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0.0.122

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guanjihuan 2022-08-13 06:35:32 +08:00
parent 4d5cfe2914
commit 302ea8829f
4 changed files with 31 additions and 31 deletions
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4
API_Reference.py
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@ -246,9 +246,9 @@ guan.print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_
chern_number = guan.calculate_chern_number_for_square_lattice(hamiltonian_function, precision=100, print_show=0) chern_number = guan.calculate_chern_number_for_square_lattice(hamiltonian_function, precision=100, print_show=0)
chern_number = guan.calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0) chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)
chern_number = guan.calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0) chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0)
chern_number = guan.calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0) chern_number = guan.calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0)

2
PyPI/setup.cfg
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@ -1,7 +1,7 @@
[metadata] [metadata]
# replace with your username: # replace with your username:
name = guan name = guan
version = 0.0.121 version = 0.0.122
author = guanjihuan author = guanjihuan
author_email = guanjihuan@163.com author_email = guanjihuan@163.com
description = An open source python package description = An open source python package

2
PyPI/src/guan.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 2.1 Metadata-Version: 2.1
Name: guan Name: guan
Version: 0.0.121 Version: 0.0.122
Summary: An open source python package Summary: An open source python package
Home-page: https://py.guanjihuan.com Home-page: https://py.guanjihuan.com
Author: guanjihuan Author: guanjihuan

54
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. # 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-0.0.121, updated on August 12, 2022. # The current version is guan-0.0.122, updated on August 13, 2022.
# Installation: pip install --upgrade guan # Installation: pip install --upgrade guan
@ -1551,7 +1551,7 @@ def calculate_chern_number_for_square_lattice(hamiltonian_function, precision=10
chern_number = chern_number/(2*math.pi*1j) chern_number = chern_number/(2*math.pi*1j)
return chern_number 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): 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 delta = 2*math.pi/precision_of_plaquettes
chern_number = 0 chern_number = 0
for kx in np.arange(-math.pi, math.pi, delta): for kx in np.arange(-math.pi, math.pi, delta):
@ -1560,39 +1560,39 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop(hamiltonian_funct
for ky in np.arange(-math.pi, math.pi, delta): for ky in np.arange(-math.pi, math.pi, delta):
vector_array = [] vector_array = []
# line_1 # line_1
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_wilson_loop):
H_delta = hamiltonian_function(kx+delta/precision_of_Wilson_loop*i0, ky) H_delta = hamiltonian_function(kx+delta/precision_of_wilson_loop*i0, ky)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_2 # line_2
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_wilson_loop):
H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_Wilson_loop*i0) H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_wilson_loop*i0)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_3 # line_3
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_wilson_loop):
H_delta = hamiltonian_function(kx+delta-delta/precision_of_Wilson_loop*i0, ky+delta) H_delta = hamiltonian_function(kx+delta-delta/precision_of_wilson_loop*i0, ky+delta)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_4 # line_4
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_wilson_loop):
H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_Wilson_loop*i0) H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_wilson_loop*i0)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
Wilson_loop = 1 wilson_loop = 1
for i0 in range(len(vector_array)-1): for i0 in range(len(vector_array)-1):
Wilson_loop = Wilson_loop*np.dot(vector_array[i0].transpose().conj(), vector_array[i0+1]) wilson_loop = wilson_loop*np.dot(vector_array[i0].transpose().conj(), vector_array[i0+1])
Wilson_loop = Wilson_loop*np.dot(vector_array[len(vector_array)-1].transpose().conj(), vector_array[0]) wilson_loop = wilson_loop*np.dot(vector_array[len(vector_array)-1].transpose().conj(), vector_array[0])
arg = np.log(np.diagonal(Wilson_loop))/1j arg = np.log(np.diagonal(wilson_loop))/1j
chern_number = chern_number + arg chern_number = chern_number + arg
chern_number = chern_number/(2*math.pi) chern_number = chern_number/(2*math.pi)
return chern_number return chern_number
def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_Wilson_loop=5, print_show=0): def calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
delta = 2*math.pi/precision_of_plaquettes delta = 2*math.pi/precision_of_plaquettes
chern_number = 0 chern_number = 0
for kx in np.arange(-math.pi, math.pi, delta): for kx in np.arange(-math.pi, math.pi, delta):
@ -1601,30 +1601,30 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_ca
for ky in np.arange(-math.pi, math.pi, delta): for ky in np.arange(-math.pi, math.pi, delta):
vector_array = [] vector_array = []
# line_1 # line_1
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_wilson_loop):
H_delta = hamiltonian_function(kx+delta/precision_of_Wilson_loop*i0, ky) H_delta = hamiltonian_function(kx+delta/precision_of_wilson_loop*i0, ky)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_2 # line_2
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_wilson_loop):
H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_Wilson_loop*i0) H_delta = hamiltonian_function(kx+delta, ky+delta/precision_of_wilson_loop*i0)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_3 # line_3
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_wilson_loop):
H_delta = hamiltonian_function(kx+delta-delta/precision_of_Wilson_loop*i0, ky+delta) H_delta = hamiltonian_function(kx+delta-delta/precision_of_wilson_loop*i0, ky+delta)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
# line_4 # line_4
for i0 in range(precision_of_Wilson_loop): for i0 in range(precision_of_wilson_loop):
H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_Wilson_loop*i0) H_delta = hamiltonian_function(kx, ky+delta-delta/precision_of_wilson_loop*i0)
eigenvalue, eigenvector = np.linalg.eig(H_delta) eigenvalue, eigenvector = np.linalg.eig(H_delta)
vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))] vector_delta = eigenvector[:, np.argsort(np.real(eigenvalue))]
vector_array.append(vector_delta) vector_array.append(vector_delta)
Wilson_loop = 1 wilson_loop = 1
dim = len(index_of_bands) dim = len(index_of_bands)
for i0 in range(len(vector_array)-1): for i0 in range(len(vector_array)-1):
dot_matrix = np.zeros((dim , dim), dtype=complex) dot_matrix = np.zeros((dim , dim), dtype=complex)
@ -1636,7 +1636,7 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_ca
i02 += 1 i02 += 1
i01 += 1 i01 += 1
det_value = np.linalg.det(dot_matrix) det_value = np.linalg.det(dot_matrix)
Wilson_loop = Wilson_loop*det_value wilson_loop = wilson_loop*det_value
dot_matrix_plus = np.zeros((dim , dim), dtype=complex) dot_matrix_plus = np.zeros((dim , dim), dtype=complex)
i01 = 0 i01 = 0
for dim1 in index_of_bands: for dim1 in index_of_bands:
@ -1646,8 +1646,8 @@ def calculate_chern_number_for_square_lattice_with_Wilson_loop_for_degenerate_ca
i02 += 1 i02 += 1
i01 += 1 i01 += 1
det_value = np.linalg.det(dot_matrix_plus) det_value = np.linalg.det(dot_matrix_plus)
Wilson_loop = Wilson_loop*det_value wilson_loop = wilson_loop*det_value
arg = np.log(Wilson_loop)/1j arg = np.log(wilson_loop)/1j
chern_number = chern_number + arg chern_number = chern_number + arg
chern_number = chern_number/(2*math.pi) chern_number = chern_number/(2*math.pi)
return chern_number return chern_number