Driver Lap Times Visualization

import tif1
import seaborn as sns
import matplotlib.pyplot as plt

# Enable Matplotlib patches for plotting timedelta values and load
# tif1's dark color scheme
tif1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

# Load the race session
race = tif1.get_session(2023, "Azerbaijan", 'R')
laps = race.laps

# Get all laps for a single driver
# Filter out slow laps as they distort the graph axis
driver_laps = laps[laps["Driver"] == "ALO"].copy()

# Remove slow laps (pit laps, safety car, etc.)
# Keep only laps within 107% of the driver's fastest lap
fastest_lap = driver_laps["LapTime"].min()
driver_laps = driver_laps[driver_laps["LapTime"] < fastest_lap * 1.07]

# Remove deleted/invalid laps
driver_laps = driver_laps[~driver_laps["Deleted"]]

# Reset index for cleaner plotting
driver_laps = driver_laps.reset_index(drop=True)

fig, ax = plt.subplots(figsize=(10, 8))

# Create scatter plot with compound-based coloring
sns.scatterplot(
    data=driver_laps,
    x="LapNumber",
    y="LapTime",
    ax=ax,
    hue="Compound",
    palette=tif1.plotting.get_compound_mapping(session=race),
    s=80,
    linewidth=0,
    legend='auto'
)

# Set axis labels
ax.set_xlabel("Lap Number", fontsize=12)
ax.set_ylabel("Lap Time", fontsize=12)

# The y-axis increases from bottom to top by default
# Since we are plotting time, it makes sense to invert the axis
ax.invert_yaxis()

# Add title
plt.suptitle("Alonso Lap Times in the 2023 Azerbaijan Grand Prix",
             fontsize=14, fontweight='bold')

# Turn on major grid lines
plt.grid(color='w', which='major', axis='both', alpha=0.3)

# Remove spines for cleaner look
sns.despine(left=True, bottom=True)

plt.tight_layout()
plt.show()

import tif1

# Single-driver view (the default filters out deleted laps)
fig, ax = tif1.plot_driver_laptimes(2023, "Azerbaijan", 'R', drivers=["ALO"])
plt.show()

# Keep deleted laps and relax the outlier cutoff
fig, ax = tif1.plot_driver_laptimes(
    2023, "Azerbaijan", 'R',
    drivers=["ALO"],
    include_deleted=True,
    laptime_cutoff=1.10,
)
plt.show()

import tif1
import matplotlib.pyplot as plt

# One call: loads the race, filters the laps, and plots them by compound
fig, ax = tif1.plot_driver_laptimes(2023, "Azerbaijan", 'R', drivers=["ALO"])
plt.show()

# Or save straight to a file
# tif1.plot_driver_laptimes(2023, "Azerbaijan", 'R', drivers=["ALO"], save_path="driver_laptimes_example.png")

import tif1

# Top-3 finishers by default
fig, ax = tif1.plot_driver_laptimes(2023, "Azerbaijan", 'R')
plt.show()

# Or choose your own set
fig, ax = tif1.plot_driver_laptimes(2023, "Azerbaijan", 'R', drivers=["ALO", "VER", "PER"])
plt.show()

# Use custom colors
custom_palette = {
    "SOFT": "#FF0000",
    "MEDIUM": "#FFA500",
    "HARD": "#FFFFFF",
    "INTERMEDIATE": "#00FF00",
    "WET": "#0000FF"
}

# Adjust marker size and style
sns.scatterplot(
    data=driver_laps,
    x="LapNumber",
    y="LapTime",
    hue="Compound",
    palette=custom_palette,
    s=100,  # Larger markers
    marker='D',  # Diamond markers
    alpha=0.7,  # Transparency
    edgecolor='black',  # Marker outline
    linewidth=0.5
)