> ## 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. # Multi-Driver Speed Comparison > Visualize driver speed variations around the circuit with color-coded track maps Visualizing speed variations around a circuit helps identify braking zones, acceleration areas, and flat-out sections. This tutorial shows how to create a color-coded track map where speed is represented by color intensity along the racing line. Multi-driver speed comparison showing speed variations around the circuit ## Setup and configuration First, set up the plotting environment with FastF1 color scheme and configure the session details. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from matplotlib.collections import LineCollection import tif1 # Setup plotting with FastF1 colors tif1.plotting.setup_mpl(color_scheme='fastf1') # Configuration year = 2023 event = "Bahrain" session_type = "Q" driver = "VER" colormap = mpl.cm.plasma ``` ## Loading session and telemetry Load the session and extract telemetry data from the driver's fastest lap. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} # Load session session = tif1.get_session(year, event, session_type) weekend = session.event # Get fastest lap telemetry lap = session.laps.pick_driver(driver).pick_fastest() telemetry = lap.get_car_data() # Extract data for plotting x = telemetry['X'].values y = telemetry['Y'].values color = telemetry['Speed'].values ``` ## Creating line segments Convert the track coordinates into line segments for color mapping. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} # Create line segments for coloring points = np.array([x, y]).T.reshape(-1, 1, 2) segments = np.concatenate([points[:-1], points[1:]], axis=1) ``` ## Building the visualization Create the plot with a background track line and color-coded speed overlay. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} # Create visualization with FastF1 styling fig, ax = plt.subplots(sharex=True, sharey=True, figsize=(12, 6.75)) fig.suptitle(f'{weekend["EventName"]} {year} - {driver} - Speed', size=24, y=0.97, color='white') # Adjust margins and turn off axis plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.12) ax.axis('off') ax.set_aspect('equal', adjustable='datalim') # Create background track line ax.plot(x, y, color='black', linestyle='-', linewidth=16, zorder=0) # Create a continuous norm to map from data points to colors norm = plt.Normalize(color.min(), color.max()) lc = LineCollection(segments, cmap=colormap, norm=norm, linestyle='-', linewidth=5) # Set the values used for colormapping lc.set_array(color) # Add line segments to plot line = ax.add_collection(lc) ``` ## Adding the color bar Add a horizontal color bar to show the speed scale. ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} # Create color bar as legend cbaxes = fig.add_axes([0.25, 0.05, 0.5, 0.05]) normlegend = mpl.colors.Normalize(vmin=color.min(), vmax=color.max()) legend = mpl.colorbar.ColorbarBase(cbaxes, norm=normlegend, cmap=colormap, orientation="horizontal") legend.set_label('Speed (km/h)', color='white', fontsize=12) legend.ax.xaxis.set_tick_params(color='white') plt.setp(plt.getp(legend.ax.axes, 'xticklabels'), color='white') plt.show() ``` ## Complete example ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np from matplotlib.collections import LineCollection import tif1 # Setup plotting with FastF1 colors tif1.plotting.setup_mpl(color_scheme='fastf1') # Configuration year = 2023 event = "Bahrain" session_type = "Q" driver = "VER" colormap = mpl.cm.plasma # Load session session = tif1.get_session(year, event, session_type) weekend = session.event # Get fastest lap telemetry lap = session.laps.pick_driver(driver).pick_fastest() telemetry = lap.get_car_data() # Extract data for plotting x = telemetry['X'].values y = telemetry['Y'].values color = telemetry['Speed'].values # Create line segments for coloring points = np.array([x, y]).T.reshape(-1, 1, 2) segments = np.concatenate([points[:-1], points[1:]], axis=1) # Create visualization with FastF1 styling fig, ax = plt.subplots(sharex=True, sharey=True, figsize=(12, 6.75)) fig.suptitle(f'{weekend["EventName"]} {year} - {driver} - Speed', size=24, y=0.97, color='white') # Adjust margins and turn off axis plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.12) ax.axis('off') ax.set_aspect('equal', adjustable='datalim') # Create background track line ax.plot(x, y, color='black', linestyle='-', linewidth=16, zorder=0) # Create a continuous norm to map from data points to colors norm = plt.Normalize(color.min(), color.max()) lc = LineCollection(segments, cmap=colormap, norm=norm, linestyle='-', linewidth=5) # Set the values used for colormapping lc.set_array(color) # Add line segments to plot line = ax.add_collection(lc) # Create color bar as legend cbaxes = fig.add_axes([0.25, 0.05, 0.5, 0.05]) normlegend = mpl.colors.Normalize(vmin=color.min(), vmax=color.max()) legend = mpl.colorbar.ColorbarBase(cbaxes, norm=normlegend, cmap=colormap, orientation="horizontal") legend.set_label('Speed (km/h)', color='white', fontsize=12) legend.ax.xaxis.set_tick_params(color='white') plt.setp(plt.getp(legend.ax.axes, 'xticklabels'), color='white') plt.show() ``` ## Interpreting the visualization * **Red/yellow sections:** High-speed areas like straights and fast corners * **Blue/purple sections:** Low-speed areas indicating braking zones and slow corners * **Color transitions:** Show acceleration and deceleration patterns * **Track layout:** Black background line provides context for the circuit shape ## Comparing multiple drivers To compare multiple drivers, create separate plots or overlay them with different colormaps: ```python theme={"theme":{"light":"github-light","dark":"github-dark"}} drivers = ['VER', 'PER', 'LEC'] fig, axes = plt.subplots(1, len(drivers), figsize=(18, 6)) for idx, driver in enumerate(drivers): lap = session.laps.pick_driver(driver).pick_fastest() telemetry = lap.get_car_data() x = telemetry['X'].values y = telemetry['Y'].values speed = telemetry['Speed'].values points = np.array([x, y]).T.reshape(-1, 1, 2) segments = np.concatenate([points[:-1], points[1:]], axis=1) axes[idx].plot(x, y, color='black', linewidth=16, zorder=0) norm = plt.Normalize(speed.min(), speed.max()) lc = LineCollection(segments, cmap='plasma', norm=norm, linewidth=5) lc.set_array(speed) axes[idx].add_collection(lc) axes[idx].set_aspect('equal') axes[idx].axis('off') axes[idx].set_title(driver, color='white', fontsize=16) plt.tight_layout() plt.show() ``` ## Next steps * Analyze [lap time deltas](/tutorials/lap-delta-comparison) for detailed time differences * Compare [telemetry channels](/tutorials/telemetry-comparison) side by side * Visualize [gear shifts](/tutorials/gear-shifts-on-track) to see transmission strategies