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0.0.126

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guanjihuan
2022-08-28 01:53:51 +08:00
parent 73d2cda181
commit a015561026
4 changed files with 70 additions and 3 deletions
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2
PyPI/src/guan.egg-info/PKG-INFO
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@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: guan
Version: 0.0.125
Version: 0.0.126
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

67
PyPI/src/guan/__init__.py
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@@ -2,7 +2,7 @@
# With this package, you can calculate band structures, density of states, quantum transport and topological invariant of tight-binding models by invoking the functions you need. Other frequently used functions are also integrated in this package, such as file reading/writing, figure plotting, data processing.
# The current version is guan-0.0.125, updated on August 25, 2022.
# The current version is guan-0.0.126, updated on August 28, 2022.
# Installation: pip install --upgrade guan
@@ -1551,6 +1551,71 @@ def calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_
chern_number = chern_number/(2*math.pi*1j)
return chern_number
def calculate_chern_number_for_square_lattice_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision=100, print_show=0):
delta = 2*math.pi/precision
chern_number = 0
for kx in np.arange(-math.pi, math.pi, delta):
if print_show == 1:
print(kx)
for ky in np.arange(-math.pi, math.pi, delta):
H = hamiltonian_function(kx, ky)
eigenvalue, vector = np.linalg.eigh(H)
H_delta_kx = hamiltonian_function(kx+delta, ky)
eigenvalue, vector_delta_kx = np.linalg.eigh(H_delta_kx)
H_delta_ky = hamiltonian_function(kx, ky+delta)
eigenvalue, vector_delta_ky = np.linalg.eigh(H_delta_ky)
H_delta_kx_ky = hamiltonian_function(kx+delta, ky+delta)
eigenvalue, vector_delta_kx_ky = np.linalg.eigh(H_delta_kx_ky)
dim = len(index_of_bands)
det_value = 1
# first dot
dot_matrix = np.zeros((dim , dim), dtype=complex)
i0 = 0
for dim1 in index_of_bands:
j0 = 0
for dim2 in index_of_bands:
dot_matrix[dim1, dim2] = np.dot(np.conj(vector[:, dim1]), vector_delta_kx[:, dim2])
j0 += 1
i0 += 1
dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
det_value = det_value*dot_matrix
# second dot
dot_matrix = np.zeros((dim , dim), dtype=complex)
i0 = 0
for dim1 in index_of_bands:
j0 = 0
for dim2 in index_of_bands:
dot_matrix[dim1, dim2] = np.dot(np.conj(vector_delta_kx[:, dim1]), vector_delta_kx_ky[:, dim2])
j0 += 1
i0 += 1
dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
det_value = det_value*dot_matrix
# third dot
dot_matrix = np.zeros((dim , dim), dtype=complex)
i0 = 0
for dim1 in index_of_bands:
j0 = 0
for dim2 in index_of_bands:
dot_matrix[dim1, dim2] = np.dot(np.conj(vector_delta_kx_ky[:, dim1]), vector_delta_ky[:, dim2])
j0 += 1
i0 += 1
dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
det_value = det_value*dot_matrix
# four dot
dot_matrix = np.zeros((dim , dim), dtype=complex)
i0 = 0
for dim1 in index_of_bands:
j0 = 0
for dim2 in index_of_bands:
dot_matrix[dim1, dim2] = np.dot(np.conj(vector_delta_ky[:, dim1]), vector[:, dim2])
j0 += 1
i0 += 1
dot_matrix = np.linalg.det(dot_matrix)/abs(np.linalg.det(dot_matrix))
det_value= det_value*dot_matrix
chern_number += cmath.log(det_value)
chern_number = chern_number/(2*math.pi*1j)
return chern_number
def calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
delta = 2*math.pi/precision_of_plaquettes
chern_number = 0