Iterate over columns in 2 separate numpy arrays

Question

I currently have two 600 x 96 numpy arrays that I am trying to iterate over at the same time and apply a function to columns in the same index position in each array. When trying a nested for loop, the function returns 96^2 values instead of 96.

for i in range(SingleR.shape[1]):
    for j in range(ConAvgFull.shape[1]):
        lag_finder(SingleR[:,i], ConAvgFull[:,j], 500)

Here is what the function does.

from scipy import signal
import numpy as np

laglist = []
def lag_finder(y1, y2, sr):
    y1 = y1.reshape(600, 1)
    y2 = y2.reshape(600, 1)
    n = len(y2)
    corr = signal.correlate(y2, y1, mode='same') / np.sqrt(signal.correlate(y1, y1, 
    mode='same')[int(n/2)] * signal.correlate(y2, y2, mode='same')[int(n/2)])

    delay_arr = np.linspace(-0.5*n/sr, 0.5*n/sr, n)
    delay = delay_arr[np.argmax(corr)]
    if np.argmax(corr) > 0:
        laglist.append(delay)

How can I solve this?

Answer 1

Since you want to apply the function over the same column index, just use the same index:

for i in range(SingleR.shape[1]):
    # We're using i in both arrays, accessing at the same index
    lag_finder(SingleR[:,i], ConAvgFull[:,i], 500)