> ## 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 Brake Zones

> Identify braking zones around the circuit

Brake zone visualization highlights where drivers apply the brakes around the circuit. This is crucial for understanding corner entry, comparing braking points, and analyzing overtaking opportunities.

<img src="https://mintlify.s3.us-west-1.amazonaws.com/tracinginsightscom/assets/track_brake_zones.png" alt="Track brake zones showing where drivers brake" />

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

## Getting brake data

Brake data is binary: 0 (not braking) or 1 (braking).

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

# Extract position and brake
x = telemetry['X']
y = telemetry['Y']
brake = telemetry['Brake']
```

## Creating the brake zone 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 brake (binary)
norm = plt.Normalize(0, 1)
lc = LineCollection(segments, cmap='RdYlGn_r', norm=norm, linewidth=4)
lc.set_array(brake)
line = ax.add_collection(lc)

# Colorbar
cbar = plt.colorbar(line, ax=ax, label='Brake', ticks=[0, 1])
cbar.ax.set_yticklabels(['Off', 'On'])

# Format
ax.set_aspect('equal')
ax.axis('off')
plt.suptitle(f"{session.event['EventName']} {session.event.year} - Brake Zones")
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, 'Monza', 'Q')
fastest_lap = session.laps.pick_fastest()

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

# 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, 1)
lc = LineCollection(segments, cmap='RdYlGn_r', norm=norm, linewidth=4)
lc.set_array(brake)
line = ax.add_collection(lc)

# Colorbar
cbar = plt.colorbar(line, ax=ax, label='Brake', ticks=[0, 1])
cbar.ax.set_yticklabels(['Off', 'On'])

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

## Analyzing brake zones

* **Red sections:** Active braking zones before corners
* **Green sections:** No braking (straights and corners)
* **Length of red zones:** Indicates braking duration and intensity

Circuits like Monza have fewer but more intense braking zones, while Monaco has frequent braking throughout the lap.

## Comparing drivers

To compare braking points between drivers:

```python theme={"theme":{"light":"github-light","dark":"github-dark"}}
# Get two laps
lap1 = session.laps.pick_drivers('VER').pick_fastest()
lap2 = session.laps.pick_drivers('HAM').pick_fastest()

# Plot both on same figure
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(20, 10))

# Plot lap 1 on ax1, lap 2 on ax2
# (use same code as above for each)
```

## Next steps

* Combine with [speed maps](/tutorials/track-speed-map) to see braking effectiveness
* Analyze [throttle application](/tutorials/track-throttle-map) after braking
* Study [longitudinal acceleration](/tutorials/track-acceleration-map) during braking