From e2de158c99ff6ca659a8048b4ab0c250afa5f413 Mon Sep 17 00:00:00 2001
From: Kostas Vilkelis <kostasvilkelis@gmail.com>
Date: Tue, 30 May 2023 15:01:21 +0200
Subject: [PATCH] add 1d hubbard test
---
codes/test.py | 28 ++++++++++++++++++++++++++++
1 file changed, 28 insertions(+)
diff --git a/codes/test.py b/codes/test.py
index b59fb59..7e60416 100644
--- a/codes/test.py
+++ b/codes/test.py
@@ -69,3 +69,31 @@ def hubbard_2D(U, N_ks):
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 * np.eye(2)
+ # add hoppings between lattice points
+ bulk_hubbard[chain.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, 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]
+ )
+
+ # onsite interactions
+ v_int = U * np.ones((2,2))
+ V = np.array([v_int for i in range(N_ks)])
+ return hamiltonians_0, V
--
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