How to apply specific function in pandas dataframe

I have a specific function that works with 2d arrays. The function angle

calculates the angle between vectors.

When you call the function below, it is entered in "directions" as a parameter, which is a 2-dimensional array (with 2 columns, one with x vals and the other with y vals).

Now directions

obtained by using the np.diff()

function 2d array .

import matplotlib.pyplot as plt
import numpy as np
import os
import rdp

def angle(dir):
    """
    Returns the angles between vectors.

    Parameters:
    dir is a 2D-array of shape (N,M) representing N vectors in M-dimensional space.

    The return value is a 1D-array of values of shape (N-1,), with each value between 0 and pi.

    0 implies the vectors point in the same direction
    pi/2 implies the vectors are orthogonal
    pi implies the vectors point in opposite directions
    """
    dir2 = dir[1:]
    dir1 = dir[:-1]
    return np.arccos((dir1*dir2).sum(axis=1)/(np.sqrt((dir1**2).sum(axis=1)*(dir2**2).sum(axis=1))))

tolerance = 70
min_angle = np.pi*0.22

filename = os.path.expanduser('~/tmp/bla.data')
points = np.genfromtxt(filename).T
print(len(points))
x, y = points.T

# Use the Ramer-Douglas-Peucker algorithm to simplify the path
# http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
# Python implementation: https://github.com/sebleier/RDP/
simplified = np.array(rdp.rdp(points.tolist(), tolerance))

print(len(simplified))
sx, sy = simplified.T

# compute the direction vectors on the simplified curve
directions = np.diff(simplified, axis=0)
theta = angle(directions)

# Select the index of the points with the greatest theta
# Large theta is associated with greatest change in direction.
idx = np.where(theta>min_angle)[0]+1

      

        
        
        
      

    

I want to implement the above code pandas.DataFrame

with trajectory data.

Below is a sample df

. sx

, sy

values ​​belonging to the same subid

are counted as one trajectory, for example, line (0-3) has the same subid

as 2, and id

since 11 is counted as points on the trajectory. Lines (4-6) are one path, etc. Therefore, when the value of subid

or changes id

, individual track data is detected.

  id      subid     simplified_points     sx       sy
0 11      2         (3,4)                 3        4
1 11      2         (5,6)                 5        6
2 11      2         (7,8)                 7        8
3 11      2         (9,9)                 9        9
4 11      3         (10,12)               10       12
5 11      3         (12,14)               12       14
6 11      3         (13,15)               13       15
7 12      9         (18,20)               18       20
8 12      9         (22,24)               22       24
9 12      9         (25,27)               25       27

      

        
        
        
      

    

The above data frame was obtained after applying the rdp algorithm. simplified_points

is further decompressed into two columns sx

and sy

is the result of rdp algo.

The problem is getting directions

for each of these trajectories and then getting theta

and idx

. Since the above code was only implemented for one path and also for a 2d array, I can't seem to implement it for a pandas dataframe.

Please suggest me a way to implement the above code for each trajectory file in df.