From 781854b81ad37953d8931e2ac2cdfa71b01cadf7 Mon Sep 17 00:00:00 2001
From: guanjihuan <guanjihuan@163.com>
Date: Thu, 16 Dec 2021 19:52:09 +0800
Subject: [PATCH] Update Hofstadter_butterfly_of_graphene_ribbon.py

---
 .../Hofstadter_butterfly_of_graphene_ribbon.py  | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py b/academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py
index 7703ace..799c1e5 100644
--- a/academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py
+++ b/academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py
@@ -9,7 +9,7 @@ import cmath
 import functools 
 import guan
 
-def hamiltonian(B, k, N, M, t1):  # graphene哈密顿量（N是条带的宽度参数）
+def hamiltonian(B, k, N, M, t1, a):  # graphene哈密顿量（N是条带的宽度参数）
     # 初始化为零矩阵
     h00 = np.zeros((4*N, 4*N), dtype=complex)
     h01 = np.zeros((4*N, 4*N), dtype=complex)
@@ -20,11 +20,11 @@ def hamiltonian(B, k, N, M, t1):  # graphene哈密顿量（N是条带的宽度
         h00[i*4+2, i*4+2] = M
         h00[i*4+3, i*4+3] = -M
         # 最近邻
-        h00[i*4+0, i*4+1] = t1*cmath.exp(-2*pi*1j*B*(3*i+1/4)*(np.sqrt(3)/2))
+        h00[i*4+0, i*4+1] = t1*cmath.exp(-2*pi*1j*B*(3*a*i+1/4*a)*(np.sqrt(3)/2*a))
         h00[i*4+1, i*4+0] = np.conj(h00[i*4+0, i*4+1])
         h00[i*4+1, i*4+2] = t1
         h00[i*4+2, i*4+1] = np.conj(h00[i*4+1, i*4+2])
-        h00[i*4+2, i*4+3] = t1*cmath.exp(2*pi*1j*B*(3*i+7/4)*(np.sqrt(3)/2))
+        h00[i*4+2, i*4+3] = t1*cmath.exp(2*pi*1j*B*(3*a*i+7/4*a)*(np.sqrt(3)/2)*a)
         h00[i*4+3, i*4+2] = np.conj(h00[i*4+2, i*4+3])
     for i in range(N-1):
         # 最近邻
@@ -33,17 +33,18 @@ def hamiltonian(B, k, N, M, t1):  # graphene哈密顿量（N是条带的宽度
     # 原胞间的跃迁h01
     for i in range(N):
         # 最近邻
-        h01[i*4+1, i*4+0] = t1*cmath.exp(-2*pi*1j*B*(3*i+1/4)*(np.sqrt(3)/2))
-        h01[i*4+2, i*4+3] = t1*cmath.exp(-2*pi*1j*B*(3*i+7/4)*(np.sqrt(3)/2))
+        h01[i*4+1, i*4+0] = t1*cmath.exp(-2*pi*1j*B*(3*a*i+1/4*a)*(np.sqrt(3)/2*a))
+        h01[i*4+2, i*4+3] = t1*cmath.exp(-2*pi*1j*B*(3*a*i+7/4*a)*(np.sqrt(3)/2*a))
     matrix = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
     return matrix
 
 def main():
     N = 30
-    hamiltonian_function0 = functools.partial(hamiltonian, k=0, N=N, M=0, t1=1)
-    B_array = np.linspace(0, 1/(3*np.sqrt(3)/2), 100)
+    a = 1
+    hamiltonian_function0 = functools.partial(hamiltonian, k=0, N=N, M=0, t1=1, a=a)
+    B_array = np.linspace(0, 1/(3*np.sqrt(3)/2*a*a), 100)
     eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(B_array, hamiltonian_function0)
-    BS_array = B_array*(3*np.sqrt(3)/2)
+    BS_array = B_array*(3*np.sqrt(3)/2*a*a)
     guan.plot(BS_array, eigenvalue_array, xlabel='Flux (BS/phi_0)', ylabel='E', title='Ny=%i'%N, filename='a', show=1, save=0, type='k.', y_min=None, y_max=None, markersize=3)
    
 if __name__ == '__main__':