def plot_p_over_vel(ax, plot_data: pd.PlotData):
range_gears = np.unique(plot_data.gear)
range_gears = np.sort(range_gears)
range_gears = range_gears[range_gears > 0.0]
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(plot_data=plot_data)
energy, kinetic_energy, potential_energy = data_processing.get_energy(plot_data=plot_data)
times_steps = plot_data.run_time
power = data_processing.derive_no_nan(x=kinetic_energy, time_steps=times_steps) / 1000.0
x_points = []
y_points = []
scales = []
for gear in range_gears:
current_gear = plot_data.gear == gear
interesting = np.logical_and(current_gear, full_acceleration_mask)
speed_ms = plot_data.speed_ms
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)', plot_mean=True, plot_polynomial=False)
def energy_over_time(ax, plot_data: pd.PlotData):
race_time = plot_data.run_time
energy, kinetic_energy, potential_energy = data_processing.get_energy(plot_data=plot_data)
energy_data = np.array([energy, kinetic_energy, potential_energy])
labels = ['Energy (kJ)', 'Kinetic Energy (kJ)', 'Potential Energy (kJ)']
y_points = energy_data
x_points = np.array([race_time] * y_points.shape[0])
line_plot(ax, x_points=x_points, y_points=y_points, title='Energy over time',
labels=labels, alpha=0.5, x_label='Time (s)', y_label='Energy (kJ)', min_max_annotations=False)
def power_over_time(ax, plot_data: pd.PlotData):
race_time = plot_data.run_time
energy, kinetic_energy, potential_energy = data_processing.get_energy(plot_data=plot_data)
# energy is dominated by potential energy -> take only kinetic energy
power = data_processing.derive_no_nan(kinetic_energy, race_time)
full_acceleration = data_processing.get_full_acceleration_mask(plot_data=plot_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])
power_data_mean = [np.mean(d) for d in power_data]
labels = ['Power at full throttle (kW)', 'Power otherwise (kW)']
labels = [l + ', mean: {:.1f}'.format(power_data_mean[li]) for li, l in enumerate(labels)]
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 plot_energy_over_2d_pos(ax, plot_data: pd.PlotData):
scale_factor = 100.0
range_gears = np.unique(plot_data.gear)
range_gears = np.sort(range_gears)
energy, kinetic_energy, potential_energy = data_processing.get_energy(plot_data=plot_data)
energy_truncated = energy - np.min(energy)
energy_normalized = energy_truncated / np.max(energy_truncated)
labels = ['Gear {}'.format(str(g)) for g in range_gears]
lines_x = []
lines_y = []
scales = []
for i, g in enumerate(range_gears):
current_gear = plot_data.gear == g
lines_x += [plot_data.pos_x[current_gear]]
lines_y += [plot_data.pos_y[current_gear]]
scales += [energy_normalized[current_gear] * scale_factor]
plot_over_2d_pos(ax, plot_data=plot_data, lines_x=lines_x, lines_y=lines_y,
scale=scales, alpha=0.5, title='Gear at 2D positions, scaled by energy', labels=labels)