From fe3af47b31b17c64fd0e6dc84466efe4301537b2 Mon Sep 17 00:00:00 2001
From: antoniolrm <am@antoniomanesco.org>
Date: Mon, 23 Oct 2023 15:20:39 +0200
Subject: [PATCH] delete unused functions
---
codes/kwant_examples.py | 52 +----------------------------------------
1 file changed, 1 insertion(+), 51 deletions(-)
diff --git a/codes/kwant_examples.py b/codes/kwant_examples.py
index 0b25865..fffc827 100644
--- a/codes/kwant_examples.py
+++ b/codes/kwant_examples.py
@@ -33,54 +33,4 @@ def graphene_extended_hubbard():
func_hop = nn_int,
)
- return bulk_graphene, syst_V
-
-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
+ return bulk_graphene, syst_V
\ No newline at end of file
--
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