guanjihuan/py.guanjihuan.com
1
1
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

0.1.48 增加装饰器

Browse Source
This commit is contained in:
guanjihuan
2023-11-27 16:42:45 +08:00
parent 206fcda152
commit 7a9b86f791
13 changed files with 271 additions and 379 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
PyPI/setup.cfg
View File

@@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.1.47
version = 0.1.48
author = guanjihuan
author_email = guanjihuan@163.com
description = An open source python package

2
PyPI/src/guan.egg-info/PKG-INFO
View File

@@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: guan
Version: 0.1.47
Version: 0.1.48
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

34
PyPI/src/guan/Fourier_transform.py
View File

@@ -1,17 +1,18 @@
# Module: Fourier_transform
import guan
# 通过元胞和跃迁项得到一维的哈密顿量（需要输入k值）
@guan.function_decorator
def one_dimensional_fourier_transform(k, unit_cell, hopping):
import numpy as np
import cmath
unit_cell = np.array(unit_cell)
hopping = np.array(hopping)
hamiltonian = unit_cell+hopping*cmath.exp(1j*k)+hopping.transpose().conj()*cmath.exp(-1j*k)
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 通过元胞和跃迁项得到二维方格子的哈密顿量（需要输入k值）
@guan.function_decorator
def two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell, hopping_1, hopping_2):
import numpy as np
import cmath
@@ -19,11 +20,10 @@ def two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell, hopp
hopping_1 = np.array(hopping_1)
hopping_2 = np.array(hopping_2)
hamiltonian = unit_cell+hopping_1*cmath.exp(1j*k1)+hopping_1.transpose().conj()*cmath.exp(-1j*k1)+hopping_2*cmath.exp(1j*k2)+hopping_2.transpose().conj()*cmath.exp(-1j*k2)
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 通过元胞和跃迁项得到三维立方格子的哈密顿量（需要输入k值）
@guan.function_decorator
def three_dimensional_fourier_transform_for_cubic_lattice(k1, k2, k3, unit_cell, hopping_1, hopping_2, hopping_3):
import numpy as np
import cmath
@@ -32,43 +32,41 @@ def three_dimensional_fourier_transform_for_cubic_lattice(k1, k2, k3, unit_cell,
hopping_2 = np.array(hopping_2)
hopping_3 = np.array(hopping_3)
hamiltonian = unit_cell+hopping_1*cmath.exp(1j*k1)+hopping_1.transpose().conj()*cmath.exp(-1j*k1)+hopping_2*cmath.exp(1j*k2)+hopping_2.transpose().conj()*cmath.exp(-1j*k2)+hopping_3*cmath.exp(1j*k3)+hopping_3.transpose().conj()*cmath.exp(-1j*k3)
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 通过元胞和跃迁项得到一维的哈密顿量（返回的哈密顿量为携带k的函数）
@guan.function_decorator
def one_dimensional_fourier_transform_with_k(unit_cell, hopping):
import functools
import guan
hamiltonian_function = functools.partial(guan.one_dimensional_fourier_transform, unit_cell=unit_cell, hopping=hopping)
guan.statistics_of_guan_package()
return hamiltonian_function
# 通过元胞和跃迁项得到二维方格子的哈密顿量（返回的哈密顿量为携带k的函数）
@guan.function_decorator
def two_dimensional_fourier_transform_for_square_lattice_with_k1_k2(unit_cell, hopping_1, hopping_2):
import functools
import guan
hamiltonian_function = functools.partial(guan.two_dimensional_fourier_transform_for_square_lattice, unit_cell=unit_cell, hopping_1=hopping_1, hopping_2=hopping_2)
guan.statistics_of_guan_package()
return hamiltonian_function
# 通过元胞和跃迁项得到三维立方格子的哈密顿量（返回的哈密顿量为携带k的函数）
@guan.function_decorator
def three_dimensional_fourier_transform_for_cubic_lattice_with_k1_k2_k3(unit_cell, hopping_1, hopping_2, hopping_3):
import functools
import guan
hamiltonian_function = functools.partial(guan.three_dimensional_fourier_transform_for_cubic_lattice, unit_cell=unit_cell, hopping_1=hopping_1, hopping_2=hopping_2, hopping_3=hopping_3)
guan.statistics_of_guan_package()
return hamiltonian_function
# 由实空间格矢得到倒空间格矢（一维）
@guan.function_decorator
def calculate_one_dimensional_reciprocal_lattice_vector(a1):
import numpy as np
b1 = 2*np.pi/a1
import guan
guan.statistics_of_guan_package()
return b1
# 由实空间格矢得到倒空间格矢（二维）
@guan.function_decorator
def calculate_two_dimensional_reciprocal_lattice_vectors(a1, a2):
import numpy as np
a1 = np.array(a1)
@@ -80,11 +78,10 @@ def calculate_two_dimensional_reciprocal_lattice_vectors(a1, a2):
b2 = 2*np.pi*np.cross(a3, a1)/np.dot(a1, np.cross(a2, a3))
b1 = np.delete(b1, 2)
b2 = np.delete(b2, 2)
import guan
guan.statistics_of_guan_package()
return b1, b2
# 由实空间格矢得到倒空间格矢（三维）
@guan.function_decorator
def calculate_three_dimensional_reciprocal_lattice_vectors(a1, a2, a3):
import numpy as np
a1 = np.array(a1)
@@ -93,19 +90,17 @@ def calculate_three_dimensional_reciprocal_lattice_vectors(a1, a2, a3):
b1 = 2*np.pi*np.cross(a2, a3)/np.dot(a1, np.cross(a2, a3))
b2 = 2*np.pi*np.cross(a3, a1)/np.dot(a1, np.cross(a2, a3))
b3 = 2*np.pi*np.cross(a1, a2)/np.dot(a1, np.cross(a2, a3))
import guan
guan.statistics_of_guan_package()
return b1, b2, b3
# 由实空间格矢得到倒空间格矢（一维），这里为符号运算
@guan.function_decorator
def calculate_one_dimensional_reciprocal_lattice_vector_with_sympy(a1):
import sympy
b1 = 2*sympy.pi/a1
import guan
guan.statistics_of_guan_package()
return b1
# 由实空间格矢得到倒空间格矢（二维），这里为符号运算
@guan.function_decorator
def calculate_two_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2):
import sympy
a1 = sympy.Matrix(1, 3, [a1[0], a1[1], 0])
@@ -117,11 +112,10 @@ def calculate_two_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2):
b2 = 2*sympy.pi*cross_a3_a1/a1.dot(cross_a2_a3)
b1 = sympy.Matrix(1, 2, [b1[0], b1[1]])
b2 = sympy.Matrix(1, 2, [b2[0], b2[1]])
import guan
guan.statistics_of_guan_package()
return b1, b2
# 由实空间格矢得到倒空间格矢（三维），这里为符号运算
@guan.function_decorator
def calculate_three_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2, a3):
import sympy
cross_a2_a3 = a2.cross(a3)
@@ -130,6 +124,4 @@ def calculate_three_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2, a3
b1 = 2*sympy.pi*cross_a2_a3/a1.dot(cross_a2_a3)
b2 = 2*sympy.pi*cross_a3_a1/a1.dot(cross_a2_a3)
b3 = 2*sympy.pi*cross_a1_a2/a1.dot(cross_a2_a3)
import guan
guan.statistics_of_guan_package()
return b1, b2, b3

32
PyPI/src/guan/Green_functions.py
View File

@@ -1,6 +1,8 @@
# Module: Green_functions
import guan
# 输入哈密顿量，得到格林函数
@guan.function_decorator
def green_function(fermi_energy, hamiltonian, broadening, self_energy=0):
import numpy as np
if np.array(hamiltonian).shape==():
@@ -8,11 +10,10 @@ def green_function(fermi_energy, hamiltonian, broadening, self_energy=0):
else:
dim = np.array(hamiltonian).shape[0]
green = np.linalg.inv((fermi_energy+broadening*1j)*np.eye(dim)-hamiltonian-self_energy)
import guan
guan.statistics_of_guan_package()
return green
# 在Dyson方程中的一个中间格林函数G_{nn}^{n}
@guan.function_decorator
def green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening, self_energy=0):
import numpy as np
h01 = np.array(h01)
@@ -21,36 +22,32 @@ def green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening, se
else:
dim = np.array(h00).shape[0]
green_nn_n = np.linalg.inv((fermi_energy+broadening*1j)*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n_minus), h01)-self_energy)
import guan
guan.statistics_of_guan_package()
return green_nn_n
# 在Dyson方程中的一个中间格林函数G_{in}^{n}
@guan.function_decorator
def green_function_in_n(green_in_n_minus, h01, green_nn_n):
import numpy as np
green_in_n = np.dot(np.dot(green_in_n_minus, h01), green_nn_n)
import guan
guan.statistics_of_guan_package()
return green_in_n
# 在Dyson方程中的一个中间格林函数G_{ni}^{n}
@guan.function_decorator
def green_function_ni_n(green_nn_n, h01, green_ni_n_minus):
import numpy as np
h01 = np.array(h01)
green_ni_n = np.dot(np.dot(green_nn_n, h01.transpose().conj()), green_ni_n_minus)
import guan
guan.statistics_of_guan_package()
return green_ni_n
# 在Dyson方程中的一个中间格林函数G_{ii}^{n}
@guan.function_decorator
def green_function_ii_n(green_ii_n_minus, green_in_n_minus, h01, green_nn_n, green_ni_n_minus):
import numpy as np
green_ii_n = green_ii_n_minus+np.dot(np.dot(np.dot(np.dot(green_in_n_minus, h01), green_nn_n), h01.transpose().conj()),green_ni_n_minus)
import guan
guan.statistics_of_guan_package()
return green_ii_n
# 计算转移矩阵（该矩阵可以用来计算表面格林函数）
@guan.function_decorator
def transfer_matrix(fermi_energy, h00, h01):
import numpy as np
h01 = np.array(h01)
@@ -63,11 +60,10 @@ def transfer_matrix(fermi_energy, h00, h01):
transfer[0:dim, dim:2*dim] = np.dot(-1*np.linalg.inv(h01), h01.transpose().conj())
transfer[dim:2*dim, 0:dim] = np.identity(dim)
transfer[dim:2*dim, dim:2*dim] = 0
import guan
guan.statistics_of_guan_package()
return transfer
# 计算电极的表面格林函数
@guan.function_decorator
def surface_green_function_of_lead(fermi_energy, h00, h01):
import numpy as np
h01 = np.array(h01)
@@ -90,11 +86,10 @@ def surface_green_function_of_lead(fermi_energy, h00, h01):
s4 = temp[0:dim, dim:2*dim]
right_lead_surface = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01, s2), np.linalg.inv(s1)))
left_lead_surface = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), s3), np.linalg.inv(s4)))
import guan
guan.statistics_of_guan_package()
return right_lead_surface, left_lead_surface
# 计算电极的自能（基于Dyson方程的小矩阵形式）
@guan.function_decorator
def self_energy_of_lead(fermi_energy, h00, h01):
import numpy as np
import guan
@@ -104,10 +99,10 @@ def self_energy_of_lead(fermi_energy, h00, h01):
left_self_energy = np.dot(np.dot(h01.transpose().conj(), left_lead_surface), h01)
gamma_right = (right_self_energy - right_self_energy.transpose().conj())*1j
gamma_left = (left_self_energy - left_self_energy.transpose().conj())*1j
guan.statistics_of_guan_package()
return right_self_energy, left_self_energy, gamma_right, gamma_left
# 计算电极的自能（基于中心区整体的大矩阵形式）
@guan.function_decorator
def self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR):
import numpy as np
import guan
@@ -118,10 +113,10 @@ def self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR):
left_self_energy = np.dot(np.dot(h_LC.transpose().conj(), left_lead_surface), h_LC)
gamma_right = (right_self_energy - right_self_energy.transpose().conj())*1j
gamma_left = (left_self_energy - left_self_energy.transpose().conj())*1j
guan.statistics_of_guan_package()
return right_self_energy, left_self_energy, gamma_right, gamma_left
# 计算电极的自能（基于中心区整体的大矩阵形式，可适用于多端电导的计算）
@guan.function_decorator
def self_energy_of_lead_with_h_lead_to_center(fermi_energy, h00, h01, h_lead_to_center):
import numpy as np
import guan
@@ -129,25 +124,24 @@ def self_energy_of_lead_with_h_lead_to_center(fermi_energy, h00, h01, h_lead_to_
right_lead_surface, left_lead_surface = guan.surface_green_function_of_lead(fermi_energy, h00, h01)
self_energy = np.dot(np.dot(h_lead_to_center.transpose().conj(), right_lead_surface), h_lead_to_center)
gamma = (self_energy - self_energy.transpose().conj())*1j
guan.statistics_of_guan_package()
return self_energy, gamma
# 计算考虑电极自能后的中心区的格林函数
@guan.function_decorator
def green_function_with_leads(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian):
import numpy as np
import guan
dim = np.array(center_hamiltonian).shape[0]
right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
green = np.linalg.inv(fermi_energy*np.identity(dim)-center_hamiltonian-left_self_energy-right_self_energy)
guan.statistics_of_guan_package()
return green, gamma_right, gamma_left
# 计算用于计算局域电流的格林函数G_n
@guan.function_decorator
def electron_correlation_function_green_n_for_local_current(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian):
import numpy as np
import guan
right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
green = guan.green_function(fermi_energy, center_hamiltonian, broadening=0, self_energy=left_self_energy+right_self_energy)
G_n = np.imag(np.dot(np.dot(green, gamma_left), green.transpose().conj()))
guan.statistics_of_guan_package()
return G_n

