0.0.181
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@ -12,6 +12,13 @@ import guan
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@ -229,8 +236,7 @@ hamiltonian = guan.hamiltonian_of_cubic_lattice(k1, k2, k3)
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hamiltonian = guan.hamiltonian_of_ssh_model(k, v=0.6, w=1)
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# 石墨烯的哈密顿量
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import math
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hamiltonian = guan.hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a=1/math.sqrt(3))
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hamiltonian = guan.hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default')
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# 石墨烯有效模型的哈密顿量
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hamiltonian = guan.effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley_index=0)
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@ -242,12 +248,10 @@ hamiltonian = guan.effective_hamiltonian_of_graphene_after_discretization(qx, qy
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hamiltonian = guan.hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1, period=0)
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# Haldane模型的哈密顿量
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import math
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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))
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hamiltonian = guan.hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a='default')
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# 准一维Haldane模型条带的哈密顿量
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import math
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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)
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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)
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# 一个量子反常霍尔效应的哈密顿量
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hamiltonian = guan.hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1)
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@ -583,27 +587,24 @@ chern_number = guan.calculate_chern_number_for_square_lattice_with_wilson_loop(h
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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)
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# 通过高效法计算贝利曲率
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import math
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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)
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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)
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# 通过高效法计算贝利曲率（可计算简并的情况）
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import math
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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)
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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)
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# 通过Wilson loop方法计算贝里曲率
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import math
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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)
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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)
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# 通过Wilson loop方法计算贝里曲率（可计算简并的情况）
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import math
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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)
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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)
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# 计算蜂窝格子的陈数（高效法）
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chern_number = guan.calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0)
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# 计算Wilson loop
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import math
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wilson_loop_array = guan.calculate_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0)
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wilson_loop_array = guan.calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0)
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@ -779,6 +780,13 @@ guan.print_array_with_index(array, show_index=1, index_type=0)
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@ -856,6 +864,10 @@ guan.play_element_words(random_on=0, show_translation=1, show_link=1, translatio
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@ -1,7 +1,7 @@
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[metadata]
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# replace with your username:
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name = guan
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version = 0.0.180
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version = 0.0.181
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author = guanjihuan
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author_email = guanjihuan@163.com
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description = An open source python package
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@ -1,6 +1,6 @@
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Metadata-Version: 2.1
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Name: guan
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Version: 0.0.180
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Version: 0.0.181
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Summary: An open source python package
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Home-page: https://py.guanjihuan.com
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Author: guanjihuan
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@ -2,7 +2,7 @@
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# 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.
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# The current version is guan-0.0.180, updated on December 03, 2023.
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# The current version is guan-0.0.181, updated on December 03, 2023.
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# Installation: pip install --upgrade guan
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@ -679,11 +679,12 @@ def hamiltonian_of_ssh_model(k, v=0.6, w=1):
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return hamiltonian
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# 石墨烯的哈密顿量
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import math
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def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a=1/math.sqrt(3)):
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def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
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import numpy as np
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import cmath
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import math
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if a == 'default':
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a = 1/math.sqrt(3)
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h0 = np.zeros((2, 2), dtype=complex) # mass term
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h1 = np.zeros((2, 2), dtype=complex) # nearest hopping
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h0[0, 0] = staggered_potential
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@ -753,11 +754,12 @@ def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1
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return hamiltonian
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# Haldane模型的哈密顿量
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import math
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def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a=1/math.sqrt(3)):
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def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi=math.pi/4, a='default'):
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import numpy as np
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import cmath
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import math
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if a == 'default':
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a=1/math.sqrt(3)
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h0 = np.zeros((2, 2), dtype=complex) # mass term
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h1 = np.zeros((2, 2), dtype=complex) # nearest hopping
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h2 = np.zeros((2, 2), dtype=complex) # next nearest hopping
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@ -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
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return hamiltonian
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# 准一维Haldane模型条带的哈密顿量
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import math
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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):
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def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi='default', period=0):
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import numpy as np
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import cmath
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import math
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if phi == 'default':
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phi=math.pi/4
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h00 = np.zeros((4*N, 4*N), dtype=complex) # hopping in a unit cell
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h01 = np.zeros((4*N, 4*N), dtype=complex) # hopping between unit cells
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for i in range(N):
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@ -2422,11 +2426,15 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_ca
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return chern_number
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# 通过高效法计算贝利曲率
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import math
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def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
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def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
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import numpy as np
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import cmath
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import guan
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import math
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if k_min == 'default':
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k_min = -math.pi
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if k_max == 'default':
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k_max=math.pi
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if np.array(hamiltonian_function(0, 0)).shape==():
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dim = 1
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else:
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@ -2464,10 +2472,14 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=
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return k_array, berry_curvature_array
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# 通过高效法计算贝利曲率（可计算简并的情况）
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import math
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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):
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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):
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import numpy as np
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import cmath
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import math
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if k_min == 'default':
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k_min = -math.pi
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if k_max == 'default':
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k_max=math.pi
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delta = (k_max-k_min)/precision
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k_array = np.arange(k_min, k_max, delta)
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berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex)
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@ -2538,9 +2550,13 @@ def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamilton
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return k_array, berry_curvature_array
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# 通过Wilson loop方法计算贝里曲率
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import math
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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):
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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):
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import numpy as np
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import math
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if k_min == 'default':
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k_min = -math.pi
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if k_max == 'default':
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k_max=math.pi
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if np.array(hamiltonian_function(0, 0)).shape==():
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dim = 1
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else:
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@ -2590,9 +2606,13 @@ def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math
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return k_array, berry_curvature_array
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# 通过Wilson loop方法计算贝里曲率（可计算简并的情况）
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import math
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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):
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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):
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import numpy as np
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import math
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if k_min == 'default':
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k_min = -math.pi
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if k_max == 'default':
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k_max=math.pi
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delta = (k_max-k_min)/precision_of_plaquettes
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k_array = np.arange(k_min, k_max, delta)
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berry_curvature_array = np.zeros((k_array.shape[0], k_array.shape[0]), dtype=complex)
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@ -2700,10 +2720,14 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
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return chern_number
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# 计算Wilson loop
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import math
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def calculate_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0):
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def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
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import numpy as np
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import guan
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import math
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if k_min == 'default':
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k_min = -math.pi
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if k_max == 'default':
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k_max=math.pi
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k_array = np.linspace(k_min, k_max, precision)
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dim = np.array(hamiltonian_function(0)).shape[0]
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wilson_loop_array = np.ones(dim, dtype=complex)
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