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Update Hofstadter_butterfly_of_graphene_ribbon.py

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guanjihuan 2021-12-16 19:52:09 +08:00
parent 3f7148c3b3
commit 781854b81a
1 changed files with 9 additions and 8 deletions
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academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py
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@ -9,7 +9,7 @@ import cmath
import functools import functools
import guan 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 # 初始化为零矩阵
h00 = np.zeros((4*N, 4*N), dtype=complex) h00 = np.zeros((4*N, 4*N), dtype=complex)
h01 = 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+2, i*4+2] = M
h00[i*4+3, i*4+3] = -M h00[i*4+3, i*4+3] = -M
# 最近邻 # 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+0] = np.conj(h00[i*4+0, i*4+1])
h00[i*4+1, i*4+2] = t1 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+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]) h00[i*4+3, i*4+2] = np.conj(h00[i*4+2, i*4+3])
for i in range(N-1): for i in range(N-1):
# 最近邻 # 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 # 原胞间的跃迁h01
for i in range(N): for i in range(N):
# 最近邻 # 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+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*i+7/4)*(np.sqrt(3)/2)) 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) matrix = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
return matrix return matrix
def main(): def main():
N = 30 N = 30
hamiltonian_function0 = functools.partial(hamiltonian, k=0, N=N, M=0, t1=1) a = 1
B_array = np.linspace(0, 1/(3*np.sqrt(3)/2), 100) 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) 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) 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__': if __name__ == '__main__':