76
PyPI/src/guan/Hamiltonian_of_examples.py
View File

@@ -1,6 +1,8 @@
# Module: Hamiltonian_of_examples
import guan
# 构建一维的有限尺寸体系哈密顿量（可设置是否为周期边界条件）
@guan.function_decorator
def hamiltonian_of_finite_size_system_along_one_direction(N, on_site=0, hopping=1, period=0):
import numpy as np
on_site = np.array(on_site)
@@ -18,11 +20,10 @@ def hamiltonian_of_finite_size_system_along_one_direction(N, on_site=0, hopping=
if period == 1:
hamiltonian[(N-1)*dim+0:(N-1)*dim+dim, 0:dim] = hopping
hamiltonian[0:dim, (N-1)*dim+0:(N-1)*dim+dim] = hopping.transpose().conj()
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 构建二维的方格子有限尺寸体系哈密顿量（可设置是否为周期边界条件）
@guan.function_decorator
def hamiltonian_of_finite_size_system_along_two_directions_for_square_lattice(N1, N2, on_site=0, hopping_1=1, hopping_2=1, period_1=0, period_2=0):
import numpy as np
on_site = np.array(on_site)
@@ -52,11 +53,10 @@ def hamiltonian_of_finite_size_system_along_two_directions_for_square_lattice(N1
for i1 in range(N1):
hamiltonian[i1*N2*dim+(N2-1)*dim+0:i1*N2*dim+(N2-1)*dim+dim, i1*N2*dim+0:i1*N2*dim+dim] = hopping_2
hamiltonian[i1*N2*dim+0:i1*N2*dim+dim, i1*N2*dim+(N2-1)*dim+0:i1*N2*dim+(N2-1)*dim+dim] = hopping_2.transpose().conj()
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 构建三维的立方格子有限尺寸体系哈密顿量（可设置是否为周期边界条件）
@guan.function_decorator
def hamiltonian_of_finite_size_system_along_three_directions_for_cubic_lattice(N1, N2, N3, on_site=0, hopping_1=1, hopping_2=1, hopping_3=1, period_1=0, period_2=0, period_3=0):
import numpy as np
on_site = np.array(on_site)
@@ -102,11 +102,10 @@ def hamiltonian_of_finite_size_system_along_three_directions_for_cubic_lattice(N
for i2 in range(N2):
hamiltonian[i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+0:i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+dim, i1*N2*N3*dim+i2*N3*dim+0:i1*N2*N3*dim+i2*N3*dim+dim] = hopping_3
hamiltonian[i1*N2*N3*dim+i2*N3*dim+0:i1*N2*N3*dim+i2*N3*dim+dim, i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+0:i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+dim] = hopping_3.transpose().conj()
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 构建有限尺寸的SSH模型哈密顿量
@guan.function_decorator
def hamiltonian_of_finite_size_ssh_model(N, v=0.6, w=1, onsite_1=0, onsite_2=0, period=1):
import numpy as np
hamiltonian = np.zeros((2*N, 2*N))
@@ -121,11 +120,10 @@ def hamiltonian_of_finite_size_ssh_model(N, v=0.6, w=1, onsite_1=0, onsite_2=0,
if period==1:
hamiltonian[0, 2*N-1] = w
hamiltonian[2*N-1, 0] = w
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 获取Zigzag边的石墨烯条带的元胞间跃迁
@guan.function_decorator
def get_hopping_term_of_graphene_ribbon_along_zigzag_direction(N, eta=0):
import numpy as np
hopping = np.zeros((4*N, 4*N), dtype=complex)
@@ -136,11 +134,10 @@ def get_hopping_term_of_graphene_ribbon_along_zigzag_direction(N, eta=0):
hopping[4*i0+3, 4*i0+3] = eta
hopping[4*i0+1, 4*i0+0] = 1
hopping[4*i0+2, 4*i0+3] = 1
import guan
guan.statistics_of_guan_package()
return hopping
# 构建有限尺寸的石墨烯哈密顿量（可设置是否为周期边界条件）
@guan.function_decorator
def hamiltonian_of_finite_size_system_along_two_directions_for_graphene(N1, N2, period_1=0, period_2=0):
import numpy as np
import guan
@@ -149,10 +146,10 @@ def hamiltonian_of_finite_size_system_along_two_directions_for_graphene(N1, N2,
hopping_2 = np.zeros((4, 4), dtype=complex)
hopping_2[3, 0] = 1
hamiltonian = guan.hamiltonian_of_finite_size_system_along_two_directions_for_square_lattice(N1, N2, on_site, hopping_1, hopping_2, period_1, period_2)
guan.statistics_of_guan_package()
return hamiltonian
# 获取石墨烯有效模型沿着x方向的在位能和跃迁项（其中，动量qy为参数）
@guan.function_decorator
def get_onsite_and_hopping_terms_of_2d_effective_graphene_along_one_direction(qy, t=1, staggered_potential=0, eta=0, valley_index=0):
import numpy as np
constant = -np.sqrt(3)/2
@@ -170,11 +167,10 @@ def get_onsite_and_hopping_terms_of_2d_effective_graphene_along_one_direction(qy
else:
h01[0, 1] = constant*t*(1j/2)
h01[1, 0] = constant*t*(1j/2)
import guan
guan.statistics_of_guan_package()
return h00, h01
# 获取BHZ模型的在位能和跃迁项
@guan.function_decorator
def get_onsite_and_hopping_terms_of_bhz_model(A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01, a=1):
import numpy as np
E_s = C+M-4*(D+B)/(a**2)
@@ -205,11 +201,10 @@ def get_onsite_and_hopping_terms_of_bhz_model(A=0.3645/5, B=-0.686/25, C=0, D=-0
H2[1, 0] = 1j*np.conj(V_sp)
H2[2, 3] = -1j*np.conj(V_sp)
H2[3, 2] = -1j*V_sp
import guan
guan.statistics_of_guan_package()
return H0, H1, H2
# 获取半个BHZ模型的在位能和跃迁项（自旋向上）
@guan.function_decorator
def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_up(A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01, a=1):
import numpy as np
E_s = C+M-4*(D+B)/(a**2)
@@ -230,11 +225,10 @@ def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_up(A=0.3645/5, B=-0.
H2[1, 1] = V_pp
H2[0, 1] = 1j*V_sp
H2[1, 0] = 1j*np.conj(V_sp)
import guan
guan.statistics_of_guan_package()
return H0, H1, H2
# 获取半个BHZ模型的在位能和跃迁项（自旋向下）
@guan.function_decorator
def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_down(A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01, a=1):
import numpy as np
E_s = C+M-4*(D+B)/(a**2)
@@ -255,25 +249,24 @@ def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_down(A=0.3645/5, B=-
H2[1, 1] = V_pp
H2[0, 1] = -1j*np.conj(V_sp)
H2[1, 0] = -1j*V_sp
import guan
guan.statistics_of_guan_package()
return H0, H1, H2
# 一维链的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_simple_chain(k):
import guan
hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=0, hopping=1)
guan.statistics_of_guan_package()
return hamiltonian
# 二维方格子的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_square_lattice(k1, k2):
import guan
hamiltonian = guan.two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell=0, hopping_1=1, hopping_2=1)
guan.statistics_of_guan_package()
return hamiltonian
# 准一维方格子条带的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_square_lattice_in_quasi_one_dimension(k, N=10, period=0):
import numpy as np
import guan
@@ -288,28 +281,27 @@ def hamiltonian_of_square_lattice_in_quasi_one_dimension(k, N=10, period=0):
for i in range(N):
h01[i, i] = 1
hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=h00, hopping=h01)
guan.statistics_of_guan_package()
return hamiltonian
# 三维立方格子的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_cubic_lattice(k1, k2, k3):
import guan
hamiltonian = guan.three_dimensional_fourier_transform_for_cubic_lattice(k1, k2, k3, unit_cell=0, hopping_1=1, hopping_2=1, hopping_3=1)
guan.statistics_of_guan_package()
return hamiltonian
# SSH模型的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_ssh_model(k, v=0.6, w=1):
import numpy as np
import cmath
hamiltonian = np.zeros((2, 2), dtype=complex)
hamiltonian[0,1] = v+w*cmath.exp(-1j*k)
hamiltonian[1,0] = v+w*cmath.exp(1j*k)
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 石墨烯的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
import numpy as np
import cmath
@@ -323,11 +315,10 @@ def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
h1[1, 0] = t*(cmath.exp(1j*k2*a)+cmath.exp(1j*math.sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j/2*k2*a))
h1[0, 1] = h1[1, 0].conj()
hamiltonian = h0 + h1
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 石墨烯有效模型的哈密顿量（倒空间）
@guan.function_decorator
def effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley_index=0):
import numpy as np
hamiltonian = np.zeros((2, 2), dtype=complex)
@@ -340,11 +331,10 @@ def effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley
else:
hamiltonian[0, 1] = constant*t*(-qx-1j*qy)
hamiltonian[1, 0] = constant*t*(-qx+1j*qy)
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 石墨烯有效模型离散化后的哈密顿量（倒空间）
@guan.function_decorator
def effective_hamiltonian_of_graphene_after_discretization(qx, qy, t=1, staggered_potential=0, valley_index=0):
import numpy as np
hamiltonian = np.zeros((2, 2), dtype=complex)
@@ -357,11 +347,10 @@ def effective_hamiltonian_of_graphene_after_discretization(qx, qy, t=1, staggere
else:
hamiltonian[0, 1] = constant*t*(-np.sin(qx)-1j*np.sin(qy))
hamiltonian[1, 0] = constant*t*(-np.sin(qx)+1j*np.sin(qy))
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 准一维Zigzag边石墨烯条带的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1, period=0):
import numpy as np
import guan
@@ -388,10 +377,10 @@ def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1
h01[i*4+1, i*4+0] = t
h01[i*4+2, i*4+3] = t
hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=h00, hopping=h01)
guan.statistics_of_guan_package()
return hamiltonian
# Haldane模型的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi='default', a='default'):
import numpy as np
import cmath
@@ -410,11 +399,10 @@ def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi='default', a='
h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*math.sqrt(3)*k1*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j*3/2*k2*a))
h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*math.sqrt(3)*k1*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j*3/2*k2*a))
hamiltonian = h0 + h1 + h2 + h2.transpose().conj()
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 准一维Haldane模型条带的哈密顿量（倒空间）
@guan.function_decorator
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
@@ -466,11 +454,10 @@ def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2
for i in range(N-1):
h01[i*4+2, (i+1)*4+0] = t2*cmath.exp(-1j*phi)
hamiltonian = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 一个量子反常霍尔效应的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1):
import numpy as np
import math
@@ -479,11 +466,10 @@ def hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1):
hamiltonian[1, 0] = 2*t1*math.cos(k1)+1j*2*t1*math.cos(k2)
hamiltonian[0, 0] = m+2*t3*math.sin(k1)+2*t3*math.sin(k2)+2*t2*math.cos(k1+k2)
hamiltonian[1, 1] = -(m+2*t3*math.sin(k1)+2*t3*math.sin(k2)+2*t2*math.cos(k1+k2))
import guan
guan.statistics_of_guan_package()
return hamiltonian
# BHZ模型的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_bhz_model(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01):
import numpy as np
import math
@@ -499,11 +485,10 @@ def hamiltonian_of_bhz_model(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25,
hamiltonian[3, 3] = varepsilon-d3
hamiltonian[2, 3] = -d1_d2
hamiltonian[3, 2] = -np.conj(d1_d2)
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 半BHZ模型的哈密顿量（自旋向上）（倒空间）
@guan.function_decorator
def hamiltonian_of_half_bhz_model_for_spin_up(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01):
import numpy as np
import math
@@ -515,11 +500,10 @@ def hamiltonian_of_half_bhz_model_for_spin_up(kx, ky, A=0.3645/5, B=-0.686/25, C
hamiltonian[1, 1] = varepsilon-d3
hamiltonian[0, 1] = np.conj(d1_d2)
hamiltonian[1, 0] = d1_d2
import guan
guan.statistics_of_guan_package()
return hamiltonian
# 半BHZ模型的哈密顿量（自旋向下）（倒空间）
@guan.function_decorator
def hamiltonian_of_half_bhz_model_for_spin_down(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01):
import numpy as np
import math
@@ -531,11 +515,10 @@ def hamiltonian_of_half_bhz_model_for_spin_down(kx, ky, A=0.3645/5, B=-0.686/25,
hamiltonian[1, 1] = varepsilon-d3
hamiltonian[0, 1] = -d1_d2
hamiltonian[1, 0] = -np.conj(d1_d2)
import guan
guan.statistics_of_guan_package()
return hamiltonian
# BBH模型的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_bbh_model(kx, ky, gamma_x=0.5, gamma_y=0.5, lambda_x=1, lambda_y=1):
import numpy as np
import cmath
@@ -552,11 +535,10 @@ def hamiltonian_of_bbh_model(kx, ky, gamma_x=0.5, gamma_y=0.5, lambda_x=1, lambd
hamiltonian[3, 1] = np.conj(hamiltonian[1, 3])
hamiltonian[3, 0] = np.conj(hamiltonian[0, 3])
hamiltonian[2, 1] = np.conj(hamiltonian[1, 2])
import guan
guan.statistics_of_guan_package()
return hamiltonian
# Kagome模型的哈密顿量（倒空间）
@guan.function_decorator
def hamiltonian_of_kagome_lattice(kx, ky, t=1):
import numpy as np
import math
@@ -569,6 +551,4 @@ def hamiltonian_of_kagome_lattice(kx, ky, t=1):
hamiltonian[1, 2] = 2*math.cos(k3_dot_a3)
hamiltonian = hamiltonian + hamiltonian.transpose().conj()
hamiltonian = -t*hamiltonian
import guan
guan.statistics_of_guan_package()
return hamiltonian

