corner_meters = [] for peak in peaks: cumulative_dist = 0 for i, pt in enumerate(gpx.tracks[0].segments[0].points): if i <= peak: cumulative_dist += pt.distance_2d(prev_pt) prev_pt = pt corner_meters.append(round(cumulative_dist, 1))
This keyword appears to target a niche motorsport audience—likely motorcycle track day enthusiasts, data nerds, and those building automated (scripted) systems for logging race infrastructure (inf) data with a need for verified "m" (meter/mile) metrics. Introduction: When Passion Meets Data Every motorcyclist who has tipped into Turn 1 at 120 mph knows the feeling: the mix of fear, focus, and freedom. But for the modern track day rider or club racer, that feeling is no longer enough. We want proof . We want precision . We want verification . moto trackday project script auto race inf m verified
Once your script detects this rastructure, you can auto-split lap times into sectors without manual timing gates. Part 3: The "M Verified" Standard – Why Meters Matter GPS errors of 2–5 meters are common. Over a lap, that means your "lap length" might vary by 10 meters – enough to make time comparisons useless. corner_meters = [] for peak in peaks: cumulative_dist
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. We want proof
import gpxpy import numpy as np from scipy.signal import find_peaks def detect_corners(gpx_file): with open(gpx_file, 'r') as f: gpx = gpxpy.parse(f)
print(f"Auto-detected len(corner_meters) corners at meters: corner_meters") return corner_meters detect_corners("my_lap.gpx") To verify distance, compare GPS against wheel speed sensor (WSS) pulses:
