> ## 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 Throttle Map

> Visualize throttle application around the circuit

Throttle maps show where and how much throttle drivers apply around the circuit. This visualization reveals driving style, traction zones, and areas where drivers can push the car to its limits.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/tracinginsightscom/assets/track_throttle_map.png" alt="Track throttle map showing throttle application percentage" />

## Loading the 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 session
session = tif1.get_session(2023, 'Silverstone', 'Q')
fastest_lap = session.laps.pick_fastest()
```

## Getting throttle data

Throttle values range from 0 (no throttle) to 100 (full throttle).

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# Get telemetry
telemetry = fastest_lap.get_car_data()

# Extract position and throttle
x = telemetry['X']
y = telemetry['Y']
throttle = telemetry['Throttle']
```

## Creating the throttle map

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# 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 by throttle
norm = plt.Normalize(0, 100)
lc = LineCollection(segments, cmap='RdYlGn', norm=norm, linewidth=4)
lc.set_array(throttle)
line = ax.add_collection(lc)

# Colorbar
cbar = plt.colorbar(line, ax=ax, label='Throttle (%)')

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

## 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, 'Silverstone', 'Q')
fastest_lap = session.laps.pick_fastest()

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

# 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(0, 100)
lc = LineCollection(segments, cmap='RdYlGn', norm=norm, linewidth=4)
lc.set_array(throttle)
line = ax.add_collection(lc)

# Colorbar
cbar = plt.colorbar(line, ax=ax, label='Throttle (%)')

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

## Analyzing throttle patterns

* **Green sections:** Full throttle on straights
* **Yellow/orange:** Partial throttle through fast corners
* **Red sections:** Minimal or no throttle in slow corners
* **Gradients:** Show how smoothly drivers apply throttle

High-speed corners with partial throttle indicate where drivers balance speed with traction limits.

## Next steps

* Compare with [brake zone maps](/tutorials/track-brake-zones)
* Analyze [speed variations](/tutorials/track-speed-map)
* Study [gear shift patterns](/tutorials/gear-shifts-on-track)