Track Speed Map

Track speed maps provide an intuitive way to visualize how speed varies around a circuit. By color-coding the track based on speed, you can instantly identify fast straights, slow corners, and braking zones. Track speed map showing speed variations around the circuit

Loading the session

We’ll visualize the fastest lap from the 2023 Monaco Grand Prix qualifying session.

import tif1
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np

# Load qualifying session
session = tif1.get_session(2023, 'Monaco', 'Q')

# Get the fastest lap
fastest_lap = session.laps.pick_fastest()

Getting track position and telemetry

The telemetry includes X, Y coordinates for the car’s position and speed data.

# Get telemetry data
telemetry = fastest_lap.get_car_data()

# Extract position coordinates
x = telemetry['X']
y = telemetry['Y']
speed = telemetry['Speed']

Creating the visualization

We’ll use matplotlib’s LineCollection to create a color-coded track map.

# Prepare the track segments
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(figsize=(12, 10))

# Create line collection with speed-based colors
norm = plt.Normalize(speed.min(), speed.max())
lc = LineCollection(segments, cmap='plasma', norm=norm, linewidth=4)
lc.set_array(speed)

# Add to plot
line = ax.add_collection(lc)

# Add colorbar
cbar = plt.colorbar(line, ax=ax, label='Speed (km/h)')

# Format plot
ax.set_aspect('equal')
ax.axis('off')

plt.suptitle(f"{session.event['EventName']} {session.event.year} - Fastest Lap Speed Map")
plt.tight_layout()
plt.show()

Adding corner markers

You can enhance the visualization by adding corner numbers from the circuit info.

# Get circuit information
circuit_info = session.get_circuit_info()

# Add corner markers
for _, corner in circuit_info.corners.iterrows():
    ax.scatter(corner['X'], corner['Y'], color='white', s=100,
               edgecolors='black', linewidth=2, zorder=10)
    ax.text(corner['X'], corner['Y'], f"{corner['Number']}",
            ha='center', va='center', fontsize=8, fontweight='bold')

Complete example

import tif1
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np

# Load session
session = tif1.get_session(2023, 'Monaco', 'Q')
fastest_lap = session.laps.pick_fastest()

# Get telemetry
telemetry = fastest_lap.get_car_data()
x = telemetry['X']
y = telemetry['Y']
speed = telemetry['Speed']

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

# Create plot
fig, ax = plt.subplots(figsize=(12, 10))

# Color-coded track
norm = plt.Normalize(speed.min(), speed.max())
lc = LineCollection(segments, cmap='plasma', norm=norm, linewidth=4)
lc.set_array(speed)
line = ax.add_collection(lc)

# Colorbar
cbar = plt.colorbar(line, ax=ax, label='Speed (km/h)')

# Format
ax.set_aspect('equal')
ax.axis('off')
plt.suptitle(f"{session.event['EventName']} {session.event.year} - Speed Map")
plt.tight_layout()
plt.show()

Interpreting the visualization

Hot colors (red/yellow): High-speed sections like straights
Cool colors (blue/purple): Low-speed corners and braking zones
Gradients: Show acceleration and deceleration patterns

Next steps

Create throttle maps to see driver inputs
Visualize brake zones to analyze braking points
Compare acceleration patterns around the circuit

​Loading the session

​Getting track position and telemetry

​Creating the visualization

​Adding corner markers

​Complete example

​Interpreting the visualization

​Next steps