8
PyPI/src/guan/__init__.py
View File

@@ -1,5 +1,13 @@
# Guan is an open-source python package developed and maintained by https://www.guanjihuan.com/about (Ji-Huan Guan, 关济寰). The primary location of this package is on website https://py.guanjihuan.com. The GitHub location of this package is on https://github.com/guanjihuan/py.guanjihuan.com.
# 函数的装饰器，用于软件包的统计
def function_decorator(func):
def wrapper():
func()
import guan
guan.statistics_of_guan_package(func.__name__)
return wrapper
from .basic_functions import *
from .Fourier_transform import *
from .Hamiltonian_of_examples import *

32
PyPI/src/guan/band_structures_and_wave_functions.py
View File

@@ -1,17 +1,18 @@
# Module: band_structures_and_wave_functions
import guan
# 计算哈密顿量的本征值
@guan.function_decorator
def calculate_eigenvalue(hamiltonian):
import numpy as np
if np.array(hamiltonian).shape==():
eigenvalue = np.real(hamiltonian)
else:
eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
import guan
guan.statistics_of_guan_package()
return eigenvalue
# 输入哈密顿量函数（带一组参数），计算一组参数下的本征值，返回本征值向量组
@guan.function_decorator
def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print_show=0):
import numpy as np
dim_x = np.array(x_array).shape[0]
@@ -32,11 +33,10 @@ def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print
eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
eigenvalue_array[i0, :] = eigenvalue
i0 += 1
import guan
guan.statistics_of_guan_package()
return eigenvalue_array
# 输入哈密顿量函数（带两组参数），计算两组参数下的本征值，返回本征值向量组
@guan.function_decorator
def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_function, print_show=0, print_show_more=0):
import numpy as np
dim_x = np.array(x_array).shape[0]
@@ -67,19 +67,17 @@ def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_funct
eigenvalue_array[i0, j0, :] = eigenvalue
j0 += 1
i0 += 1
import guan
guan.statistics_of_guan_package()
return eigenvalue_array
# 计算哈密顿量的本征矢
@guan.function_decorator
def calculate_eigenvector(hamiltonian):
import numpy as np
eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
import guan
guan.statistics_of_guan_package()
return eigenvector
# 通过二分查找的方法获取和相邻波函数一样规范的波函数
@guan.function_decorator
def find_vector_with_the_same_gauge_with_binary_search(vector_target, vector_ref, show_error=1, show_times=0, show_phase=0, n_test=1000, precision=1e-6):
import numpy as np
import cmath
@@ -116,11 +114,10 @@ def find_vector_with_the_same_gauge_with_binary_search(vector_target, vector_ref
vector_target = vector_target*cmath.exp(1j*phase)
if show_phase==1:
print('Phase=', phase)
import guan
guan.statistics_of_guan_package()
return vector_target
# 通过使得波函数的一个非零分量为实数，得到固定规范的波函数
@guan.function_decorator
def find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=0.005, index=None):
import numpy as np
import cmath
@@ -136,11 +133,10 @@ def find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=
vector = vector*cmath.exp(1j*phase)
if np.real(vector[index]) < 0:
vector = -vector
import guan
guan.statistics_of_guan_package()
return vector
# 通过使得波函数的一个非零分量为实数，得到固定规范的波函数（在一组波函数中选取最大的那个分量）
@guan.function_decorator
def find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array, precision=0.005):
import numpy as np
import guan
@@ -151,10 +147,10 @@ def find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array
index = np.argmax(np.abs(vector_sum))
for i0 in range(Num_k):
vector_array[i0] = guan.find_vector_with_fixed_gauge_by_making_one_component_real(vector_array[i0], precision=precision, index=index)
guan.statistics_of_guan_package()
return vector_array
# 旋转两个简并的波函数（说明：参数比较多，算法效率不高）
@guan.function_decorator
def rotation_of_degenerate_vectors(vector1, vector2, index1=None, index2=None, precision=0.01, criterion=0.01, show_theta=0):
import numpy as np
import math
@@ -181,11 +177,10 @@ def rotation_of_degenerate_vectors(vector1, vector2, index1=None, index2=None, p
break
if np.abs(vector1_test[index2])<criterion and np.abs(vector2_test[index1])<criterion:
break
import guan
guan.statistics_of_guan_package()
return vector1, vector2
# 旋转两个简并的波函数向量组（说明：参数比较多，算法效率不高）
@guan.function_decorator
def rotation_of_degenerate_vectors_array(vector1_array, vector2_array, precision=0.01, criterion=0.01, show_theta=0):
import numpy as np
import guan
@@ -200,10 +195,10 @@ def rotation_of_degenerate_vectors_array(vector1_array, vector2_array, precision
index2 = np.argmax(np.abs(vector2_sum))
for i0 in range(Num_k):
vector1_array[i0], vector2_array[i0] = guan.rotation_of_degenerate_vectors(vector1=vector1_array[i0], vector2=vector2_array[i0], index1=index1, index2=index2, precision=precision, criterion=criterion, show_theta=show_theta)
guan.statistics_of_guan_package()
return vector1_array, vector2_array
# 在一组数据中找到数值相近的数
@guan.function_decorator
def find_close_values_in_one_array(array, precision=1e-2):
new_array = []
i0 = 0
@@ -214,11 +209,10 @@ def find_close_values_in_one_array(array, precision=1e-2):
new_array.append([a1, a2])
j0 +=1
i0 += 1
import guan
guan.statistics_of_guan_package()
return new_array
# 寻找能带的简并点
@guan.function_decorator
def find_degenerate_points(k_array, eigenvalue_array, precision=1e-2):
import guan
degenerate_k_array = []
@@ -230,6 +224,4 @@ def find_degenerate_points(k_array, eigenvalue_array, precision=1e-2):
degenerate_k_array.append(k)
degenerate_eigenvalue_array.append(degenerate_points)
i0 += 1
import guan
guan.statistics_of_guan_package()
return degenerate_k_array, degenerate_eigenvalue_array

