> ## 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. # Annotated Speed Trace > Visualizing speed traces with corner markers and annotations Understanding where speed changes occur on a circuit is crucial for lap analysis. By annotating a speed trace with corner locations, you can quickly identify braking zones, apex speeds, and acceleration patterns at specific corners. Speed trace with corner annotations ## Loading the session and data We'll analyze the fastest qualifying lap from the 2021 Spanish Grand Prix and overlay corner markers. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} import matplotlib.pyplot as plt import tif1 # Enable plotting support tif1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1') # Load qualifying session session = tif1.get_session(2021, 'Spanish Grand Prix', 'Q') ``` ## Getting the fastest lap telemetry Select the fastest lap and retrieve its telemetry data with distance information. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} fastest_lap = session.laps.pick_fastest() car_data = fastest_lap.get_car_data().add_distance() ``` ## Loading circuit information The circuit info contains corner locations, numbers, and other track-specific data. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} circuit_info = session.get_circuit_info() ``` The `circuit_info.corners` DataFrame includes: * `Number`: Corner number * `Letter`: Corner letter (for chicanes or complex sections) * `Distance`: Distance from start line in meters * `Angle`: Corner angle * `X`, `Y`: Track coordinates ## Creating the annotated plot Plot the speed trace and add vertical lines at each corner location. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} # Get team color for the driver team_color = tif1.plotting.get_team_color(fastest_lap['Team'], session=session) # Create figure fig, ax = plt.subplots(figsize=(12, 6)) ax.plot(car_data['Distance'], car_data['Speed'], color=team_color, label=fastest_lap['Driver'], linewidth=2) # Draw vertical dotted lines at each corner v_min = car_data['Speed'].min() v_max = car_data['Speed'].max() ax.vlines(x=circuit_info.corners['Distance'], ymin=v_min-20, ymax=v_max+20, linestyles='dotted', colors='grey') # Add corner numbers below the lines for _, corner in circuit_info.corners.iterrows(): txt = f"{corner['Number']}{corner['Letter']}" ax.text(corner['Distance'], v_min-30, txt, va='center_baseline', ha='center', size='small') ax.set_xlabel('Distance (m)') ax.set_ylabel('Speed (km/h)') ax.legend() # Adjust y-axis to include corner numbers ax.set_ylim([v_min - 40, v_max + 20]) plt.suptitle(f"Speed Trace with Corner Annotations\n" f"{session.event['EventName']} {session.event.year} Qualifying") plt.show() ``` ## Understanding the visualization The annotated speed trace reveals: * **Corner entry speeds:** Where the speed drops before each corner marker * **Minimum corner speeds:** The lowest point near each corner number * **Corner exit acceleration:** How quickly speed increases after each corner * **Straight-line performance:** Speed plateaus between corners For example, Turn 1 at Barcelona typically shows a sharp speed drop from \~310 km/h to \~150 km/h, while the high-speed Turn 9 maintains speeds above 250 km/h. ## Comparing multiple drivers You can extend this to compare multiple drivers by plotting additional speed traces: ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} # Get laps from two drivers lap1 = session.laps.pick_drivers('VER').pick_fastest() lap2 = session.laps.pick_drivers('HAM').pick_fastest() tel1 = lap1.get_car_data().add_distance() tel2 = lap2.get_car_data().add_distance() # Plot both with corner annotations fig, ax = plt.subplots(figsize=(12, 6)) color1 = tif1.plotting.get_team_color(lap1['Team'], session=session) color2 = tif1.plotting.get_team_color(lap2['Team'], session=session) ax.plot(tel1['Distance'], tel1['Speed'], color=color1, label=lap1['Driver'], linewidth=2) ax.plot(tel2['Distance'], tel2['Speed'], color=color2, label=lap2['Driver'], linewidth=2) # Add corner markers v_min = min(tel1['Speed'].min(), tel2['Speed'].min()) v_max = max(tel1['Speed'].max(), tel2['Speed'].max()) ax.vlines(x=circuit_info.corners['Distance'], ymin=v_min-20, ymax=v_max+20, linestyles='dotted', colors='grey', alpha=0.5) for _, corner in circuit_info.corners.iterrows(): txt = f"{corner['Number']}{corner['Letter']}" ax.text(corner['Distance'], v_min-30, txt, va='center_baseline', ha='center', size='small') ax.set_xlabel('Distance (m)') ax.set_ylabel('Speed (km/h)') ax.legend() ax.set_ylim([v_min - 40, v_max + 20]) plt.show() ``` ## Complete example ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} import matplotlib.pyplot as plt import tif1 # Setup tif1.plotting.setup_mpl(mpl_timedelta_support=True, color_scheme='fastf1') # Load session session = tif1.get_session(2021, 'Spanish Grand Prix', 'Q') # Get fastest lap and telemetry fastest_lap = session.laps.pick_fastest() car_data = fastest_lap.get_car_data().add_distance() # Get circuit info circuit_info = session.get_circuit_info() # Get team color team_color = tif1.plotting.get_team_color(fastest_lap['Team'], session=session) # Create plot fig, ax = plt.subplots(figsize=(12, 6)) ax.plot(car_data['Distance'], car_data['Speed'], color=team_color, label=fastest_lap['Driver'], linewidth=2) # Add corner annotations v_min = car_data['Speed'].min() v_max = car_data['Speed'].max() ax.vlines(x=circuit_info.corners['Distance'], ymin=v_min-20, ymax=v_max+20, linestyles='dotted', colors='grey') for _, corner in circuit_info.corners.iterrows(): txt = f"{corner['Number']}{corner['Letter']}" ax.text(corner['Distance'], v_min-30, txt, va='center_baseline', ha='center', size='small') ax.set_xlabel('Distance (m)') ax.set_ylabel('Speed (km/h)') ax.legend() ax.set_ylim([v_min - 40, v_max + 20]) plt.suptitle(f"Speed Trace with Corner Annotations\n" f"{session.event['EventName']} {session.event.year} Qualifying") plt.show() ``` ## Next steps * Explore [Speed Traces Comparison](/tutorials/speed-traces) for multi-driver analysis * Learn about [Gear Shifts on Track](/tutorials/gear-shifts-on-track) for transmission analysis * Dive into [Telemetry Comparison](/tutorials/telemetry-comparison) for detailed channel analysis