Resample Or Normalize Trajectory Data So Points Are Evenly Spaced

I have a DataFrame which contains X & Y data for many trajectories (not GPS data). I am trying to figure out how to resample/time-normalize them so the distance between points

Solution 1:

Pandas has an interp function, but for processing like this I would prefer numpy/scipy. The vectorized functions are often faster than pandas. Example:

from scipy.interpolate import interp1d

x = np.logspace(0,2,300)
y = x**2
df = pd.DataFrame(np.array([x, y]).T, columns=list("xy"))

# define interpolation function:
f = interp1d(x, y)

# createnew df with desired x vals, generate y with interp function:
x_new = np.linspace(x.min(),x.max(),1000)
y_new = f(x_new)
df_new = pd.DataFrame(np.array([x_new, y_new]).T, columns=["x_new", "y_new"])

Note this will fail if x_new is outside the original domain - this makes sense as it's just linear interpolation.