48
PyPI/src/guan/basic_functions.py
View File

@@ -1,129 +1,125 @@
# Module: basic_functions
import guan
# 测试
@guan.function_decorator
def test():
print('\nSuccess in the installation of Guan package!\n')
import guan
guan.statistics_of_guan_package()
# 泡利矩阵
@guan.function_decorator
def sigma_0():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.eye(2)
@guan.function_decorator
def sigma_x():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.array([[0, 1],[1, 0]])
@guan.function_decorator
def sigma_y():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.array([[0, -1j],[1j, 0]])
@guan.function_decorator
def sigma_z():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.array([[1, 0],[0, -1]])
# 泡利矩阵的张量积
@guan.function_decorator
def sigma_00():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_0(), guan.sigma_0())
@guan.function_decorator
def sigma_0x():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_0(), guan.sigma_x())
@guan.function_decorator
def sigma_0y():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_0(), guan.sigma_y())
@guan.function_decorator
def sigma_0z():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_0(), guan.sigma_z())
@guan.function_decorator
def sigma_x0():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_x(), guan.sigma_0())
@guan.function_decorator
def sigma_xx():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_x(), guan.sigma_x())
@guan.function_decorator
def sigma_xy():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_x(), guan.sigma_y())
@guan.function_decorator
def sigma_xz():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_x(), guan.sigma_z())
@guan.function_decorator
def sigma_y0():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_y(), guan.sigma_0())
@guan.function_decorator
def sigma_yx():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_y(), guan.sigma_x())
@guan.function_decorator
def sigma_yy():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_y(), guan.sigma_y())
@guan.function_decorator
def sigma_yz():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_y(), guan.sigma_z())
@guan.function_decorator
def sigma_z0():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_z(), guan.sigma_0())
@guan.function_decorator
def sigma_zx():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_z(), guan.sigma_x())
@guan.function_decorator
def sigma_zy():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_z(), guan.sigma_y())
@guan.function_decorator
def sigma_zz():
import numpy as np
import guan
guan.statistics_of_guan_package()
return np.kron(guan.sigma_z(), guan.sigma_z())

