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		| @@ -38,6 +38,42 @@ def calculate_chern_number_for_square_lattice(hamiltonian_function, precision=10 | ||||
|     chern_number = chern_number/(2*pi*1j) | ||||
|     return chern_number | ||||
|  | ||||
| def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300): | ||||
|     if np.array(hamiltonian_function(0, 0)).shape==(): | ||||
|         dim = 1 | ||||
|     else: | ||||
|         dim = np.array(hamiltonian_function(0, 0)).shape[0]    | ||||
|     chern_number = np.zeros(dim, dtype=complex) | ||||
|     L1 = 4*sqrt(3)*pi/9/a | ||||
|     L2 = 2*sqrt(3)*pi/9/a | ||||
|     L3 = 2*pi/3/a | ||||
|     delta1 = 2*L1/precision | ||||
|     delta3 = 2*L3/precision | ||||
|     for kx in np.arange(-L1, L1, delta1): | ||||
|         for ky in np.arange(-L3, L3, delta3): | ||||
|             if (-L2<=kx<=L2) or (kx>L2 and -(L1-kx)*tan(pi/3)<=ky<=(L1-kx)*tan(pi/3)) or (kx<-L2 and  -(kx-(-L1))*tan(pi/3)<=ky<=(kx-(-L1))*tan(pi/3)): | ||||
|                 H = hamiltonian_function(kx, ky) | ||||
|                 vector = guan.calculate_eigenvector(H) | ||||
|                 H_delta_kx = hamiltonian_function(kx+delta1, ky)  | ||||
|                 vector_delta_kx = guan.calculate_eigenvector(H_delta_kx) | ||||
|                 H_delta_ky = hamiltonian_function(kx, ky+delta3) | ||||
|                 vector_delta_ky = guan.calculate_eigenvector(H_delta_ky) | ||||
|                 H_delta_kx_ky = hamiltonian_function(kx+delta1, ky+delta3) | ||||
|                 vector_delta_kx_ky = guan.calculate_eigenvector(H_delta_kx_ky) | ||||
|                 for i in range(dim): | ||||
|                     vector_i = vector[:, i] | ||||
|                     vector_delta_kx_i = vector_delta_kx[:, i] | ||||
|                     vector_delta_ky_i = vector_delta_ky[:, i] | ||||
|                     vector_delta_kx_ky_i = vector_delta_kx_ky[:, i] | ||||
|                     Ux = np.dot(np.conj(vector_i), vector_delta_kx_i)/abs(np.dot(np.conj(vector_i), vector_delta_kx_i)) | ||||
|                     Uy = np.dot(np.conj(vector_i), vector_delta_ky_i)/abs(np.dot(np.conj(vector_i), vector_delta_ky_i)) | ||||
|                     Ux_y = np.dot(np.conj(vector_delta_ky_i), vector_delta_kx_ky_i)/abs(np.dot(np.conj(vector_delta_ky_i), vector_delta_kx_ky_i)) | ||||
|                     Uy_x = np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)/abs(np.dot(np.conj(vector_delta_kx_i), vector_delta_kx_ky_i)) | ||||
|                     F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy)) | ||||
|                     chern_number[i] = chern_number[i] + F | ||||
|     chern_number = chern_number/(2*pi*1j) | ||||
|     return chern_number | ||||
|  | ||||
| def calculate_wilson_loop(hamiltonian_function, k_min=-pi, k_max=pi, precision=100): | ||||
|     k_array = np.linspace(k_min, k_max, precision) | ||||
|     dim = np.array(hamiltonian_function(0)).shape[0] | ||||
|   | ||||
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