Say I have N numpy arrays, each of size (x, y, z), where y and z are the same for all but x differs for each. How would I combine these to a numpy array of size (w, y, z) where w is the sum of all x.
Or, for a numerical example: I have a list of 3 numpy array with sizes (14, 32, 32), (7, 32, 32), (50, 32, 32). How do I turn these into a (71, 32, 32) sized numpy array efficiently?
You can just concatenate them along the first axis. If your 3 numpy arrays are named x1, x2, and x3, your new array would be defined as x_combined = np.concatenate((x1,x2,x3),axis=0)
Try np.vstack
a, b , c = np.ones((14, 32, 32)), np.ones((7, 32, 32)), np.ones((50, 32, 32))
out = np.vstack([a,b,c])
In [119]: a.shape
Out[119]: (14, 32, 32)
In [120]: b.shape
Out[120]: (7, 32, 32)
In [121]: c.shape
Out[121]: (50, 32, 32)
In [122]: out.shape
Out[122]: (71, 32, 32)
shape command as in my answer. That error indicates one of your array doesn't match on both w, z dimension.