Gear shifts on track

Plot which gear is being used at which point of the track

Import FastF1 and load the data

import fastf1

import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from matplotlib import cm
import numpy as np


fastf1.Cache.enable_cache('../doc_cache')  # replace with your cache directory

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

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

Prepare the data for plotting by converting it to the appropriate numpy data types

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 a line collection. Set a segmented colormap and normalize the plot to full integer values of the colormap

cmap = cm.get_cmap('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 a colorbar to the plot. Shift the colorbar ticks by +0.5 so that they are centered for each color segment.

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()
Fastest Lap Gear Shift Visualization VER - Austrian Grand Prix 2021

Total running time of the script: ( 0 minutes 3.609 seconds)

Gallery generated by Sphinx-Gallery