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0.0.181

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guanjihuan
2023-10-03 19:09:18 +08:00
parent 7ce33b5ccb
commit 6d1ed5924c
4 changed files with 71 additions and 35 deletions
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2
Source_Code/PyPI/setup.cfg
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@@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.0.180
version = 0.0.181
author = guanjihuan
author_email = guanjihuan@163.com
description = An open source python package

2
Source_Code/PyPI/src/guan.egg-info/PKG-INFO
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@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: guan
Version: 0.0.180
Version: 0.0.181
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

58
Source_Code/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-0.0.180, updated on December 03, 2023.
# The current version is guan-0.0.181, updated on December 03, 2023.
# Installation: pip install --upgrade guan
@@ -679,11 +679,12 @@ def hamiltonian_of_ssh_model(k, v=0.6, w=1):
return hamiltonian
# 石墨烯的哈密顿量
import math
def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a=1/math.sqrt(3)):
def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
import numpy as np
import cmath
import math
if a == 'default':
a = 1/math.sqrt(3)
h0 = np.zeros((2, 2), dtype=complex) # mass term
h1 = np.zeros((2, 2), dtype=complex) # nearest hopping
h0[0, 0] = staggered_potential
@@ -753,11 +754,12 @@ def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1
return hamiltonian
# Haldane模型的哈密顿量
import math
def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1/math.sqrt(3)):
def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a='default'):
import numpy as np
import cmath
import math
if a == 'default':
a=1/math.sqrt(3)
h0 = np.zeros((2, 2), dtype=complex) # mass term
h1 = np.zeros((2, 2), dtype=complex) # nearest hopping
h2 = np.zeros((2, 2), dtype=complex) # next nearest hopping
@@ -771,10 +773,12 @@ def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1
return hamiltonian
# 准一维Haldane模型条带的哈密顿量
import math
def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi=math.pi/4, period=0):
def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi='default', period=0):
import numpy as np
import cmath
import math
if phi == 'default':
phi=math.pi/4
h00 = np.zeros((4*N, 4*N), dtype=complex) # hopping in a unit cell
h01 = np.zeros((4*N, 4*N), dtype=complex) # hopping between unit cells
for i in range(N):
@@ -2422,11 +2426,15 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_ca
return chern_number
# 通过高效法计算贝利曲率
import math
def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
import numpy as np
import cmath
import guan
import math
if k_min == 'default':
k_min = -math.pi
if k_max == 'default':
k_max=math.pi
if np.array(hamiltonian_function(0, 0)).shape==():
dim = 1
else:
@@ -2464,10 +2472,14 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=
return k_array, berry_curvature_array
# 通过高效法计算贝利曲率（可计算简并的情况）
import math
def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min='default', k_max='default', precision=100, print_show=0):
import numpy as np
import cmath
import math
if k_min == 'default':
k_min = -math.pi
if k_max == 'default':
k_max=math.pi
delta = (k_max-k_min)/precision
k_array = np.arange(k_min, k_max, delta)
berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex)
@@ -2538,9 +2550,13 @@ def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamilton
return k_array, berry_curvature_array
# 通过Wilson loop方法计算贝里曲率
import math
def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
import numpy as np
import math
if k_min == 'default':
k_min = -math.pi
if k_max == 'default':
k_max=math.pi
if np.array(hamiltonian_function(0, 0)).shape==():
dim = 1
else:
@@ -2590,9 +2606,13 @@ def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math
return k_array, berry_curvature_array
# 通过Wilson loop方法计算贝里曲率（可计算简并的情况）
import math
def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
import numpy as np
import math
if k_min == 'default':
k_min = -math.pi
if k_max == 'default':
k_max=math.pi
delta = (k_max-k_min)/precision_of_plaquettes
k_array = np.arange(k_min, k_max, delta)
berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex)
@@ -2700,10 +2720,14 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
return chern_number
# 计算Wilson loop
import math
def calculate_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
import numpy as np
import guan
import math
if k_min == 'default':
k_min = -math.pi
if k_max == 'default':
k_max=math.pi
k_array = np.linspace(k_min, k_max, precision)
dim = np.array(hamiltonian_function(0)).shape[0]
wilson_loop_array = np.ones(dim, dtype=complex)