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 plot_energy_over_2d_pos(ax, session_data):
scale_factor = 100.0
gear = session_data[networking.Fields.gear.value]
pos_x, pos_y = data_processing.get_2d_coordinates(session_data)
range_gears = np.unique(gear)
energy, kinetic_energy, potential_energy = data_processing.get_energy(
session_data=session_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 = session_data[networking.Fields.gear.value] == g
lines_x += [pos_x[current_gear]]
lines_y += [pos_y[current_gear]]
scales += [energy_normalized[current_gear] * scale_factor]
plot_over_2d_pos(ax,
session_data=session_data,
lines_x=lines_x,
lines_y=lines_y,
scale=scales,
alpha=0.5,
title='Gear at 2D positions, scaled by energy',
labels=labels)
def energy_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)
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 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)')