>>> import pygmt
>>> points = pygmt.datasets.load_sample_data(name="ocean_ridge_points")
>>> for inc in ["30m", "10m", "06m"]:
...     %timeit df = pygmt.grdtrack(points=points, grid=f"@earth_relief_{inc}_g", newcolname="bathymetry")
...

20.5 ms ± 154 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
57.6 ms ± 227 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
139 ms ± 268 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

14.1 ms ± 3.08 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
49.1 ms ± 174 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
130 ms ± 161 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)