
data_plot.change_dataset('vehicle_status')
data_plot.add_graph(['rc_signal_lost'], colors3[2:3], ['RC Lost (Detected)'])
if data_plot.finalize() is not None: plots.append(data_plot)
# cpu load
data_plot = DataPlot(data, plot_config, 'cpuload',
title='CPU & RAM', plot_height='small', y_range=Range1d(0, 1),
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(['ram_usage', 'load'], [colors3[1], colors3[2]],
['RAM Usage', 'CPU Load'])
data_plot.add_span('load', line_color=colors3[2])
data_plot.add_span('ram_usage', line_color=colors3[1])
plot_flight_modes_background(data_plot.bokeh_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# sampling: time difference
try:
data_plot = DataPlot(data, plot_config, 'sensor_combined', y_range=Range1d(0, 25e3),
y_axis_label='[us]',
title='Sampling Regularity of Sensor Data', plot_height='small',
changed_params=changed_params, x_range=x_range)
sensor_combined = ulog.get_dataset('sensor_combined').data
sampling_diff = np.diff(sensor_combined['timestamp'])
min_sampling_diff = np.amin(sampling_diff)
plot_dropouts(data_plot.bokeh_plot, ulog.dropouts, min_sampling_diff)
data_plot.add_graph([lambda data: ('timediff', np.append(sampling_diff, 0))],
[colors3[2]], ['delta t (between 2 logged samples)'])
data_plot.change_dataset('estimator_status')
data_plot.add_graph([lambda data: ('time_slip', data['time_slip']*1e6)],
[colors3[1]], ['Estimator time slip (cumulative)'])
if data_plot.finalize() is not None: plots.append(data_plot)
except:
pass
# exchange all DataPlot's with the bokeh_plot and handle parameter changes
param_changes_button = Button(label="Hide Parameter Changes", width=170)
param_change_labels = []
# FIXME: this should be a CustomJS callback, not on the server. However this
# did not work for me.
def param_changes_button_clicked():
""" callback to show/hide parameter changes """
for label in param_change_labels:
if label.visible:
param_changes_button.label = 'Show Parameter Changes'
label.visible = False
label.text_alpha = 0 # label.visible does not work, so we use this instead
else:
param_changes_button.label = 'Hide Parameter Changes'
label.visible = True
label.text_alpha = 1
param_changes_button.on_click(param_changes_button_clicked)
jinja_plot_data = []
for i in range(len(plots)):
if plots[i] is None:
plots[i] = widgetbox(param_changes_button, width=int(plot_width * 0.99))
if isinstance(plots[i], DataPlot):
if plots[i].param_change_label is not None:
param_change_labels.append(plots[i].param_change_label)
plot_title = plots[i].title
plots[i] = plots[i].bokeh_plot
fragment = 'Nav-'+plot_title.replace(' ', '-') \
.replace('&', '_').replace('(', '').replace(')', '')
jinja_plot_data.append({
'model_id': plots[i].ref['id'],
'fragment': fragment,
'title': plot_title
})
# changed parameters
plots.append(get_changed_parameters(ulog.initial_parameters, plot_width))
# information about which messages are contained in the log
# TODO: need to load all topics for this (-> log loading will take longer)
# but if we load all topics and the log contains some (external) topics
# with buggy timestamps, it will affect the plotting.
# data_list_sorted = sorted(ulog.data_list, key=lambda d: d.name + str(d.multi_id))
# table_text = []
# for d in data_list_sorted:
# message_size = sum([ULog.get_field_size(f.type_str) for f in d.field_data])
# num_data_points = len(d.data['timestamp'])
# table_text.append((d.name, str(d.multi_id), str(message_size), str(num_data_points),
# str(message_size * num_data_points)))
# topics_info = '<table><tr><th>Name</th><th>Topic instance</th><th>Message Size</th>' \
# '<th>Number of data points</th><th>Total bytes</th></tr>' + ''.join(
# ['<tr><td>'+'</td><td>'.join(list(x))+'</td></tr>' for x in table_text]) + '</table>'
# topics_div = Div(text=topics_info, width=int(plot_width*0.9))
# plots.append(widgetbox(topics_div, width=int(plot_width*0.9)))
# log messages
plots.append(get_logged_messages(ulog.logged_messages, plot_width))
