Numpy matrix binarization using only one expression

Question

I am looking for a way to binarize numpy N-d array based on the threshold using only one expression. So I have something like this:

np.random.seed(0) np.set_printoptions(precision=3) a = np.random.rand(4, 4) threshold, upper, lower = 0.5, 1, 0 

a is now:

array([[ 0.02 ,  0.833,  0.778,  0.87 ],        [ 0.979,  0.799,  0.461,  0.781],        [ 0.118,  0.64 ,  0.143,  0.945],        [ 0.522,  0.415,  0.265,  0.774]]) 

Now I can fire these 2 expressions:

a[a>threshold] = upper a[a<=threshold] = lower 

and achieve what I want:

array([[ 0.,  1.,  1.,  1.],        [ 1.,  1.,  0.,  1.],        [ 0.,  1.,  0.,  1.],        [ 1.,  0.,  0.,  1.]]) 

But is there a way to do this with just one expression?

Answer 1

We may consider np.where:

np.where(a>threshold, upper, lower) Out[6]:  array([[0, 1, 1, 1],        [1, 1, 0, 1],        [0, 1, 0, 1],        [1, 0, 0, 1]])