I have a set of 2D arrays that I wish to add up but they are of different lengths. I want it to add up such that they are centered on top of each other. For example for 1D:
a = [1,1,1,1,1]
b = [2,2,2]
therefore: c = [1,3,3,3,1]
I'm not sure how to approach this using numpy?
Assuming that for your add up operation 0 is neutral element, for centering part you might harness numpy.pad in constant mode, as long as all dimension are odd. Consider following example, lets say we have (3,5)-shaped array and we want to obtain (11,9)-shaped array then we might do:
import numpy as np
a = np.ones((3,5),dtype=int)
a_h, a_w = a.shape
required_h = 11
required_w = 9
vert_pad_size = (required_h - a_h)//2
hor_pad_size = (required_w - a_w)//2
centered = np.pad(a, [(vert_pad_size,vert_pad_size),(hor_pad_size,hor_pad_size)], mode='constant')
print(centered)
Output:
[[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 1 1 1 1 1 0 0]
[0 0 1 1 1 1 1 0 0]
[0 0 1 1 1 1 1 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0]]
Explanation: I first calculate how many rows/columns must be added to up, bottom,left,right to get desired shape and then do it using pad. Keep in mind that this solution assumes all dimension of original array and desired array are odd.
In general, you could assign slices:
>>> import numpy as np
>>> A = np.ones((5,5))
>>> B = np.zeros((3,3))
>>> A
array([[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1.]])
>>> B
array([[0., 0., 0.],
[0., 0., 0.],
[0., 0., 0.]])
>>> A[1:-1, 1:-1] = B
>>> A
array([[1., 1., 1., 1., 1.],
[1., 0., 0., 0., 1.],
[1., 0., 0., 0., 1.],
[1., 0., 0., 0., 1.],
[1., 1., 1., 1., 1.]])
Calculating the indices for the slice is left as an exercise.
[1, 3, 3, 3, 1]if you're actually adding them. Can you clarify?