# perf & top output
top_data = ''
perf_data = ''
for state in ['pre', 'post']:
if 'perf_top_'+state+'flight' in ulog.msg_info_multiple_dict:
current_top_data = ulog.msg_info_multiple_dict['perf_top_'+state+'flight'][0]
flight_data = cgi.escape('\n'.join(current_top_data))
top_data += '<p>'+state.capitalize()+' Flight:<br/><pre>'+flight_data+'</pre></p>'
if 'perf_counter_'+state+'flight' in ulog.msg_info_multiple_dict:
current_perf_data = ulog.msg_info_multiple_dict['perf_counter_'+state+'flight'][0]
flight_data = cgi.escape('\n'.join(current_perf_data))
perf_data += '<p>'+state.capitalize()+' Flight:<br/><pre>'+flight_data+'</pre></p>'
additional_data_html = ''
if len(top_data) > 0:
additional_data_html += '<h4>Processes</h4>'+top_data
if len(perf_data) > 0:
additional_data_html += '<h4>Performance Counters</h4>'+perf_data
if len(additional_data_html) > 0:
# hide by default & use a button to expand
additional_data_html = '''
<button class="btn btn-common" data-toggle="collapse" style="min-width:0;"
data-target="#show-additional-data">Show additional Data</button>
<div id="show-additional-data" class="collapse">
{:}
</div>
'''.format(additional_data_html)
additional_data_div = Div(text=additional_data_html, width=int(plot_width*0.9))
plots.append(widgetbox(additional_data_div, width=int(plot_width*0.9)))
curdoc().template_variables['plots'] = jinja_plot_data
return plots
def generate_plots(ulog, px4_ulog, db_data, vehicle_data, link_to_3d_page,
link_to_pid_analysis_page):
""" create a list of bokeh plots (and widgets) to show """
plots = []
data = ulog.data_list
# COMPATIBILITY support for old logs
if any(elem.name == 'vehicle_air_data' or elem.name == 'vehicle_magnetometer' for elem in data):
baro_alt_meter_topic = 'vehicle_air_data'
magnetometer_ga_topic = 'vehicle_magnetometer'
else: # old
baro_alt_meter_topic = 'sensor_combined'
magnetometer_ga_topic = 'sensor_combined'
for topic in data:
if topic.name == 'system_power':
# COMPATIBILITY: rename fields to new format
if 'voltage5V_v' in topic.data: # old (prior to PX4/Firmware:213aa93)
topic.data['voltage5v_v'] = topic.data.pop('voltage5V_v')
if 'voltage3V3_v' in topic.data: # old (prior to PX4/Firmware:213aa93)
topic.data['voltage3v3_v'] = topic.data.pop('voltage3V3_v')
if any(elem.name == 'vehicle_angular_velocity' for elem in data):
rate_estimated_topic_name = 'vehicle_angular_velocity'
rate_groundtruth_topic_name = 'vehicle_angular_velocity_groundtruth'
rate_field_names = ['xyz[0]', 'xyz[1]', 'xyz[2]']
else: # old
rate_estimated_topic_name = 'vehicle_attitude'
rate_groundtruth_topic_name = 'vehicle_attitude_groundtruth'
rate_field_names = ['rollspeed', 'pitchspeed', 'yawspeed']
# initialize flight mode changes
flight_mode_changes = get_flight_mode_changes(ulog)
# VTOL state changes & vehicle type
vtol_states = None
is_vtol = False
try:
cur_dataset = ulog.get_dataset('vehicle_status')
if np.amax(cur_dataset.data['is_vtol']) == 1:
is_vtol = True
vtol_states = cur_dataset.list_value_changes('in_transition_mode')
# find mode after transitions (states: 1=transition, 2=FW, 3=MC)
if 'vehicle_type' in cur_dataset.data:
vehicle_type_field = 'vehicle_type'
vtol_state_mapping = {2: 2, 1: 3}
else: # COMPATIBILITY: old logs (https://github.com/PX4/Firmware/pull/11918)
vehicle_type_field = 'is_rotary_wing'
vtol_state_mapping = {0: 2, 1: 3}
for i in range(len(vtol_states)):
if vtol_states[i][1] == 0:
t = vtol_states[i][0]
idx = np.argmax(cur_dataset.data['timestamp'] >= t) + 1
vtol_states[i] = (t, vtol_state_mapping[
cur_dataset.data[vehicle_type_field][idx]])
vtol_states.append((ulog.last_timestamp, -1))
except (KeyError, IndexError) as error:
vtol_states = None
# Heading
curdoc().template_variables['title_html'] = get_heading_html(
ulog, px4_ulog, db_data, link_to_3d_page,
additional_links=[("Open PID Analysis", link_to_pid_analysis_page)])
