Qualifying Results Visualization

import tif1
import matplotlib.pyplot as plt
import pandas as pd

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

# Load qualifying session
session = tif1.get_session(2023, 'Spanish Grand Prix', 'Q')
laps = session.laps

# Get all unique drivers
drivers = pd.unique(laps['Driver'])

# Get fastest lap for each driver
list_fastest_laps = []
for drv in drivers:
    drv_laps = laps[laps['Driver'] == drv]
    if len(drv_laps) > 0:
        fastest_idx = drv_laps['LapTime'].idxmin()
        list_fastest_laps.append(drv_laps.loc[fastest_idx])

# Create DataFrame and sort by lap time
fastest_laps = pd.DataFrame(list_fastest_laps) \
    .sort_values(by='LapTime') \
    .reset_index(drop=True)

# Calculate time delta from pole
pole_lap_time = fastest_laps['LapTime'].iloc[0]
fastest_laps['LapTimeDelta'] = fastest_laps['LapTime'] - pole_lap_time

# Preview the results
print(fastest_laps[['Driver', 'LapTime', 'LapTimeDelta']].head(10))

# Get team colors for each driver
team_colors = []
for _, lap in fastest_laps.iterrows():
    color = tif1.plotting.get_team_color(lap['Team'], session=session)
    team_colors.append(color)

# Create the plot
fig, ax = plt.subplots(figsize=(10, 8))
ax.barh(fastest_laps.index, fastest_laps['LapTimeDelta'],
        color=team_colors, edgecolor='grey', linewidth=0.5)

# Set driver labels
ax.set_yticks(fastest_laps.index)
ax.set_yticklabels(fastest_laps['Driver'])

# Show fastest at the top
ax.invert_yaxis()

# Add grid lines
ax.set_axisbelow(True)
ax.xaxis.grid(True, which='major', linestyle='--',
              color='white', alpha=0.3, zorder=-1000)

# Labels
ax.set_xlabel('Gap to Pole (seconds)', fontsize=11)
ax.set_ylabel('Driver', fontsize=11)

# Format title
pole_driver = fastest_laps['Driver'].iloc[0]
pole_time_str = str(pole_lap_time).split()[-1]

plt.suptitle(f"2023 Spanish Grand Prix Qualifying Results\n"
             f"Pole Position: {pole_time_str} ({pole_driver})",
             fontsize=13, fontweight='bold')

plt.tight_layout()
plt.show()

import tif1

# Top 10 only, deleted laps excluded (defaults)
fig, ax = tif1.plot_qualifying_grid(2023, 'Spanish Grand Prix', 'Q', n_drivers=10)
plt.show()

# Full grid
fig, ax = tif1.plot_qualifying_grid(2023, 'Spanish Grand Prix', 'Q')
plt.show()

import tif1
import matplotlib.pyplot as plt

# One call: loads qualifying, finds each driver's best lap, and plots gaps to pole
fig, ax = tif1.plot_qualifying_grid(2023, 'Spanish Grand Prix', 'Q')
plt.show()

# Or save straight to a file
# tif1.plot_qualifying_grid(2023, 'Spanish Grand Prix', 'Q', save_path="qualifying_grid.png")

import tif1

fig, ax = tif1.plot_qualifying_grid(2023, 'Spanish Grand Prix', 'Q')

# Add value labels on bars
for patch, label in zip(ax.patches, ax.get_yticklabels()):
    width = patch.get_width()
    if width > 0:
        ax.text(width, patch.get_y() + patch.get_height() / 2,
                f"+{width:.3f}s", va='center', ha='left', fontsize=9)
plt.show()

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))

# Load two different qualifying sessions
session1 = tif1.get_session(2023, 'Spanish Grand Prix', 'Q')
session2 = tif1.get_session(2023, 'Monaco Grand Prix', 'Q')

# Plot both (reuse the plotting code for each axis)
# ... plotting code for ax1 and ax2 ...

plt.tight_layout()
plt.show()

# Save as high-resolution PNG
plt.savefig('qualifying_results.png', dpi=300, bbox_inches='tight',
            facecolor='#1a1a1a')

# Save as vector format for publications
plt.savefig('qualifying_results.svg', bbox_inches='tight')