Victor, The `kwant.discretize` is devoted to discrete k.p Hamiltonians. In your case, I'd recommend to write the tight-binding version of that Hamiltonian instead. I don't understand what you mean by "finding the hopping terms". You should set that, and not find it. Best, Antonio On Sat, 6 Feb 2021, 12:16 Victor Regis, wrote:

Hi all,

I need to use the following hamiltonian to test if the rest of my code is correct, however I haven't been able to implement it in Kwant. Reading the examples, all the discretize ones are for polynomial dependences on k but mine isn't.

H = sin(k_x)*sigma_x +sin(k_y)*sigma_y +B*(2+M-cos(k_x)-cos(k_y))*sigma_z

When I implement it on a square grid I obtain the following output:

# Discrete coordinates: x y

# Onsite element: _cache_0 = ( array([[ 1.+0.j, 0.+0.j], [ 0.+0.j, -1.+0.j]])) _cache_1 = ( array([[-1.+0.j, 0.+0.j], [ 0.+0.j, 1.+0.j]])) _cache_2 = ( array([[-1.+0.j, 0.+0.j], [ 0.+0.j, 1.+0.j]])) _cache_3 = ( array([[ 2.+0.j, 0.+0.j], [ 0.+0.j, -2.+0.j]])) _cache_4 = ( array([[0.+0.j, 1.+0.j], [1.+0.j, 0.+0.j]])) _cache_5 = ( array([[0.+0.j, 0.-1.j], [0.+1.j, 0.+0.j]])) def onsite(site, B, M, cos, k_x, k_y, sin): _const_0 = (cos(k_x)) _const_1 = (cos(k_y)) _const_2 = (sin(k_x)) _const_3 = (sin(k_y)) return (B*M) * (_cache_0) + (B*_const_0) * (_cache_1) + (B*_const_1) * (_cache_2) + (B) * (_cache_3) + (_const_2) * (_cache_4) + (_const_3) * (_cache_5)

My issues are:

1. The constants are just my sines and cosines of k_x/k_y, so what happened here? 2. I think because of that it can't find the hopping terms; 3. It doesn't plot any lattice, however if I set hamiltonian = "k_x+k_y" i does plot the square lattice.

I know the hamiltonian can be linearized in k, but if possible I want to implement it as it is.

Thanks in advance.