# info text on top (logging duration, max speed, ...)
curdoc().template_variables['info_table_html'] = \
get_info_table_html(ulog, px4_ulog, db_data, vehicle_data, vtol_states)
curdoc().template_variables['error_labels_html'] = get_error_labels_html()
hardfault_html = get_hardfault_html(ulog)
if hardfault_html is not None:
curdoc().template_variables['hardfault_html'] = hardfault_html
corrupt_log_html = get_corrupt_log_html(ulog)
if corrupt_log_html:
curdoc().template_variables['corrupt_log_html'] = corrupt_log_html
# Position plot
data_plot = DataPlot2D(data, plot_config, 'vehicle_local_position',
x_axis_label='[m]', y_axis_label='[m]', plot_height='large')
data_plot.add_graph('y', 'x', colors2[0], 'Estimated',
check_if_all_zero=True)
if not data_plot.had_error: # vehicle_local_position is required
data_plot.change_dataset('vehicle_local_position_setpoint')
data_plot.add_graph('y', 'x', colors2[1], 'Setpoint')
# groundtruth (SITL only)
data_plot.change_dataset('vehicle_local_position_groundtruth')
data_plot.add_graph('y', 'x', color_gray, 'Groundtruth')
# GPS + position setpoints
plot_map(ulog, plot_config, map_type='plain', setpoints=True,
bokeh_plot=data_plot.bokeh_plot)
if data_plot.finalize() is not None:
plots.append(data_plot.bokeh_plot)
# Leaflet Map
try:
pos_datas, flight_modes = ulog_to_polyline(ulog, flight_mode_changes)
curdoc().template_variables['pos_datas'] = pos_datas
curdoc().template_variables['pos_flight_modes'] = flight_modes
except:
pass
curdoc().template_variables['has_position_data'] = True
# initialize parameter changes
changed_params = None
if not 'replay' in ulog.msg_info_dict: # replay can have many param changes
if len(ulog.changed_parameters) > 0:
changed_params = ulog.changed_parameters
plots.append(None) # save space for the param change button
### Add all data plots ###
x_range_offset = (ulog.last_timestamp - ulog.start_timestamp) * 0.05
x_range = Range1d(ulog.start_timestamp - x_range_offset, ulog.last_timestamp + x_range_offset)
# Altitude estimate
data_plot = DataPlot(data, plot_config, 'vehicle_gps_position',
y_axis_label='[m]', title='Altitude Estimate',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph([lambda data: ('alt', data['alt']*0.001)],
colors8[0:1], ['GPS Altitude'])
data_plot.change_dataset(baro_alt_meter_topic)
data_plot.add_graph(['baro_alt_meter'], colors8[1:2], ['Barometer Altitude'])
data_plot.change_dataset('vehicle_global_position')
data_plot.add_graph(['alt'], colors8[2:3], ['Fused Altitude Estimation'])
data_plot.change_dataset('position_setpoint_triplet')
data_plot.add_circle(['current.alt'], [plot_config['mission_setpoint_color']],
['Altitude Setpoint'])
data_plot.change_dataset('actuator_controls_0')
data_plot.add_graph([lambda data: ('thrust', data['control[3]']*100)],
colors8[6:7], ['Thrust [0, 100]'])
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# Roll/Pitch/Yaw angle & angular rate
for index, axis in enumerate(['roll', 'pitch', 'yaw']):
# angle
axis_name = axis.capitalize()
data_plot = DataPlot(data, plot_config, 'vehicle_attitude',
y_axis_label='[deg]', title=axis_name+' Angle',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([lambda data: (axis, np.rad2deg(data[axis]))],
colors3[0:1], [axis_name+' Estimated'], mark_nan=True)
data_plot.change_dataset('vehicle_attitude_setpoint')
data_plot.add_graph([lambda data: (axis+'_d', np.rad2deg(data[axis+'_d']))],
colors3[1:2], [axis_name+' Setpoint'],
use_step_lines=True)
if axis == 'yaw':
data_plot.add_graph(
[lambda data: ('yaw_sp_move_rate', np.rad2deg(data['yaw_sp_move_rate']))],
colors3[2:3], [axis_name+' FF Setpoint [deg/s]'],
use_step_lines=True)
data_plot.change_dataset('vehicle_attitude_groundtruth')
data_plot.add_graph([lambda data: (axis, np.rad2deg(data[axis]))],
[color_gray], [axis_name+' Groundtruth'])
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# rate
data_plot = DataPlot(data, plot_config, rate_estimated_topic_name,
y_axis_label='[deg/s]', title=axis_name+' Angular Rate',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([lambda data: (axis+'speed',
np.rad2deg(data[rate_field_names[index]]))],
colors3[0:1], [axis_name+' Rate Estimated'], mark_nan=True)
data_plot.change_dataset('vehicle_rates_setpoint')
data_plot.add_graph([lambda data: (axis, np.rad2deg(data[axis]))],
colors3[1:2], [axis_name+' Rate Setpoint'],
mark_nan=True, use_step_lines=True)
axis_letter = axis[0].upper()
rate_int_limit = '(*100)'
# this param is MC/VTOL only (it will not exist on FW)
rate_int_limit_param = 'MC_' + axis_letter + 'R_INT_LIM'
if rate_int_limit_param in ulog.initial_parameters:
rate_int_limit = '[-{0:.0f}, {0:.0f}]'.format(
ulog.initial_parameters[rate_int_limit_param]*100)
data_plot.change_dataset('rate_ctrl_status')
data_plot.add_graph([lambda data: (axis, data[axis+'speed_integ']*100)],
colors3[2:3], [axis_name+' Rate Integral '+rate_int_limit])
data_plot.change_dataset(rate_groundtruth_topic_name)
data_plot.add_graph([lambda data: (axis+'speed',
np.rad2deg(data[rate_field_names[index]]))],
[color_gray], [axis_name+' Rate Groundtruth'])
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# Local position
for axis in ['x', 'y', 'z']:
data_plot = DataPlot(data, plot_config, 'vehicle_local_position',
y_axis_label='[m]', title='Local Position '+axis.upper(),
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([axis], colors2[0:1], [axis.upper()+' Estimated'], mark_nan=True)