136
PyPI/src/guan/data_processing.py
View File

@@ -1,6 +1,8 @@
# Module: data_processing (including figure-plotting and file-reading/writing)
import guan
# 导入plt, fig, ax
@guan.function_decorator
def import_plt_and_start_fig_ax(adjust_bottom=0.2, adjust_left=0.2, labelsize=20):
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
@@ -9,11 +11,10 @@ def import_plt_and_start_fig_ax(adjust_bottom=0.2, adjust_left=0.2, labelsize=20
ax.tick_params(labelsize=labelsize)
labels = ax.get_xticklabels() + ax.get_yticklabels()
[label.set_fontname('Times New Roman') for label in labels]
import guan
guan.statistics_of_guan_package()
return plt, fig, ax
# 基于plt, fig, ax画图
@guan.function_decorator
def plot_without_starting_fig(plt, fig, ax, x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, style='', y_min=None, y_max=None, linewidth=None, markersize=None, color=None):
if color==None:
ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
@@ -28,10 +29,9 @@ def plot_without_starting_fig(plt, fig, ax, x_array, y_array, xlabel='x', ylabel
if y_max==None:
y_max=max(y_array)
ax.set_ylim(y_min, y_max)
import guan
guan.statistics_of_guan_package()
# 画图
@guan.function_decorator
def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2):
import guan
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
@@ -50,9 +50,9 @@ def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labels
if show == 1:
plt.show()
plt.close('all')
guan.statistics_of_guan_package()
# 一组横坐标数据，两组纵坐标数据画图
@guan.function_decorator
def plot_two_array(x_array, y1_array, y2_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, markersize_1=None, markersize_2=None, adjust_bottom=0.2, adjust_left=0.2):
import guan
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
@@ -76,9 +76,9 @@ def plot_two_array(x_array, y1_array, y2_array, xlabel='x', ylabel='y', title=''
if show == 1:
plt.show()
plt.close('all')
guan.statistics_of_guan_package()
# 两组横坐标数据，两组纵坐标数据画图
@guan.function_decorator
def plot_two_array_with_two_horizontal_array(x1_array, x2_array, y1_array, y2_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, markersize_1=None, markersize_2=None, adjust_bottom=0.2, adjust_left=0.2):
import guan
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
@@ -102,9 +102,9 @@ def plot_two_array_with_two_horizontal_array(x1_array, x2_array, y1_array, y2_ar
if show == 1:
plt.show()
plt.close('all')
guan.statistics_of_guan_package()
# 一组横坐标数据，三组纵坐标数据画图
@guan.function_decorator
def plot_three_array(x_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', style_3='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, linewidth_3=None,markersize_1=None, markersize_2=None, markersize_3=None, adjust_bottom=0.2, adjust_left=0.2):
import guan
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
@@ -131,9 +131,9 @@ def plot_three_array(x_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='
if show == 1:
plt.show()
plt.close('all')
guan.statistics_of_guan_package()
# 三组横坐标数据，三组纵坐标数据画图
@guan.function_decorator
def plot_three_array_with_three_horizontal_array(x1_array, x2_array, x3_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', style_3='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, linewidth_3=None,markersize_1=None, markersize_2=None, markersize_3=None, adjust_bottom=0.2, adjust_left=0.2):
import guan
plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
@@ -160,9 +160,9 @@ def plot_three_array_with_three_horizontal_array(x1_array, x2_array, x3_array, y
if show == 1:
plt.show()
plt.close('all')
guan.statistics_of_guan_package()
# 画三维图
@guan.function_decorator
def plot_3d_surface(x_array, y_array, matrix, xlabel='x', ylabel='y', zlabel='z', title='', fontsize=20, labelsize=15, show=1, save=0, filename='a', file_format='.jpg', dpi=300, z_min=None, z_max=None, rcount=100, ccount=100):
import numpy as np
import matplotlib.pyplot as plt
@@ -202,10 +202,9 @@ def plot_3d_surface(x_array, y_array, matrix, xlabel='x', ylabel='y', zlabel='z'
if show == 1:
plt.show()
plt.close('all')
import guan
guan.statistics_of_guan_package()
# 画Contour图
@guan.function_decorator
def plot_contour(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=15, cmap='jet', levels=None, show=1, save=0, filename='a', file_format='.jpg', dpi=300):
import numpy as np
import matplotlib.pyplot as plt
@@ -229,10 +228,9 @@ def plot_contour(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fon
if show == 1:
plt.show()
plt.close('all')
import guan
guan.statistics_of_guan_package()
# 画棋盘图/伪彩色图
@guan.function_decorator
def plot_pcolor(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=15, cmap='jet', levels=None, show=1, save=0, filename='a', file_format='.jpg', dpi=300):
import numpy as np
import matplotlib.pyplot as plt
@@ -256,10 +254,9 @@ def plot_pcolor(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', font
if show == 1:
plt.show()
plt.close('all')
import guan
guan.statistics_of_guan_package()
# 通过坐标画点和线
@guan.function_decorator
def draw_dots_and_lines(coordinate_array, draw_dots=1, draw_lines=1, max_distance=1.1, line_style='-k', linewidth=1, dot_style='ro', markersize=3, show=1, save=0, filename='a', file_format='.eps', dpi=300):
import numpy as np
import matplotlib.pyplot as plt
@@ -285,10 +282,9 @@ def draw_dots_and_lines(coordinate_array, draw_dots=1, draw_lines=1, max_distanc
plt.savefig(filename+file_format)
else:
plt.savefig(filename+file_format, dpi=dpi)
import guan
guan.statistics_of_guan_package()
# 合并两个图片
@guan.function_decorator
def combine_two_images(image_path_array, figsize=(16,8), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
import numpy as np
num = np.array(image_path_array).shape[0]
@@ -312,10 +308,9 @@ def combine_two_images(image_path_array, figsize=(16,8), show=0, save=1, filenam
if save == 1:
plt.savefig(filename+file_format, dpi=dpi)
plt.close('all')
import guan
guan.statistics_of_guan_package()
# 合并三个图片
@guan.function_decorator
def combine_three_images(image_path_array, figsize=(16,5), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
import numpy as np
num = np.array(image_path_array).shape[0]
@@ -343,10 +338,9 @@ def combine_three_images(image_path_array, figsize=(16,5), show=0, save=1, filen
if save == 1:
plt.savefig(filename+file_format, dpi=dpi)
plt.close('all')
import guan
guan.statistics_of_guan_package()
# 合并四个图片
@guan.function_decorator
def combine_four_images(image_path_array, figsize=(16,16), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
import numpy as np
num = np.array(image_path_array).shape[0]
@@ -378,10 +372,9 @@ def combine_four_images(image_path_array, figsize=(16,16), show=0, save=1, filen
if save == 1:
plt.savefig(filename+file_format, dpi=dpi)
plt.close('all')
import guan
guan.statistics_of_guan_package()
# 对某个目录中的txt文件批量读取和画图
@guan.function_decorator
def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
import re
import os
@@ -392,9 +385,9 @@ def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
filename = file[:-4]
x_array, y_array = guan.read_one_dimensional_data(filename=filename)
guan.plot(x_array, y_array, xlabel=xlabel, ylabel=ylabel, title=filename, show=0, save=1, filename=filename)
guan.statistics_of_guan_package()
# 将图片制作GIF动画
@guan.function_decorator
def make_gif(image_path_array, filename='a', duration=0.1):
import imageio
images = []
@@ -402,34 +395,30 @@ def make_gif(image_path_array, filename='a', duration=0.1):
im = imageio.imread(image_path)
images.append(im)
imageio.mimsave(filename+'.gif', images, 'GIF', duration=duration)
import guan
guan.statistics_of_guan_package()
# 选取Matplotlib颜色
@guan.function_decorator
def color_matplotlib():
color_array = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']
import guan
guan.statistics_of_guan_package()
return color_array
# 将变量存到文件
@guan.function_decorator
def dump_data(data, filename, file_format='.txt'):
import pickle
with open(filename+file_format, 'wb') as f:
pickle.dump(data, f)
import guan
guan.statistics_of_guan_package()
# 从文件中恢复数据到变量
@guan.function_decorator
def load_data(filename, file_format='.txt'):
import pickle
with open(filename+file_format, 'rb') as f:
data = pickle.load(f)
import guan
guan.statistics_of_guan_package()
return data
# 读取文件中的一维数据（一行一组x和y）
@guan.function_decorator
def read_one_dimensional_data(filename='a', file_format='.txt'):
import numpy as np
f = open(filename+file_format, 'r')
@@ -450,11 +439,10 @@ def read_one_dimensional_data(filename='a', file_format='.txt'):
y_array = [y_row]
else:
y_array = np.append(y_array, [y_row], axis=0)
import guan
guan.statistics_of_guan_package()
return x_array, y_array
# 读取文件中的一维数据（一行一组x和y）（支持复数形式）
@guan.function_decorator
def read_one_dimensional_complex_data(filename='a', file_format='.txt'):
import numpy as np
f = open(filename+file_format, 'r')
@@ -475,11 +463,10 @@ def read_one_dimensional_complex_data(filename='a', file_format='.txt'):
y_array = [y_row]
else:
y_array = np.append(y_array, [y_row], axis=0)
import guan
guan.statistics_of_guan_package()
return x_array, y_array
# 读取文件中的二维数据（第一行和第一列分别为横纵坐标）
@guan.function_decorator
def read_two_dimensional_data(filename='a', file_format='.txt'):
import numpy as np
f = open(filename+file_format, 'r')
@@ -506,11 +493,10 @@ def read_two_dimensional_data(filename='a', file_format='.txt'):
matrix = [matrix_row]
else:
matrix = np.append(matrix, [matrix_row], axis=0)
import guan
guan.statistics_of_guan_package()
return x_array, y_array, matrix
# 读取文件中的二维数据（第一行和第一列分别为横纵坐标）（支持复数形式）
@guan.function_decorator
def read_two_dimensional_complex_data(filename='a', file_format='.txt'):
import numpy as np
f = open(filename+file_format, 'r')
@@ -537,34 +523,30 @@ def read_two_dimensional_complex_data(filename='a', file_format='.txt'):
matrix = [matrix_row]
else:
matrix = np.append(matrix, [matrix_row], axis=0)
import guan
guan.statistics_of_guan_package()
return x_array, y_array, matrix
# 读取文件中的二维数据（不包括x和y）
@guan.function_decorator
def read_two_dimensional_data_without_xy_array(filename='a', file_format='.txt'):
import numpy as np
matrix = np.loadtxt(filename+file_format)
import guan
guan.statistics_of_guan_package()
return matrix
# 打开文件用于新增内容
@guan.function_decorator
def open_file(filename='a', file_format='.txt'):
f = open(filename+file_format, 'a', encoding='UTF-8')
import guan
guan.statistics_of_guan_package()
return f
# 在文件中写入一维数据（一行一组x和y）
@guan.function_decorator
def write_one_dimensional_data(x_array, y_array, filename='a', file_format='.txt'):
import guan
with open(filename+file_format, 'w', encoding='UTF-8') as f:
guan.write_one_dimensional_data_without_opening_file(x_array, y_array, f)
import guan
guan.statistics_of_guan_package()
# 在文件中写入一维数据（一行一组x和y）（需要输入已打开的文件）
@guan.function_decorator
def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
import numpy as np
x_array = np.array(x_array)
@@ -579,17 +561,16 @@ def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
f.write(str(y_array[i0, j0])+' ')
f.write('\n')
i0 += 1
import guan
guan.statistics_of_guan_package()
# 在文件中写入二维数据（第一行和第一列分别为横纵坐标）
@guan.function_decorator
def write_two_dimensional_data(x_array, y_array, matrix, filename='a', file_format='.txt'):
import guan
with open(filename+file_format, 'w', encoding='UTF-8') as f:
guan.write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f)
guan.statistics_of_guan_package()
# 在文件中写入二维数据（第一行和第一列分别为横纵坐标）（需要输入已打开的文件）
@guan.function_decorator
def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f):
import numpy as np
x_array = np.array(x_array)
@@ -608,26 +589,25 @@ def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f)
j0 += 1
f.write('\n')
i0 += 1
import guan
guan.statistics_of_guan_package()
# 在文件中写入二维数据（不包括x和y）
@guan.function_decorator
def write_two_dimensional_data_without_xy_array(matrix, filename='a', file_format='.txt'):
import guan
with open(filename+file_format, 'w', encoding='UTF-8') as f:
guan.write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f)
guan.statistics_of_guan_package()
# 在文件中写入二维数据（不包括x和y）（需要输入已打开的文件）
@guan.function_decorator
def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f):
for row in matrix:
for element in row:
f.write(str(element)+' ')
f.write('\n')
import guan
guan.statistics_of_guan_package()
# 以显示编号的样式，打印数组
@guan.function_decorator
def print_array_with_index(array, show_index=1, index_type=0):
if show_index==0:
for i0 in array:
@@ -643,10 +623,9 @@ def print_array_with_index(array, show_index=1, index_type=0):
for i0 in array:
index += 1
print(index, i0)
import guan
guan.statistics_of_guan_package()
# 获取目录中的所有文件名
@guan.function_decorator
def get_all_filenames_in_directory(directory='./', file_format=None):
import os
file_list = []
@@ -660,6 +639,7 @@ def get_all_filenames_in_directory(directory='./', file_format=None):
return file_list
# 读取文件夹中某种文本文件类型的文件路径和内容
@guan.function_decorator
def read_text_files_in_directory(directory='./', file_format='.md'):
import os
file_list = []
@@ -671,11 +651,10 @@ def read_text_files_in_directory(directory='./', file_format='.md'):
for file in file_list:
with open(file, 'r') as f:
content_array.append(f.read())
import guan
guan.statistics_of_guan_package()
return file_list, content_array
# 在多个文本文件中查找关键词
@guan.function_decorator
def find_words_in_multiple_files(words, directory='./', file_format='.md'):
import guan
file_list, content_array = guan.read_text_files_in_directory(directory=directory, file_format=file_format)
@@ -684,10 +663,10 @@ def find_words_in_multiple_files(words, directory='./', file_format='.md'):
for i0 in range(num_files):
if words in content_array[i0]:
file_list_with_words.append(file_list[i0])
guan.statistics_of_guan_package()
return file_list_with_words
# 并行计算前的预处理，把参数分成多份
@guan.function_decorator
def preprocess_for_parallel_calculations(parameter_array_all, cpus=1, task_index=0):
import numpy as np
num_all = np.array(parameter_array_all).shape[0]
@@ -700,36 +679,32 @@ def preprocess_for_parallel_calculations(parameter_array_all, cpus=1, task_index
parameter_array = parameter_array_all[task_index*num_parameter:(task_index+1)*num_parameter]
else:
parameter_array = parameter_array_all[task_index*num_parameter:num_all]
import guan
guan.statistics_of_guan_package()
return parameter_array
# 随机获得一个整数，左闭右闭
@guan.function_decorator
def get_random_number(start=0, end=1):
import random
rand_number = random.randint(start, end) # 左闭右闭 [start, end]
import guan
guan.statistics_of_guan_package()
return rand_number
# 选取一个种子生成固定的随机整数
@guan.function_decorator
def generate_random_int_number_for_a_specific_seed(seed=0, x_min=0, x_max=10):
import numpy as np
np.random.seed(seed)
rand_num = np.random.randint(x_min, x_max) # 左闭右开[x_min, x_max)
import guan
guan.statistics_of_guan_package()
return rand_num
# 使用jieba软件包进行分词
@guan.function_decorator
def divide_text_into_words(text):
import jieba
words = jieba.lcut(text)
import guan
guan.statistics_of_guan_package()
return words
# 判断某个字符是中文还是英文或其他
@guan.function_decorator
def check_Chinese_or_English(a):
if '\u4e00' <= a <= '\u9fff' :
word_type = 'Chinese'
@@ -740,6 +715,7 @@ def check_Chinese_or_English(a):
return word_type
# 统计中英文文本的字数，默认不包括空格
@guan.function_decorator
def count_words(text, include_space=0, show_words=0):
import jieba
import guan
@@ -764,46 +740,41 @@ def count_words(text, include_space=0, show_words=0):
new_words_2.append(word)
if show_words == 1:
print(new_words_2)
import guan
guan.statistics_of_guan_package()
return num_words
# 将RGB转成HEX
@guan.function_decorator
def rgb_to_hex(rgb, pound=1):
import guan
guan.statistics_of_guan_package()
if pound==0:
return '%02x%02x%02x' % rgb
else:
return '#%02x%02x%02x' % rgb
# 将HEX转成RGB
@guan.function_decorator
def hex_to_rgb(hex):
hex = hex.lstrip('#')
length = len(hex)
import guan
guan.statistics_of_guan_package()
return tuple(int(hex[i:i+length//3], 16) for i in range(0, length, length//3))
# 使用MD5进行散列加密
@guan.function_decorator
def encryption_MD5(password, salt=''):
import hashlib
password = salt+password
hashed_password = hashlib.md5(password.encode()).hexdigest()
import guan
guan.statistics_of_guan_package()
return hashed_password
# 使用SHA-256进行散列加密
@guan.function_decorator
def encryption_SHA_256(password, salt=''):
import hashlib
password = salt+password
hashed_password = hashlib.sha256(password.encode()).hexdigest()
import guan
guan.statistics_of_guan_package()
return hashed_password
# 自动先后运行程序
@guan.function_decorator
def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='python ', show_time=0):
import os
import time
@@ -821,25 +792,22 @@ def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='pytho
if show_time == 1:
end = time.time()
print('Total running time = '+str((end-start)/60)+' min')
import guan
guan.statistics_of_guan_package()
# 如果不存在文件夹，则新建文件夹
@guan.function_decorator
def make_directory(directory='./test'):
import os
if not os.path.exists(directory):
os.makedirs(directory)
import guan
guan.statistics_of_guan_package()
# 复制一份文件
@guan.function_decorator
def copy_file(file1='./a.txt', file2='./b.txt'):
import shutil
shutil.copy(file1, file2)
import guan
guan.statistics_of_guan_package()
# 拼接两个PDF文件
@guan.function_decorator
def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_file='combined_file.pdf'):
import PyPDF2
output_pdf = PyPDF2.PdfWriter()
@@ -852,6 +820,4 @@ def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_fil
for page in range(len(pdf2.pages)):
output_pdf.add_page(pdf2.pages[page])
with open(output_file, 'wb') as combined_file:
output_pdf.write(combined_file)
import guan
guan.statistics_of_guan_package()
output_pdf.write(combined_file)

