Given a numpy 2D array of points, aka 3D array with size of the 3rd dimension equals to 2, how do I get the minimum x and y coordinate over all points?

Examples:

First:

I edited my original example, since it was wrong.

data = np.array([[[ 0,  1],[ 2,  3],[ 4,  5]],[[11, 12],[13, 14],[15, 16]]])minx = 0 # data[0][0][0]
miny = 1 # data[0][0][1]

4 x 4 x 2:

Second:

array([[[ 0, 77],[29, 12],[28, 71],[46, 17]],[[45, 76],[33, 82],[14, 17],[ 3, 18]],[[99, 40],[96,  3],[74, 60],[ 4, 57]],[[67, 57],[23, 81],[12, 12],[45, 98]]])minx = 0 # data[0][0][0]
miny = 3 # data[2][1][1]

Is there an easy way to get now the minimum x and y coordinates of all points of the data? I played around with amin and different axis values, but nothing worked.

Clarification:

My array stores positions from different robots over time. First dimension is time, second is the index of an robot. The third dimension is then either x or y of a robots for a given time.

Since I want to draw their paths to pixels, I need to normalize my data, so that the points are as close as possible to the origin without getting negative. I thought that subtracting [minx,miny] from every point will do that for me.

alko's answer didn't work for me, so here's what I did:

import numpy as nparray = np.arange(15).reshape(5,3)
x,y = np.unravel_index(np.argmin(array),array.shape)