data_plot.change_dataset('vehicle_local_position_setpoint')
data_plot.add_graph([axis], colors2[1:2], [axis.upper()+' Setpoint'],
use_step_lines=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# Velocity
data_plot = DataPlot(data, plot_config, 'vehicle_local_position',
y_axis_label='[m/s]', title='Velocity',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['vx', 'vy', 'vz'], colors8[0:3], ['X', 'Y', 'Z'])
data_plot.change_dataset('vehicle_local_position_setpoint')
data_plot.add_graph(['vx', 'vy', 'vz'], [colors8[5], colors8[4], colors8[6]],
['X Setpoint', 'Y Setpoint', 'Z Setpoint'], use_step_lines=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# Visual Odometry (only if topic found)
if any(elem.name == 'vehicle_visual_odometry' for elem in data):
# Vision position
data_plot = DataPlot(data, plot_config, 'vehicle_visual_odometry',
y_axis_label='[m]', title='Visual Odometry Position',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['x', 'y', 'z'], colors3, ['X', 'Y', 'Z'], mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
data_plot.change_dataset('vehicle_local_position_groundtruth')
data_plot.add_graph(['x', 'y', 'z'], colors8[2:5],
['Groundtruth X', 'Groundtruth Y', 'Groundtruth Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# Vision velocity
data_plot = DataPlot(data, plot_config, 'vehicle_visual_odometry',
y_axis_label='[m]', title='Visual Odometry Velocity',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['vx', 'vy', 'vz'], colors3, ['X', 'Y', 'Z'], mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
data_plot.change_dataset('vehicle_local_position_groundtruth')
data_plot.add_graph(['vx', 'vy', 'vz'], colors8[2:5],
['Groundtruth VX', 'Groundtruth VY', 'Groundtruth VZ'])
if data_plot.finalize() is not None: plots.append(data_plot)
# Vision attitude
data_plot = DataPlot(data, plot_config, 'vehicle_visual_odometry',
y_axis_label='[deg]', title='Visual Odometry Attitude',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([lambda data: ('roll', np.rad2deg(data['roll'])),
lambda data: ('pitch', np.rad2deg(data['pitch'])),
lambda data: ('yaw', np.rad2deg(data['yaw']))],
colors3, ['Roll', 'Pitch', 'Yaw'], mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
data_plot.change_dataset('vehicle_attitude_groundtruth')
data_plot.add_graph([lambda data: ('roll', np.rad2deg(data['roll'])),
lambda data: ('pitch', np.rad2deg(data['pitch'])),
lambda data: ('yaw', np.rad2deg(data['yaw']))],
colors8[2:5],
['Roll Groundtruth', 'Pitch Groundtruth', 'Yaw Groundtruth'])
# Vision attitude rate
data_plot = DataPlot(data, plot_config, 'vehicle_visual_odometry',
y_axis_label='[deg]', title='Visual Odometry Attitude Rate',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([lambda data: ('rollspeed', np.rad2deg(data['rollspeed'])),
lambda data: ('pitchspeed', np.rad2deg(data['pitchspeed'])),
lambda data: ('yawspeed', np.rad2deg(data['yawspeed']))],
colors3, ['Roll Rate', 'Pitch Rate', 'Yaw Rate'], mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
data_plot.change_dataset(rate_groundtruth_topic_name)
data_plot.add_graph([lambda data: ('rollspeed', np.rad2deg(data[rate_field_names[0]])),
lambda data: ('pitchspeed', np.rad2deg(data[rate_field_names[1]])),
lambda data: ('yawspeed', np.rad2deg(data[rate_field_names[2]]))],
colors8[2:5],
['Roll Rate Groundtruth', 'Pitch Rate Groundtruth',
'Yaw Rate Groundtruth'])
if data_plot.finalize() is not None: plots.append(data_plot)
# Airspeed vs Ground speed: but only if there's valid airspeed data or a VTOL
try:
if is_vtol or ulog.get_dataset('airspeed') is not None:
data_plot = DataPlot(data, plot_config, 'vehicle_global_position',
y_axis_label='[m/s]', title='Airspeed',
plot_height='small',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph([lambda data: ('groundspeed_estimated',
np.sqrt(data['vel_n']**2 + data['vel_e']**2))],
colors8[0:1], ['Ground Speed Estimated'])
data_plot.change_dataset('airspeed')
data_plot.add_graph(['indicated_airspeed_m_s'], colors8[1:2], ['Airspeed Indicated'])
data_plot.change_dataset('vehicle_gps_position')
data_plot.add_graph(['vel_m_s'], colors8[2:3], ['Ground Speed (from GPS)'])
data_plot.change_dataset('tecs_status')
data_plot.add_graph(['airspeed_sp'], colors8[3:4], ['Airspeed Setpoint'])
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
except (KeyError, IndexError) as error:
pass
# TECS (fixed-wing or VTOLs)
data_plot = DataPlot(data, plot_config, 'tecs_status', y_start=0, title='TECS',
y_axis_label='[m/s]', plot_height='small',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(['height_rate', 'height_rate_setpoint'],
colors2, ['Height Rate', 'Height Rate Setpoint'],
mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# manual control inputs
# prefer the manual_control_setpoint topic. Old logs do not contain it
if any(elem.name == 'manual_control_setpoint' for elem in data):
data_plot = DataPlot(data, plot_config, 'manual_control_setpoint',
title='Manual Control Inputs (Radio or Joystick)',
plot_height='small', y_range=Range1d(-1.1, 1.1),
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(['y', 'x', 'r', 'z',
lambda data: ('mode_slot', data['mode_slot']/6),
'aux1', 'aux2',
lambda data: ('kill_switch', data['kill_switch'] == 1)],
colors8,
['Y / Roll', 'X / Pitch', 'Yaw', 'Throttle [0, 1]',
'Flight Mode', 'Aux1', 'Aux2', 'Kill Switch'])