17
PyPI/src/guan/density_of_states.py
View File

@@ -1,16 +1,18 @@
# Module: density_of_states
import guan
# 计算体系的总态密度
@guan.function_decorator
def total_density_of_states(fermi_energy, hamiltonian, broadening=0.01):
import numpy as np
import math
import guan
green = guan.green_function(fermi_energy, hamiltonian, broadening)
total_dos = -np.trace(np.imag(green))/math.pi
guan.statistics_of_guan_package()
return total_dos
# 对于不同费米能，计算体系的总态密度
@guan.function_decorator
def total_density_of_states_with_fermi_energy_array(fermi_energy_array, hamiltonian, broadening=0.01, print_show=0):
import numpy as np
import guan
@@ -22,10 +24,10 @@ def total_density_of_states_with_fermi_energy_array(fermi_energy_array, hamilton
print(fermi_energy)
total_dos_array[i0] = guan.total_density_of_states(fermi_energy, hamiltonian, broadening)
i0 += 1
guan.statistics_of_guan_package()
return total_dos_array
# 计算方格子的局域态密度（其中，哈密顿量的维度为：dim_hamiltonian = N1*N2*internal_degree）
@guan.function_decorator
def local_density_of_states_for_square_lattice(fermi_energy, hamiltonian, N1, N2, internal_degree=1, broadening=0.01):
import numpy as np
import math
@@ -36,10 +38,10 @@ def local_density_of_states_for_square_lattice(fermi_energy, hamiltonian, N1, N2
for i2 in range(N2):
for i in range(internal_degree):
local_dos[i2, i1] = local_dos[i2, i1]-np.imag(green[i1*N2*internal_degree+i2*internal_degree+i, i1*N2*internal_degree+i2*internal_degree+i])/math.pi
guan.statistics_of_guan_package()
return local_dos
# 计算立方格子的局域态密度（其中，哈密顿量的维度为：dim_hamiltonian = N1*N2*N3*internal_degree）
@guan.function_decorator
def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2, N3, internal_degree=1, broadening=0.01):
import numpy as np
import math
@@ -51,10 +53,10 @@ def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2,
for i3 in range(N3):
for i in range(internal_degree):
local_dos[i3, i2, i1] = local_dos[i3, i2, i1]-np.imag(green[i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i, i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i])/math.pi
guan.statistics_of_guan_package()
return local_dos
# 利用Dyson方程，计算方格子的局域态密度（其中，h00的维度为：dim_h00 = N2*internal_degree）
@guan.function_decorator
def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy, h00, h01, N2, N1, internal_degree=1, broadening=0.01):
import numpy as np
import math
@@ -85,10 +87,10 @@ def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy
for i2 in range(N2):
for i in range(internal_degree):
local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/math.pi
guan.statistics_of_guan_package()
return local_dos
# 利用Dyson方程，计算立方格子的局域态密度（其中，h00的维度为：dim_h00 = N2*N3*internal_degree）
@guan.function_decorator
def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy, h00, h01, N3, N2, N1, internal_degree=1, broadening=0.01):
import numpy as np
import math
@@ -119,11 +121,11 @@ def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy,
for i2 in range(N2):
for i3 in range(N3):
for i in range(internal_degree):
local_dos[i3, i2, i1] = local_dos[i3, i2, i1] -np.imag(green_ii_n_minus[i2*N3*internal_degree+i3*internal_degree+i, i2*N3*internal_degree+i3*internal_degree+i])/math.pi
guan.statistics_of_guan_package()
local_dos[i3, i2, i1] = local_dos[i3, i2, i1] -np.imag(green_ii_n_minus[i2*N3*internal_degree+i3*internal_degree+i, i2*N3*internal_degree+i3*internal_degree+i])/math.pi
return local_dos
# 利用Dyson方程，计算方格子条带（考虑了电极自能）的局域态密度（其中，h00的维度为：dim_h00 = N2*internal_degree）
@guan.function_decorator
def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equation(fermi_energy, h00, h01, N2, N1, right_self_energy, left_self_energy, internal_degree=1, broadening=0.01):
import numpy as np
import math
@@ -160,5 +162,4 @@ def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equa
for i2 in range(N2):
for i in range(internal_degree):
local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/math.pi
guan.statistics_of_guan_package()
return local_dos

