> ## Documentation Index
> Fetch the complete documentation index at: https://tif1.tracinginsights.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Track Speed Map

> Visualize speed variations around the circuit

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.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/tracinginsightscom/assets/track_speed_map.png" alt="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.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# 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.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# 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.

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# 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

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
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](/tutorials/track-throttle-map) to see driver inputs
* Visualize [brake zones](/tutorials/track-brake-zones) to analyze braking points
* Compare [acceleration patterns](/tutorials/track-acceleration-map) around the circuit