def suspension_vel_der_diff_l_r_f_r_over_time(ax, session_data):
race_time = data_processing.get_run_time_cleaned(session_data=session_data)
susp_fl = session_data[networking.Fields.susp_fl.value]
susp_fr = session_data[networking.Fields.susp_fr.value]
susp_rl = session_data[networking.Fields.susp_rl.value]
susp_rr = session_data[networking.Fields.susp_rr.value]
susp_data = [susp_fl, susp_fr, susp_rl, susp_rr]
susp_data = [
data_processing.derive_no_nan(susp, race_time) for susp in susp_data
]
susp_vel_fl = session_data[networking.Fields.susp_vel_fl.value]
susp_vel_fr = session_data[networking.Fields.susp_vel_fr.value]
susp_vel_rl = session_data[networking.Fields.susp_vel_rl.value]
susp_vel_rr = session_data[networking.Fields.susp_vel_rr.value]
susp_vel = [susp_vel_fl, susp_vel_fr, susp_vel_rl, susp_vel_rr]
susp_data = np.array(susp_data) - np.array(susp_vel)
labels = ['susp_fl', 'susp_fr', 'susp_rl', 'susp_rr']
x_points = np.array([race_time] * len(susp_data))
y_points = np.array(susp_data)
line_plot(ax,
x_points=x_points,
y_points=y_points,
title='Suspension velocity derived over time',
labels=labels,
alpha=0.5,
x_label='Time (s)',
y_label='Suspension velocity derived - given (mm/s)',
flip_y=True,
min_max_annotations=True)
def drift_angle_change_bars(ax, session_data):
race_time = data_processing.get_run_time_cleaned(session_data=session_data)
time_differences = data_processing.differences(race_time)
# prevent negative times due to next lap
time_differences[time_differences < 0.0] = np.finfo(
time_differences.dtype).eps
speed_ms = session_data[networking.Fields.speed_ms.value]
drift_angle_deg = data_processing.get_drift_angle(session_data)
drift_angle_deg_der = data_processing.derive_no_nan(drift_angle_deg,
time_steps=race_time)
# filter very slow parts
fast_enough = speed_ms > 1.0 # m/s
drift_angle_deg_der = drift_angle_deg_der[fast_enough]
drift_angle_deg_der = np.abs(drift_angle_deg_der)
# filter out rare extreme values
outlier_threshold = np.nanpercentile(drift_angle_deg_der, 99)
usual_values = drift_angle_deg_der < outlier_threshold
drift_angle_deg_der = drift_angle_deg_der[usual_values]
time_differences = time_differences[fast_enough]
time_differences = time_differences[usual_values]
series_labels = ['']
bar_plot(ax,
data=[drift_angle_deg_der],
weights=[time_differences],
num_bins=10,
title='Drift Angle Change Histogram',
x_label='Drift Angle Change (deg/s)',
y_label='Accumulated Time (s)',
series_labels=series_labels)
def power_over_time(ax, session_data):
race_time = data_processing.get_run_time_cleaned(session_data=session_data)
energy, kinetic_energy, potential_energy = data_processing.get_energy(
session_data=session_data)
power = data_processing.derive_no_nan(energy, race_time)
full_acceleration = data_processing.get_full_acceleration_mask(
session_data=session_data)
not_full_acceleration = np.logical_not(full_acceleration)
power_full_acceleration = power.copy()
power_full_acceleration[not_full_acceleration] = 0.0
power_not_full_acceleration = power.copy()
power_not_full_acceleration[full_acceleration] = 0.0
power_data = np.array(
[power_full_acceleration, power_not_full_acceleration])
labels = ['Power at full throttle (kW)', 'Power otherwise (kW)']
y_points = power_data
x_points = np.array([race_time] * y_points.shape[0])
line_plot(ax,
x_points=x_points,
y_points=y_points,
title='Power over time',
labels=labels,
alpha=0.5,
x_label='Time (s)',
y_label='Power (kW)',
min_max_annotations=False)
def drift_over_speed(ax, session_data):
steering = np.abs(session_data[networking.Fields.steering.value])
speed_ms = session_data[networking.Fields.speed_ms.value]
drift_angle_deg = data_processing.get_drift_angle(session_data)
race_time = data_processing.get_run_time_cleaned(session_data=session_data)
drift_angle_deg_der = data_processing.derive_no_nan(drift_angle_deg,
time_steps=race_time)
# filter very slow parts
fast_enough = speed_ms > 1.0 # m/s
steering = steering[fast_enough]
speed_ms = speed_ms[fast_enough]
# drift_angle_deg = drift_angle_deg[fast_enough]
drift_angle_deg_der = drift_angle_deg_der[fast_enough]
colors = [steering]
scales = [25]
alphas = [0.5]
labels = ['drift over steer']
# scatter_plot(ax, x_points=[speed_ms], y_points=[drift_angle_deg],
# title='Drift over speed (steering as color)',
# labels=labels, colors=colors, scales=scales, alphas=alphas,
# x_label='Speed (m/s)', y_label='Drift angle (deg)')
scatter_plot(ax,
x_points=[speed_ms],
y_points=[drift_angle_deg_der],
title='Drift change over speed (steering as color)',
labels=labels,
colors=colors,
scales=scales,
alphas=alphas,
x_label='Speed (m/s)',
y_label='Drift angle (deg/s)')
def plot_p_over_vel(ax, session_data):
data_gear = session_data[networking.Fields.gear.value]
range_gears = list(set(data_gear))
range_gears.sort()
labels = ['Gear {}'.format(str(g)) for g in range_gears]
scale = 50.0
alphas = [0.5] * len(labels)
colors = [static_colors[i] for i, g in enumerate(range_gears)]
full_acceleration_mask = data_processing.get_full_acceleration_mask(
session_data=session_data)
energy, kinetic_energy, potential_energy = data_processing.get_energy(
session_data=session_data)
times_steps = data_processing.get_run_time_cleaned(
session_data=session_data)
power = data_processing.derive_no_nan(x=energy,
time_steps=times_steps) / 1000.0
x_points = []
y_points = []
scales = []
for gear in range_gears:
current_gear = session_data[networking.Fields.gear.value] == gear
interesting = np.logical_and(current_gear, full_acceleration_mask)
speed_ms = session_data[networking.Fields.speed_ms.value]
x_points += [speed_ms[interesting]]
y_points += [power[interesting]]
scales += [np.ones_like(speed_ms[interesting]) * scale]
scatter_plot(ax,
x_points=x_points,
y_points=y_points,
title='Power over velocity (full throttle)',
labels=labels,
colors=colors,
scales=scales,
alphas=alphas,
x_label='Velocity (m/s)',
y_label='Power (kW)')