I am trying to figure out how to iteratively append 2D arrays to generate a singular larger array. On each iteration a 16x200 ndarray is generated as seen below:
For each iteration a new 16x200 array is generated, I would like to 'append' this to the previously generated array for a total of N iterations. For example for two iterations the first generated array would be 16x200 and for the second iteration the newly generated 16x200 array would be appended to the first creating a 16x400 sized array.
train = np.array([])
for i in [1, 2, 1, 2]:
spike_count = [0, 0, 0, 0]
img = cv2.imread("images/" + str(i) + ".png", 0) # Read the associated image to be classified
k = np.array(temporallyEncode(img, 200, 4))
# Somehow append k to train on each iteration
In the case of the above embedded code the loop iterates 4 times so the final train array is expected to be 16x800 in size. Any help would be greatly appreciated, I have drawn a blank on how to successfully accomplish this. The code below is a general case:
import numpy as np
totalArray = np.array([])
for i in range(1,3):
arrayToAppend = totalArray = np.zeros((4, 200))
# Append arrayToAppend to totalArray somehow