delete unused functions

codes/kwant_examples.py

@@ -33,54 +33,4 @@ def graphene_extended_hubbard():
         func_hop = nn_int,
     )
     
-    return bulk_graphene, syst_V
-
+    return bulk_graphene, syst_V
\ No newline at end of file
-def hubbard_2D(U, N_ks):
-    square = kwant.lattice.square(a=1, norbs=2)
-    # create bulk system
-    bulk_hubbard = kwant.Builder(kwant.TranslationalSymmetry(*square.prim_vecs))
-    bulk_hubbard[square.shape((lambda pos: True), (0, 0))] = 0 * np.eye(2)
-    # add hoppings between lattice points
-    bulk_hubbard[square.neighbors()] = -1
-
-    # use kwant wraparound to sample bulk k-space
-    wrapped_fsyst = kwant.wraparound.wraparound(bulk_hubbard).finalized()
-
-    # return a hamiltonian for a given kx, ky
-    @np.vectorize
-    def hamiltonian_return(kx, ky, params={}):
-        ham = wrapped_fsyst.hamiltonian_submatrix(params={**params, **dict(k_x=kx, k_y=ky)})
-        return ham
-    
-    N_k_axis = np.linspace(0, 2 * np.pi, N_ks, endpoint=False)
-    hamiltonians_0 = np.array(
-        [[hamiltonian_return(kx, ky) for kx in N_k_axis] for ky in N_k_axis]
-    )
-
-    # onsite interactions
-    v_int = U * np.ones((2,2))
-    V = np.array([[v_int for i in range(N_ks)] for j in range(N_ks)])
-    return hamiltonians_0, V
-
-
-def hubbard_1D(U, N_ks):
-    chain = kwant.lattice.chain(a=1, norbs=2)
-    # create bulk system
-    bulk_hubbard = kwant.Builder(kwant.TranslationalSymmetry(*chain.prim_vecs))
-    bulk_hubbard[chain.shape((lambda pos: True), (0,))] = 0 * s0
-    # add hoppings between lattice points
-    bulk_hubbard[chain.neighbors()] = -1
-
-
-    # return a hamiltonian for a given kx, ky
-    @np.vectorize
-    def hamiltonian_return(kx, params={}):
-        ham = wrapped_fsyst.hamiltonian_submatrix(params={**params, **dict(k_x=kx)})
-        return ham
-
-    N_k_axis = np.linspace(0, 2 * np.pi, N_ks, endpoint=False)
-    hamiltonians_0 = np.array(
-        [hamiltonian_return(kx) for kx in N_k_axis]
-        )
-
-    return hamiltonians_0, V