# TODO: add RTL switch and others? Look at params which functions are mapped?
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
else: # it's an old log (COMPATIBILITY)
data_plot = DataPlot(data, plot_config, 'rc_channels',
title='Raw Radio Control Inputs',
plot_height='small', y_range=Range1d(-1.1, 1.1),
changed_params=changed_params, x_range=x_range)
num_rc_channels = 8
if data_plot.dataset:
max_channels = np.amax(data_plot.dataset.data['channel_count'])
if max_channels < num_rc_channels: num_rc_channels = max_channels
legends = []
for i in range(num_rc_channels):
channel_names = px4_ulog.get_configured_rc_input_names(i)
if channel_names is None:
legends.append('Channel '+str(i))
else:
legends.append('Channel '+str(i)+' ('+', '.join(channel_names)+')')
data_plot.add_graph(['channels['+str(i)+']' for i in range(num_rc_channels)],
colors8[0:num_rc_channels], legends, mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator controls 0
data_plot = DataPlot(data, plot_config, 'actuator_controls_0',
y_start=0, title='Actuator Controls 0', plot_height='small',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(['control[0]', 'control[1]', 'control[2]', 'control[3]'],
colors8[0:4], ['Roll', 'Pitch', 'Yaw', 'Thrust'], mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator controls (Main) FFT (for filter & output noise analysis)
data_plot = DataPlotFFT(data, plot_config, 'actuator_controls_0',
title='Actuator Controls FFT')
data_plot.add_graph(['control[0]', 'control[1]', 'control[2]'],
colors3, ['Roll', 'Pitch', 'Yaw'])
if not data_plot.had_error:
if 'MC_DTERM_CUTOFF' in ulog.initial_parameters:
data_plot.mark_frequency(
ulog.initial_parameters['MC_DTERM_CUTOFF'],
'MC_DTERM_CUTOFF')
if 'IMU_GYRO_CUTOFF' in ulog.initial_parameters:
data_plot.mark_frequency(
ulog.initial_parameters['IMU_GYRO_CUTOFF'],
'IMU_GYRO_CUTOFF', 20)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator controls 1
# (only present on VTOL, Fixed-wing config)
data_plot = DataPlot(data, plot_config, 'actuator_controls_1',
y_start=0, title='Actuator Controls 1 (VTOL in Fixed-Wing mode)',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['control[0]', 'control[1]', 'control[2]', 'control[3]'],
colors8[0:4], ['Roll', 'Pitch', 'Yaw', 'Thrust'], mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator outputs 0: Main
data_plot = DataPlot(data, plot_config, 'actuator_outputs',
y_start=0, title='Actuator Outputs (Main)', plot_height='small',
changed_params=changed_params, x_range=x_range)
num_actuator_outputs = 8
if data_plot.dataset:
max_outputs = np.amax(data_plot.dataset.data['noutputs'])
if max_outputs < num_actuator_outputs: num_actuator_outputs = max_outputs
data_plot.add_graph(['output['+str(i)+']' for i in
range(num_actuator_outputs)], colors8[0:num_actuator_outputs],
['Output '+str(i) for i in range(num_actuator_outputs)], mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# actuator outputs 1: AUX
data_plot = DataPlot(data, plot_config, 'actuator_outputs',
y_start=0, title='Actuator Outputs (AUX)', plot_height='small',
changed_params=changed_params, topic_instance=1,
x_range=x_range)
num_actuator_outputs = 8
# only plot if at least one of the outputs is not constant
all_constant = True
if data_plot.dataset:
max_outputs = np.amax(data_plot.dataset.data['noutputs'])
if max_outputs < num_actuator_outputs: num_actuator_outputs = max_outputs
for i in range(num_actuator_outputs):
output_data = data_plot.dataset.data['output['+str(i)+']']
if not np.all(output_data == output_data[0]):
all_constant = False
if not all_constant:
data_plot.add_graph(['output['+str(i)+']' for i in
range(num_actuator_outputs)], colors8[0:num_actuator_outputs],
['Output '+str(i) for i in range(num_actuator_outputs)], mark_nan=True)
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# raw acceleration
data_plot = DataPlot(data, plot_config, 'sensor_combined',
y_axis_label='[m/s^2]', title='Raw Acceleration',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['accelerometer_m_s2[0]', 'accelerometer_m_s2[1]',
'accelerometer_m_s2[2]'], colors3, ['X', 'Y', 'Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# Vibration Metrics
data_plot = DataPlot(data, plot_config, 'estimator_status',
title='Vibration Metrics',
