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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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44
API_Reference/API_Reference.py
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@ -12,6 +12,13 @@ import guan
@ -229,8 +236,7 @@ hamiltonian = guan.hamiltonian_of_cubic_lattice(k1, k2, k3)
hamiltonian = guan.hamiltonian_of_ssh_model(k, v=0.6, w=1) hamiltonian = guan.hamiltonian_of_ssh_model(k, v=0.6, w=1)
# 石墨烯的哈密顿量 # 石墨烯的哈密顿量
import math hamiltonian = guan.hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default')
hamiltonian = guan.hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a=1/math.sqrt(3))
# 石墨烯有效模型的哈密顿量 # 石墨烯有效模型的哈密顿量
hamiltonian = guan.effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley_index=0) hamiltonian = guan.effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley_index=0)
@ -242,12 +248,10 @@ hamiltonian = guan.effective_hamiltonian_of_graphene_after_discretization(qx, qy
hamiltonian = guan.hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1, period=0) hamiltonian = guan.hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1, period=0)
# Haldane模型的哈密顿量 # Haldane模型的哈密顿量
import math hamiltonian = guan.hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a='default')
hamiltonian = guan.hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1/math.sqrt(3))
# 准一维Haldane模型条带的哈密顿量 # 准一维Haldane模型条带的哈密顿量
import math hamiltonian = guan.hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi='default', period=0)
hamiltonian = guan.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)
# 一个量子反常霍尔效应的哈密顿量 # 一个量子反常霍尔效应的哈密顿量
hamiltonian = guan.hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1) hamiltonian = guan.hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1)
@ -583,27 +587,24 @@ chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop(h
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)
# 通过高效法计算贝利曲率 # 通过高效法计算贝利曲率
import math k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0)
k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0)
# 通过高效法计算贝利曲率（可计算简并的情况） # 通过高效法计算贝利曲率（可计算简并的情况）
import math k_array, berry_curvature_array = guan.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)
k_array, berry_curvature_array = guan.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)
# 通过Wilson loop方法计算贝里曲率 # 通过Wilson loop方法计算贝里曲率
import math k_array, berry_curvature_array = guan.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)
k_array, berry_curvature_array = guan.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)
# 通过Wilson loop方法计算贝里曲率（可计算简并的情况） # 通过Wilson loop方法计算贝里曲率（可计算简并的情况）
import math k_array, berry_curvature_array = guan.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)
k_array, berry_curvature_array = guan.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)
# 计算蜂窝格子的陈数（高效法） # 计算蜂窝格子的陈数（高效法）
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)
# 计算Wilson loop # 计算Wilson loop
import math wilson_loop_array = guan.calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0)
wilson_loop_array = guan.calculate_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0)
@ -779,6 +780,13 @@ guan.print_array_with_index(array, show_index=1, index_type=0)
@ -856,6 +864,10 @@ guan.play_element_words(random_on=0, show_translation=1, show_link=1, translatio

2
Source_Code/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 = 0.0.180 version = 0.0.181
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
Source_Code/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: 0.0.180 Version: 0.0.181
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

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. # 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 # Installation: pip install --upgrade guan
@ -679,11 +679,12 @@ def hamiltonian_of_ssh_model(k, v=0.6, w=1):
return hamiltonian return hamiltonian
# 石墨烯的哈密顿量 # 石墨烯的哈密顿量
import math def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a=1/math.sqrt(3)):
import numpy as np import numpy as np
import cmath import cmath
import math import math
if a == 'default':
a = 1/math.sqrt(3)
h0 = np.zeros((2, 2), dtype=complex) # mass term h0 = np.zeros((2, 2), dtype=complex) # mass term
h1 = np.zeros((2, 2), dtype=complex) # nearest hopping h1 = np.zeros((2, 2), dtype=complex) # nearest hopping
h0[0, 0] = staggered_potential 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 return hamiltonian
# Haldane模型的哈密顿量 # 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='default'):
def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1/math.sqrt(3)):
import numpy as np import numpy as np
import cmath import cmath
import math import math
if a == 'default':
a=1/math.sqrt(3)
h0 = np.zeros((2, 2), dtype=complex) # mass term h0 = np.zeros((2, 2), dtype=complex) # mass term
h1 = np.zeros((2, 2), dtype=complex) # nearest hopping h1 = np.zeros((2, 2), dtype=complex) # nearest hopping
h2 = np.zeros((2, 2), dtype=complex) # next 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 return hamiltonian
# 准一维Haldane模型条带的哈密顿量 # 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='default', period=0):
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):
import numpy as np import numpy as np
import cmath 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 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 h01 = np.zeros((4*N, 4*N), dtype=complex) # hopping between unit cells
for i in range(N): 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 return chern_number
# 通过高效法计算贝利曲率 # 通过高效法计算贝利曲率
import math def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
import numpy as np import numpy as np
import cmath import cmath
import guan 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==(): if np.array(hamiltonian_function(0, 0)).shape==():
dim = 1 dim = 1
else: else:
@ -2464,10 +2472,14 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=
return k_array, berry_curvature_array 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） # 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='default', k_max='default', precision=100, print_show=0):
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):
import numpy as np import numpy as np
import cmath 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 delta = (k_max-k_min)/precision
k_array = np.arange(k_min, k_max, delta) k_array = np.arange(k_min, k_max, delta)
berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex) 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 return k_array, berry_curvature_array
# 通过Wilson loop方法计算贝里曲率 # 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='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
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):
import numpy as np 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==(): if np.array(hamiltonian_function(0, 0)).shape==():
dim = 1 dim = 1
else: else:
@ -2590,9 +2606,13 @@ def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math
return k_array, berry_curvature_array 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） # 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='default', k_max='default', 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=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
import numpy as np 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 delta = (k_max-k_min)/precision_of_plaquettes
k_array = np.arange(k_min, k_max, delta) k_array = np.arange(k_min, k_max, delta)
berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex) 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 return chern_number
# 计算Wilson loop # 计算Wilson loop
import math def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
def calculate_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
import numpy as np import numpy as np
import guan 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) k_array = np.linspace(k_min, k_max, precision)
dim = np.array(hamiltonian_function(0)).shape[0] dim = np.array(hamiltonian_function(0)).shape[0]
wilson_loop_array = np.ones(dim, dtype=complex) wilson_loop_array = np.ones(dim, dtype=complex)