Telemetry Analysis Examples

import matplotlib.pyplot as plt
import fastf1.plotting

# Enable Matplotlib patches for plotting timedelta values and load
# FastF1's dark color scheme
fastf1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

# load a session and its telemetry data
session = fastf1.get_session(2021, 'Spanish Grand Prix', 'Q')
session.load()

# Select the two laps that we want to compare
ver_lap = session.laps.pick_drivers('VER').pick_fastest()
ham_lap = session.laps.pick_drivers('HAM').pick_fastest()

# Get the telemetry data for each lap and add distance column
ver_tel = ver_lap.get_car_data().add_distance()
ham_tel = ham_lap.get_car_data().add_distance()

# Create the plot with team colors
rbr_color = fastf1.plotting.get_team_color(ver_lap['Team'], session=session)
mer_color = fastf1.plotting.get_team_color(ham_lap['Team'], session=session)

fig, ax = plt.subplots()
ax.plot(ver_tel['Distance'], ver_tel['Speed'], color=rbr_color, label='VER')
ax.plot(ham_tel['Distance'], ham_tel['Speed'], color=mer_color, label='HAM')

ax.set_xlabel('Distance in m')
ax.set_ylabel('Speed in km/h')
ax.legend()
plt.suptitle(f"Fastest Lap Comparison \n "
             f"{session.event['EventName']} {session.event.year} Qualifying")

plt.show()

import matplotlib as mpl
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.collections import LineCollection
import fastf1 as ff1

# Define variables
year = 2021
wknd = 9
ses = 'R'
driver = 'RIC'
colormap = mpl.cm.plasma

# Load the session and select the desired data
session = ff1.get_session(year, wknd, ses)
weekend = session.event
session.load()
lap = session.laps.pick_drivers(driver).pick_fastest()

# Get telemetry data
x = lap.telemetry['X']              # values for x-axis
y = lap.telemetry['Y']              # values for y-axis
color = lap.telemetry['Speed']      # value to base color gradient on

# Create line segments for coloring
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)

# Create the plot
fig, ax = plt.subplots(sharex=True, sharey=True, figsize=(12, 6.75))
fig.suptitle(f'{weekend.name} {year} - {driver} - Speed', size=24, y=0.97)

plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.12)
ax.axis('off')

# Create background track line
ax.plot(lap.telemetry['X'], lap.telemetry['Y'],
        color='black', linestyle='-', linewidth=16, zorder=0)

# Create a continuous norm to map from data points to colors
norm = plt.Normalize(color.min(), color.max())
lc = LineCollection(segments, cmap=colormap, norm=norm,
                    linestyle='-', linewidth=5)
lc.set_array(color)
line = ax.add_collection(lc)

# Add color bar as legend
cbaxes = fig.add_axes([0.25, 0.05, 0.5, 0.05])
normlegend = mpl.colors.Normalize(vmin=color.min(), vmax=color.max())
legend = mpl.colorbar.ColorbarBase(cbaxes, norm=normlegend, cmap=colormap,
                                   orientation="horizontal")

plt.show()

import matplotlib.pyplot as plt
import fastf1.plotting

fastf1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

# Load session data
session = fastf1.get_session(2021, 'Spanish Grand Prix', 'Q')
session.load()

# Select the fastest lap and get car telemetry
fastest_lap = session.laps.pick_fastest()
car_data = fastest_lap.get_car_data().add_distance()

# Load circuit info with corner locations
circuit_info = session.get_circuit_info()

# Create the plot
team_color = fastf1.plotting.get_team_color(fastest_lap['Team'],
                                            session=session)

fig, ax = plt.subplots()
ax.plot(car_data['Distance'], car_data['Speed'],
        color=team_color, label=fastest_lap['Driver'])

# Draw vertical lines at each corner
v_min = car_data['Speed'].min()
v_max = car_data['Speed'].max()
ax.vlines(x=circuit_info.corners['Distance'], ymin=v_min-20, ymax=v_max+20,
          linestyles='dotted', colors='grey')

# Plot corner numbers
for _, corner in circuit_info.corners.iterrows():
    txt = f"{corner['Number']}{corner['Letter']}"
    ax.text(corner['Distance'], v_min-30, txt,
            va='center_baseline', ha='center', size='small')

ax.set_xlabel('Distance in m')
ax.set_ylabel('Speed in km/h')
ax.legend()
ax.set_ylim([v_min - 40, v_max + 20])

plt.show()

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import colormaps
from matplotlib.collections import LineCollection
import fastf1

# Load session data
session = fastf1.get_session(2021, 'Austrian Grand Prix', 'Q')
session.load()

lap = session.laps.pick_fastest()
tel = lap.get_telemetry()

# Prepare data for plotting
x = np.array(tel['X'].values)
y = np.array(tel['Y'].values)

points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
gear = tel['nGear'].to_numpy().astype(float)

# Create line collection with segmented colormap
cmap = colormaps['Paired']
lc_comp = LineCollection(segments, norm=plt.Normalize(1, cmap.N+1), cmap=cmap)
lc_comp.set_array(gear)
lc_comp.set_linewidth(4)

# Create the plot
plt.gca().add_collection(lc_comp)
plt.axis('equal')
plt.tick_params(labelleft=False, left=False, labelbottom=False, bottom=False)

title = plt.suptitle(
    f"Fastest Lap Gear Shift Visualization\n"
    f"{lap['Driver']} - {session.event['EventName']} {session.event.year}"
)

# Add colorbar with gear numbers
cbar = plt.colorbar(mappable=lc_comp, label="Gear",
                    boundaries=np.arange(1, 10))
cbar.set_ticks(np.arange(1.5, 9.5))
cbar.set_ticklabels(np.arange(1, 9))

plt.show()