plot_height='small', changed_params=changed_params,
x_range=x_range, y_start=0)
data_plot.add_graph(['vibe[2]'], colors3[2:3], ['Delta Velocity Vibration Level [m/s]'])
data_plot.add_horizontal_background_boxes(
['green', 'orange', 'red'], [0.02, 0.04])
if data_plot.finalize() is not None: plots.append(data_plot)
# Acceleration Spectrogram
data_plot = DataPlotSpec(data, plot_config, 'sensor_combined',
y_axis_label='[Hz]', title='Acceleration Power Spectral Density',
plot_height='small', x_range=x_range)
data_plot.add_graph(['accelerometer_m_s2[0]', 'accelerometer_m_s2[1]', 'accelerometer_m_s2[2]'],
['X', 'Y', 'Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# raw angular speed
data_plot = DataPlot(data, plot_config, 'sensor_combined',
y_axis_label='[deg/s]', title='Raw Angular Speed (Gyroscope)',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph([
lambda data: ('gyro_rad[0]', np.rad2deg(data['gyro_rad[0]'])),
lambda data: ('gyro_rad[1]', np.rad2deg(data['gyro_rad[1]'])),
lambda data: ('gyro_rad[2]', np.rad2deg(data['gyro_rad[2]']))],
colors3, ['X', 'Y', 'Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# magnetic field strength
data_plot = DataPlot(data, plot_config, magnetometer_ga_topic,
y_axis_label='[gauss]', title='Raw Magnetic Field Strength',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['magnetometer_ga[0]', 'magnetometer_ga[1]',
'magnetometer_ga[2]'], colors3,
['X', 'Y', 'Z'])
if data_plot.finalize() is not None: plots.append(data_plot)
# distance sensor
data_plot = DataPlot(data, plot_config, 'distance_sensor',
y_start=0, y_axis_label='[m]', title='Distance Sensor',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['current_distance', 'covariance'], colors3[0:2],
['Distance', 'Covariance'])
if data_plot.finalize() is not None: plots.append(data_plot)
# gps uncertainty
# the accuracy values can be really large if there is no fix, so we limit the
# y axis range to some sane values
data_plot = DataPlot(data, plot_config, 'vehicle_gps_position',
title='GPS Uncertainty', y_range=Range1d(0, 40),
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['eph', 'epv', 'satellites_used', 'fix_type'], colors8[::2],
['Horizontal position accuracy [m]', 'Vertical position accuracy [m]',
'Num Satellites used', 'GPS Fix'])
if data_plot.finalize() is not None: plots.append(data_plot)
# gps noise & jamming
data_plot = DataPlot(data, plot_config, 'vehicle_gps_position',
y_start=0, title='GPS Noise & Jamming',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['noise_per_ms', 'jamming_indicator'], colors3[0:2],
['Noise per ms', 'Jamming Indicator'])
if data_plot.finalize() is not None: plots.append(data_plot)
# thrust and magnetic field
data_plot = DataPlot(data, plot_config, magnetometer_ga_topic,
y_start=0, title='Thrust and Magnetic Field', plot_height='small',
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(
[lambda data: ('len_mag', np.sqrt(data['magnetometer_ga[0]']**2 +
data['magnetometer_ga[1]']**2 +
data['magnetometer_ga[2]']**2))],
colors3[0:1], ['Norm of Magnetic Field'])
data_plot.change_dataset('actuator_controls_0')
data_plot.add_graph([lambda data: ('thrust', data['control[3]'])],
colors3[1:2], ['Thrust'])
if is_vtol:
data_plot.change_dataset('actuator_controls_1')
data_plot.add_graph([lambda data: ('thrust', data['control[3]'])],
colors3[2:3], ['Thrust (Fixed-wing)'])
if data_plot.finalize() is not None: plots.append(data_plot)
# power
data_plot = DataPlot(data, plot_config, 'battery_status',
y_start=0, title='Power',
plot_height='small', changed_params=changed_params,
x_range=x_range)
data_plot.add_graph(['voltage_v', 'voltage_filtered_v',
'current_a', lambda data: ('discharged_mah', data['discharged_mah']/100),
lambda data: ('remaining', data['remaining']*10)],
colors8[::2]+colors8[1:2],
['Battery Voltage [V]', 'Battery Voltage filtered [V]',
'Battery Current [A]', 'Discharged Amount [mAh / 100]',
'Battery remaining [0=empty, 10=full]'])
data_plot.change_dataset('system_power')
if data_plot.dataset:
if 'voltage5v_v' in data_plot.dataset.data and \
np.amax(data_plot.dataset.data['voltage5v_v']) > 0.0001:
data_plot.add_graph(['voltage5v_v'], colors8[7:8], ['5 V'])
if 'voltage3v3_v' in data_plot.dataset.data and \
