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
import seaborn as sns
import matplotlib.pyplot as plt

# Setup plotting
tif1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1')

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

# Get driver laps and filter
driver_laps = laps[laps["Driver"] == "ALO"].copy()
fastest_lap = driver_laps["LapTime"].min()
driver_laps = driver_laps[driver_laps["LapTime"] < fastest_lap * 1.07]
driver_laps = driver_laps[~driver_laps["Deleted"]].reset_index(drop=True)

# Create scatter plot
fig, ax = plt.subplots(figsize=(10, 8))

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'
)

# Enhance plot
ax.set_xlabel("Lap Number", fontsize=12)
ax.set_ylabel("Lap Time", fontsize=12)
ax.invert_yaxis()
plt.suptitle("Alonso Lap Times in the 2023 Azerbaijan Grand Prix",
             fontsize=14, fontweight='bold')
plt.grid(color='w', which='major', axis='both', alpha=0.3)
sns.despine(left=True, bottom=True)

plt.tight_layout()
plt.show()

# Compare top 3 finishers
final_lap = laps[laps["LapNumber"] == laps["LapNumber"].max()]
top_3_drivers = final_lap.sort_values("Position").head(3)["Driver"].tolist()

fig, axes = plt.subplots(1, 3, figsize=(18, 6), sharey=True)

for idx, driver in enumerate(top_3_drivers):
    ax = axes[idx]

    # Filter driver laps
    driver_laps = laps[laps["Driver"] == driver].copy()
    fastest_lap = driver_laps["LapTime"].min()
    driver_laps = driver_laps[driver_laps["LapTime"] < fastest_lap * 1.07]
    driver_laps = driver_laps[~driver_laps["Deleted"]]

    # Plot
    sns.scatterplot(
        data=driver_laps,
        x="LapNumber",
        y="LapTime",
        ax=ax,
        hue="Compound",
        palette=tif1.plotting.get_compound_mapping(session=race),
        s=60,
        linewidth=0,
        legend=(idx == 2)  # Only show legend on last plot
    )

    ax.set_xlabel("Lap Number")
    ax.set_ylabel("Lap Time" if idx == 0 else "")
    ax.set_title(f"{driver}", fontweight='bold')
    ax.invert_yaxis()
    ax.grid(color='w', which='major', axis='both', alpha=0.3)
    sns.despine(left=True, bottom=True, ax=ax)

plt.suptitle("Top 3 Finishers - Lap Time Comparison",
             fontsize=16, fontweight='bold', y=1.02)
plt.tight_layout()
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
)