I want to vectorize the process of adding a 2d array to every 2d array inside a 3d array.
I imported an image file using image from matplotlib
data = image.imread('test.jpg')
Then I tried to add the average of each RGB array to another array of the same shape as data
data2 = np.zeros_like(data)
data3 = np.average(data, axis=2)
for i in range(len(data2[0,0,:])):
data2[:,:,i] = data3
I just want to vectorize the above 2 line code to one line
Convert data3 to the result datatype and then broadcast/repeat after extending to 3D with np.newaxis/None -
b = data3.astype(data.dtype)
data2_out = np.broadcast_to(b[...,None], data.shape)
The output would simply be a view into b and hence we are gaining memory-efficiency there.
If you need an output with its own memory space, we can force the copy with data2_out.copy() or use np.repeat, like so -
np.repeat(b[...,None],data.shape[2],axis=2)
If you already have the output array data2 initialized and just want to assign into it, we can do so with extending data3 to 3D and this might more intuitive in some scenarios too, like so -
data2[:] = data3[...,None]
