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guanjihuan 2022-04-01 16:40:40 +08:00
parent 96bf2a0172
commit fb933a9d29
5 changed files with 6 additions and 6 deletions
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2
academic_codes/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_with_guan.py.py
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@ -5,4 +5,4 @@ fermi_energy_array = np.linspace(-4, 4, 400)
h00 = guan.hamiltonian_of_finite_size_system_along_one_direction(4) h00 = guan.hamiltonian_of_finite_size_system_along_one_direction(4)
h01 = np.identity(4) h01 = np.identity(4)
conductance_array = guan.calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01) conductance_array = guan.calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01)
guan.plot(fermi_energy_array, conductance_array, xlabel='E', ylabel='Conductance', type='-') guan.plot(fermi_energy_array, conductance_array, xlabel='E', ylabel='Conductance', style='-')

2
academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py
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@ -45,7 +45,7 @@ def main():
B_array = np.linspace(0, 1/(3*np.sqrt(3)/2*a*a), 100) 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*a*a) 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, style='k.', y_min=None, y_max=None, markersize=3)
if __name__ == '__main__': if __name__ == '__main__':
main() main()

2
academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_Wilson_loop.py
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@ -61,7 +61,7 @@ def main():
nu_x[i0] += 1 nu_x[i0] += 1
nu_x = np.sort(nu_x) nu_x = np.sort(nu_x)
nu_x_array.append(nu_x.real) nu_x_array.append(nu_x.real)
guan.plot(ky_array, nu_x_array, xlabel='ky', ylabel='nu_x', type='-', y_min=0, y_max=1) guan.plot(ky_array, nu_x_array, xlabel='ky', ylabel='nu_x', style='-', y_min=0, y_max=1)
if __name__ == '__main__': if __name__ == '__main__':
main() main()

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academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_nested_Wilson_loop.py
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@ -81,7 +81,7 @@ def main():
p_y_for_nu_x += 1 p_y_for_nu_x += 1
p_y_for_nu_x_array.append(p_y_for_nu_x.real) p_y_for_nu_x_array.append(p_y_for_nu_x.real)
print('p_y_for_nu_x=', p_y_for_nu_x) print('p_y_for_nu_x=', p_y_for_nu_x)
guan.plot(kx2_array, p_y_for_nu_x_array, xlabel='kx', ylabel='p_y_for_nu_x', type='-o', y_min=0, y_max=1) guan.plot(kx2_array, p_y_for_nu_x_array, xlabel='kx', ylabel='p_y_for_nu_x', style='-o', y_min=0, y_max=1)
# Part II: calculate p_x_for_nu_y # Part II: calculate p_x_for_nu_y
p_x_for_nu_y_array = [] p_x_for_nu_y_array = []
@ -117,7 +117,7 @@ def main():
p_x_for_nu_y_array.append(p_x_for_nu_y.real) p_x_for_nu_y_array.append(p_x_for_nu_y.real)
print('p_x_for_nu_y=', p_x_for_nu_y) print('p_x_for_nu_y=', p_x_for_nu_y)
# print(sum(p_x_for_nu_y_array)/len(p_x_for_nu_y_array)) # print(sum(p_x_for_nu_y_array)/len(p_x_for_nu_y_array))
guan.plot(ky2_array, p_x_for_nu_y_array, xlabel='ky', ylabel='p_x_for_nu_y', type='-o', y_min=0, y_max=1) guan.plot(ky2_array, p_x_for_nu_y_array, xlabel='ky', ylabel='p_x_for_nu_y', style='-o', y_min=0, y_max=1)
def get_nu_x_vector(kx_array, ky): def get_nu_x_vector(kx_array, ky):
Num_kx = len(kx_array) Num_kx = len(kx_array)

2
academic_codes/2021.12.27_Chern_numbers_of_Landau_levels/Chern_numbers_of_Landau_levels_in_square_lattice.py
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@ -30,7 +30,7 @@ def main():
k_array = np.linspace(-pi, pi, 100) k_array = np.linspace(-pi, pi, 100)
H_k = functools.partial(hamiltonian, ky=0, Ny=Ny, B=1/Ny) H_k = functools.partial(hamiltonian, ky=0, Ny=Ny, B=1/Ny)
eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(k_array, H_k) eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(k_array, H_k)
guan.plot(k_array, eigenvalue_array, xlabel='kx', ylabel='E', type='k') guan.plot(k_array, eigenvalue_array, xlabel='kx', ylabel='E', style='k')
H_k = functools.partial(hamiltonian, Ny=Ny, B=1/Ny) H_k = functools.partial(hamiltonian, Ny=Ny, B=1/Ny)
chern_number = guan.calculate_chern_number_for_square_lattice(H_k, precision=100) chern_number = guan.calculate_chern_number_for_square_lattice(H_k, precision=100)