np.amax(data_plot.dataset.data['voltage3v3_v']) > 0.0001:
data_plot.add_graph(['voltage3v3_v'], colors8[5:6], ['3.3 V'])
if data_plot.finalize() is not None: plots.append(data_plot)
# estimator watchdog
try:
data_plot = DataPlot(data, plot_config, 'estimator_status',
y_start=0, title='Estimator Watchdog',
plot_height='small', changed_params=changed_params,
x_range=x_range)
estimator_status = ulog.get_dataset('estimator_status').data
plot_data = []
plot_labels = []
input_data = [
('Health Flags (vel, pos, hgt)', estimator_status['health_flags']),
('Timeout Flags (vel, pos, hgt)', estimator_status['timeout_flags']),
('Velocity Check Bit', (estimator_status['innovation_check_flags'])&0x1),
('Horizontal Position Check Bit', (estimator_status['innovation_check_flags']>>1)&1),
('Vertical Position Check Bit', (estimator_status['innovation_check_flags']>>2)&1),
('Mag X, Y, Z Check Bits', (estimator_status['innovation_check_flags']>>3)&0x7),
('Yaw Check Bit', (estimator_status['innovation_check_flags']>>6)&1),
('Airspeed Check Bit', (estimator_status['innovation_check_flags']>>7)&1),
('Synthetic Sideslip Check Bit', (estimator_status['innovation_check_flags']>>8)&1),
('Height to Ground Check Bit', (estimator_status['innovation_check_flags']>>9)&1),
('Optical Flow X, Y Check Bits', (estimator_status['innovation_check_flags']>>10)&0x3),
]
# filter: show only the flags that have non-zero samples
for cur_label, cur_data in input_data:
if np.amax(cur_data) > 0.1:
data_label = 'flags_'+str(len(plot_data)) # just some unique string
plot_data.append(lambda d, data=cur_data, label=data_label: (label, data))
plot_labels.append(cur_label)
if len(plot_data) >= 8: # cannot add more than that
break
if len(plot_data) == 0:
# add the plot even in the absence of any problem, so that the user
# can validate that (otherwise it's ambiguous: it could be that the
# estimator_status topic is not logged)
plot_data = [lambda d: ('flags', input_data[0][1])]
plot_labels = [input_data[0][0]]
data_plot.add_graph(plot_data, colors8[0:len(plot_data)], plot_labels)
if data_plot.finalize() is not None: plots.append(data_plot)
except (KeyError, IndexError) as error:
print('Error in estimator plot: '+str(error))
# RC Quality
data_plot = DataPlot(data, plot_config, 'input_rc',
title='RC Quality', plot_height='small', y_range=Range1d(0, 1),
changed_params=changed_params, x_range=x_range)
data_plot.add_graph([lambda data: ('rssi', data['rssi']/100), 'rc_lost'],
colors3[0:2], ['RSSI [0, 1]', 'RC Lost (Indicator)'])
data_plot.change_dataset('vehicle_status')
data_plot.add_graph(['rc_signal_lost'], colors3[2:3], ['RC Lost (Detected)'])
if data_plot.finalize() is not None: plots.append(data_plot)
# cpu load
data_plot = DataPlot(data, plot_config, 'cpuload',
title='CPU & RAM', plot_height='small', y_range=Range1d(0, 1),
changed_params=changed_params, x_range=x_range)
data_plot.add_graph(['ram_usage', 'load'], [colors3[1], colors3[2]],
['RAM Usage', 'CPU Load'])
data_plot.add_span('load', line_color=colors3[2])
data_plot.add_span('ram_usage', line_color=colors3[1])
plot_flight_modes_background(data_plot, flight_mode_changes, vtol_states)
if data_plot.finalize() is not None: plots.append(data_plot)
# sampling: time difference
try:
data_plot = DataPlot(data, plot_config, 'sensor_combined', y_range=Range1d(0, 25e3),
y_axis_label='[us]',
title='Sampling Regularity of Sensor Data', plot_height='small',
changed_params=changed_params, x_range=x_range)
sensor_combined = ulog.get_dataset('sensor_combined').data
sampling_diff = np.diff(sensor_combined['timestamp'])
min_sampling_diff = np.amin(sampling_diff)
plot_dropouts(data_plot.bokeh_plot, ulog.dropouts, min_sampling_diff)
data_plot.add_graph([lambda data: ('timediff', np.append(sampling_diff, 0))],
[colors3[2]], ['delta t (between 2 logged samples)'])
data_plot.change_dataset('estimator_status')
data_plot.add_graph([lambda data: ('time_slip', data['time_slip']*1e6)],
[colors3[1]], ['Estimator time slip (cumulative)'])
if data_plot.finalize() is not None: plots.append(data_plot)
except:
pass
# exchange all DataPlot's with the bokeh_plot and handle parameter changes
param_changes_button = Button(label="Hide Parameter Changes", width=170)
param_change_labels = []
