pip install gpxpy geopy numpy scipy matplotlib pandas Here’s a simplified script skeleton that detects corner entries based on yaw rate (GPS-derived heading change):
Lap 10: 1:48.22 Sector times: - S1 (0–850m): 32.10s - S2 (850–1850m): 34.05s <<< anomaly: +0.5s vs best - S3 (1850–3024m): 42.07s Auto-race-inf detection flags that meter 1,850 is the entry to a fast right-left chicane. The script pulls throttle position data and reveals you’re lifting 20 meters early every lap at that exact spot. moto trackday project script auto race inf m verified
# Heading change rate (yaw rate proxy) yaw_rate = np.abs(np.diff(headings)) peaks, _ = find_peaks(yaw_rate, height=15) # >15 deg change = corner pip install gpxpy geopy numpy scipy matplotlib pandas
Within one season, you’ll stop riding by feel alone. You’ll ride by – and drop seconds off your lap time. Have you built a trackday script? Share your GitHub or RaceStudio template in the comments. Let’s verify every meter, together. You’ll ride by – and drop seconds off your lap time
# Extract points and heading headings = [] for pt in gpx.tracks[0].segments[0].points: headings.append(pt.course) # degrees