I have this working code tested over lots of np.arrays. It is a for-loop that walks through all the values of a np.array (x,y).
For each y-row, it finds the first x-column for which the value is different to zero. Afterward, it finds the last x-column for which value is different to 0.
Then all the columns between first x-column and last x-column are centred.
This is then repeated for all y-rows. Example:
#Input : array([[ 0.0, 0.149, 0.064, 0.736, 0.0], [ 0.0, 0.0, 0.258, 0.979, 0.618 ], [ 0.0, 0.0, 0.0, 0.786, 0.666], [ 0.0, 0.0, 0.0, 0.782, 0.954], #Output : array([[ 0.0, 0.149, 0.064, 0.736, 0.0], [ 0.0, 0.258, 0.979, 0.618, 0.0], [ 0.0, 0.786, 0.666, 0.0, 0.0], [ 0.0, 0.782, 0.954, 0.0, 0.0],
Not all the values between first and last columns are different than zero.
for y in range(len(array)): begin = False inside = False end = False for x in range(len(array)): if (array[y][x] == 0) & (begin == True) & (end == False): boundary_two = ( x - 1 ) inside = False end = True elif (array[y][x] != 0) & (inside == False): boundary_one = x begin = True inside = True y_position.append(y) m = np.split(array[y],[boundary_one,boundary_two]) zeros = len(array)-len(m) array[y] = np.concatenate((np.zeros(zeros//2),m,np.zeros(int(np.ceil(zeros/2)))))
Furthermore, I added a variable(count) inside the function (which I erase for the upper code example, to simplify lecture) which count how many empty rows since last non empty rows. When count ==10, we break out of the loop. This is to save time. Once we have +/- 10 empty rows after the non-empty ones, it is sure all other y-rows will be empty as well.
Finally, I must save the value for the last y-row non-empty.
This is my script most time demanding calculation, so I was wondering if there is a way of to improve it, either by making it clearer and/or faster.
Thanks a lot, hope it is clear!