def Gaussian_elimination(A):

rank = 0

m,n = np.shape(A)

i =0

for j in range(n):

p = np.argmax(abs(A[i:m,j]))

if p > 0 :

A[[i,p+i]] = A[[p+i,i]]

if A[i,j] != 0:

rank += 1

for r in range(i+1, m):

A[r,j:] -= (A[r,j]/A[i,j]*A[i,j:])

i += 1

if i > m:

break

return A, rank

A = np.array([[1,2.01],[2.01,4.0401]])

U,s,V = np.linalg.svd(A)

#np.shape((abs(s)>tol).nonzero())[1]

A, rank = Gaussian_elimination(A)

rank