# FIXME: this should be a CustomJS callback, not on the server. However this
# did not work for me.
def param_changes_button_clicked():
""" callback to show/hide parameter changes """
for label in param_change_labels:
if label.visible:
param_changes_button.label = 'Show Parameter Changes'
label.visible = False
label.text_alpha = 0 # label.visible does not work, so we use this instead
else:
param_changes_button.label = 'Hide Parameter Changes'
label.visible = True
label.text_alpha = 1
param_changes_button.on_click(param_changes_button_clicked)
jinja_plot_data = []
for i in range(len(plots)):
if plots[i] is None:
plots[i] = widgetbox(param_changes_button, width=int(plot_width * 0.99))
if isinstance(plots[i], DataPlot):
if plots[i].param_change_label is not None:
param_change_labels.append(plots[i].param_change_label)
plot_title = plots[i].title
plots[i] = plots[i].bokeh_plot
fragment = 'Nav-'+plot_title.replace(' ', '-') \
.replace('&', '_').replace('(', '').replace(')', '')
jinja_plot_data.append({
'model_id': plots[i].ref['id'],
'fragment': fragment,
'title': plot_title
})
# changed parameters
plots.append(get_changed_parameters(ulog.initial_parameters, plot_width))
# information about which messages are contained in the log
# TODO: need to load all topics for this (-> log loading will take longer)
# but if we load all topics and the log contains some (external) topics
# with buggy timestamps, it will affect the plotting.
# data_list_sorted = sorted(ulog.data_list, key=lambda d: d.name + str(d.multi_id))
# table_text = []
# for d in data_list_sorted:
# message_size = sum([ULog.get_field_size(f.type_str) for f in d.field_data])
# num_data_points = len(d.data['timestamp'])
# table_text.append((d.name, str(d.multi_id), str(message_size), str(num_data_points),
# str(message_size * num_data_points)))
# topics_info = '<table><tr><th>Name</th><th>Topic instance</th><th>Message Size</th>' \
# '<th>Number of data points</th><th>Total bytes</th></tr>' + ''.join(
# ['<tr><td>'+'</td><td>'.join(list(x))+'</td></tr>' for x in table_text]) + '</table>'
# topics_div = Div(text=topics_info, width=int(plot_width*0.9))
# plots.append(widgetbox(topics_div, width=int(plot_width*0.9)))
# log messages
plots.append(get_logged_messages(ulog.logged_messages, plot_width))
# console messages, perf & top output
top_data = ''
perf_data = ''
console_messages = ''
if 'boot_console_output' in ulog.msg_info_multiple_dict:
console_output = ulog.msg_info_multiple_dict['boot_console_output'][0]
console_output = escape(''.join(console_output))
console_messages = '<p><pre>'+console_output+'</pre></p>'
for state in ['pre', 'post']:
if 'perf_top_'+state+'flight' in ulog.msg_info_multiple_dict:
current_top_data = ulog.msg_info_multiple_dict['perf_top_'+state+'flight'][0]
flight_data = escape('\n'.join(current_top_data))
top_data += '<p>'+state.capitalize()+' Flight:<br/><pre>'+flight_data+'</pre></p>'
if 'perf_counter_'+state+'flight' in ulog.msg_info_multiple_dict:
current_perf_data = ulog.msg_info_multiple_dict['perf_counter_'+state+'flight'][0]
flight_data = escape('\n'.join(current_perf_data))
perf_data += '<p>'+state.capitalize()+' Flight:<br/><pre>'+flight_data+'</pre></p>'
additional_data_html = ''
if len(console_messages) > 0:
additional_data_html += '<h5>Console Output</h5>'+console_messages
if len(top_data) > 0:
additional_data_html += '<h5>Processes</h5>'+top_data
if len(perf_data) > 0:
additional_data_html += '<h5>Performance Counters</h5>'+perf_data
if len(additional_data_html) > 0:
# hide by default & use a button to expand
additional_data_html = '''
<button id="show-additional-data-btn" class="btn btn-secondary" data-toggle="collapse" style="min-width:0;"
data-target="#show-additional-data">Show additional Data</button>
<div id="show-additional-data" class="collapse">
{:}
</div>
'''.format(additional_data_html)
curdoc().template_variables['additional_info'] = additional_data_html
curdoc().template_variables['plots'] = jinja_plot_data
return plots