184
PyPI/src/guan/others.py
View File

@@ -1,6 +1,8 @@
# Module: others
import guan
# 获取运行的日期和时间并写入文件
@guan.function_decorator
def statistics_with_day_and_time(content='', filename='a', file_format='.txt'):
import datetime
datetime_today = str(datetime.date.today())
@@ -10,10 +12,9 @@ def statistics_with_day_and_time(content='', filename='a', file_format='.txt'):
f2.write(datetime_today+' '+datetime_time+'\n')
else:
f2.write(datetime_today+' '+datetime_time+' '+content+'\n')
import guan
guan.statistics_of_guan_package()
# 统计Python文件中import的数量并排序
@guan.function_decorator
def count_number_of_import_statements(filename, file_format='.py', num=1000):
with open(filename+file_format, 'r') as file:
lines = file.readlines()
@@ -24,27 +25,24 @@ def count_number_of_import_statements(filename, file_format='.py', num=1000):
import_array.append(line)
from collections import Counter
import_statement_counter = Counter(import_array).most_common(num)
import guan
guan.statistics_of_guan_package()
return import_statement_counter
# 根据一定的字符长度来分割文本
@guan.function_decorator
def split_text(text, wrap_width=3000):
import textwrap
split_text_list = textwrap.wrap(text, wrap_width)
import guan
guan.statistics_of_guan_package()
return split_text_list
# 获取CPU使用率
@guan.function_decorator
def get_cpu_usage(interval=1):
import psutil
cpu_usage = psutil.cpu_percent(interval=interval)
import guan
guan.statistics_of_guan_package()
return cpu_usage
# 获取内存信息
@guan.function_decorator
def get_memory_info():
import psutil
memory_info = psutil.virtual_memory()
@@ -55,6 +53,7 @@ def get_memory_info():
return total_memory, used_memory, available_memory, used_memory_percent
# 获取本月的所有日期
@guan.function_decorator
def get_days_of_the_current_month(str_or_datetime='str'):
import datetime
today = datetime.date.today()
@@ -71,11 +70,10 @@ def get_days_of_the_current_month(str_or_datetime='str'):
elif str_or_datetime=='datetime':
day_array.append(current_date)
current_date += datetime.timedelta(days=1)
import guan
guan.statistics_of_guan_package()
return day_array
# 获取上个月份
@guan.function_decorator
def get_last_month():
import datetime
today = datetime.date.today()
@@ -85,11 +83,10 @@ def get_last_month():
year_of_last_month = today.year - 1
else:
year_of_last_month = today.year
import guan
guan.statistics_of_guan_package()
return year_of_last_month, last_month
# 获取上上个月份
@guan.function_decorator
def get_the_month_before_last():
import datetime
today = datetime.date.today()
@@ -104,11 +101,10 @@ def get_the_month_before_last():
year_of_the_month_before_last = today.year - 1
else:
year_of_the_month_before_last = today.year
import guan
guan.statistics_of_guan_package()
return year_of_the_month_before_last, the_month_before_last
# 获取上个月的所有日期
@guan.function_decorator
def get_days_of_the_last_month(str_or_datetime='str'):
import datetime
import guan
@@ -127,10 +123,10 @@ def get_days_of_the_last_month(str_or_datetime='str'):
elif str_or_datetime=='datetime':
day_array.append(current_date)
current_date += datetime.timedelta(days=1)
guan.statistics_of_guan_package()
return day_array
# 获取上上个月的所有日期
@guan.function_decorator
def get_days_of_the_month_before_last(str_or_datetime='str'):
import datetime
import guan
@@ -149,39 +145,38 @@ def get_days_of_the_month_before_last(str_or_datetime='str'):
elif str_or_datetime=='datetime':
day_array.append(current_date)
current_date += datetime.timedelta(days=1)
guan.statistics_of_guan_package()
return day_array
# 获取所有股票
@guan.function_decorator
def all_stocks():
import numpy as np
import akshare as ak
stocks = ak.stock_zh_a_spot_em()
title = np.array(stocks.columns)
stock_data = stocks.values
import guan
guan.statistics_of_guan_package()
return title, stock_data
# 获取所有股票的代码
@guan.function_decorator
def all_stock_symbols():
import guan
title, stock_data = guan.all_stocks()
stock_symbols = stock_data[:, 1]
guan.statistics_of_guan_package()
return stock_symbols
# 从股票代码获取股票名称
@guan.function_decorator
def find_stock_name_from_symbol(symbol='000002'):
import guan
title, stock_data = guan.all_stocks()
for stock in stock_data:
if symbol in stock:
stock_name = stock[2]
guan.statistics_of_guan_package()
return stock_name
# 获取单个股票的历史数据
@guan.function_decorator
def history_data_of_one_stock(symbol='000002', period='daily', start_date="19000101", end_date='21000101'):
# period = 'daily'
# period = 'weekly'
@@ -191,20 +186,18 @@ def history_data_of_one_stock(symbol='000002', period='daily', start_date="19000
stock = ak.stock_zh_a_hist(symbol=symbol, period=period, start_date=start_date, end_date=end_date)
title = np.array(stock.columns)
stock_data = stock.values[::-1]
import guan
guan.statistics_of_guan_package()
return title, stock_data
# 获取软件包中的所有模块名
@guan.function_decorator
def get_all_modules_in_one_package(package_name='guan'):
import pkgutil
package = __import__(package_name)
module_names = [name for _, name, _ in pkgutil.iter_modules(package.__path__)]
import guan
guan.statistics_of_guan_package()
return module_names
# 获取软件包中一个模块的所有函数名
@guan.function_decorator
def get_all_functions_in_one_module(module_name, package_name='guan'):
import inspect
function_names = []
@@ -212,11 +205,10 @@ def get_all_functions_in_one_module(module_name, package_name='guan'):
for name, obj in inspect.getmembers(module):
if inspect.isfunction(obj):
function_names.append(name)
import guan
guan.statistics_of_guan_package()
return function_names
# 获取软件包中的所有函数名
@guan.function_decorator
def get_all_functions_in_one_package(package_name='guan', print_show=1):
import guan
module_names = guan.get_all_modules_in_one_package(package_name=package_name)
@@ -231,10 +223,10 @@ def get_all_functions_in_one_package(package_name='guan', print_show=1):
print('function:', name)
if print_show == 1:
print()
guan.statistics_of_guan_package()
return all_function_names
# 获取包含某个字符的进程PID值
@guan.function_decorator
def get_PID(name):
import subprocess
command = "ps -ef | grep "+name
@@ -244,11 +236,17 @@ def get_PID(name):
import re
ps_ef = re.split(r'\s+', ps_ef)
id_running = ps_ef[1]
import guan
guan.statistics_of_guan_package()
return id_running
# 获取函数的源码
@guan.function_decorator
def get_function_source(function_name):
import inspect
function_source = inspect.getsource(function_name)
return function_source
# 在服务器上运行函数（说明：接口服务可能为关闭状态，如果无法使用请联系管理员。目前仅支持长度较短的函数，此外由于服务器只获取一个函数内的代码，因此需要函数是独立的可运行的代码。需要注意的是：返回的值是字符串类型，需要自行转换成数字类型。）
@guan.function_decorator
def run(function_name, args=(), return_show=0, get_print=1):
import socket
import json
@@ -283,10 +281,10 @@ def run(function_name, args=(), return_show=0, get_print=1):
except:
break
client_socket.close()
guan.statistics_of_guan_package()
return return_data
# 在服务器上运行大语言模型，通过Python函数调用（说明：接口服务可能为关闭状态，如果无法使用请联系管理员）
@guan.function_decorator
def chat(prompt='你好', stream_show=1, top_p=0.8, temperature=0.8):
import socket
import json
@@ -321,11 +319,10 @@ def chat(prompt='你好', stream_show=1, top_p=0.8, temperature=0.8):
except:
break
client_socket.close()
import guan
guan.statistics_of_guan_package()
return response
# 查找文件名相同的文件
@guan.function_decorator
def find_repeated_file_with_same_filename(directory='./', ignored_directory_with_words=[], ignored_file_with_words=[], num=1000):
import os
from collections import Counter
@@ -347,11 +344,10 @@ def find_repeated_file_with_same_filename(directory='./', ignored_directory_with
for item in count_file:
if item[1]>1:
repeated_file.append(item)
import guan
guan.statistics_of_guan_package()
return repeated_file
# 统计各个子文件夹中的文件数量
@guan.function_decorator
def count_file_in_sub_directory(directory='./', sort=0, reverse=1, print_show=1, smaller_than_num=None):
import os
import numpy as np
@@ -402,12 +398,10 @@ def count_file_in_sub_directory(directory='./', sort=0, reverse=1, print_show=1,
print(dirs_list[i0])
print(count_file_array[i0])
print()
import guan
guan.statistics_of_guan_package()
return sub_directory, num_in_sub_directory
# 改变当前的目录位置
@guan.function_decorator
def change_directory_by_replacement(current_key_word='code', new_key_word='data'):
import os
code_path = os.getcwd()
@@ -416,10 +410,9 @@ def change_directory_by_replacement(current_key_word='code', new_key_word='data'
if os.path.exists(data_path) == False:
os.makedirs(data_path)
os.chdir(data_path)
import guan
guan.statistics_of_guan_package()
# 在多个子文件夹中产生必要的文件，例如 readme.md
@guan.function_decorator
def creat_necessary_file(directory, filename='readme', file_format='.md', content='', overwrite=None, ignored_directory_with_words=[]):
import os
directory_with_file = []
@@ -447,20 +440,18 @@ def creat_necessary_file(directory, filename='readme', file_format='.md', conten
f = open(filename+file_format, 'w', encoding="utf-8")
f.write(content)
f.close()
import guan
guan.statistics_of_guan_package()
# 删除特定文件名的文件（慎用）
@guan.function_decorator
def delete_file_with_specific_name(directory, filename='readme', file_format='.md'):
import os
for root, dirs, files in os.walk(directory):
for i0 in range(len(files)):
if files[i0] == filename+file_format:
os.remove(root+'/'+files[i0])
import guan
guan.statistics_of_guan_package()
# 将所有文件移到根目录（慎用）
@guan.function_decorator
def move_all_files_to_root_directory(directory):
import os
import shutil
@@ -473,10 +464,9 @@ def move_all_files_to_root_directory(directory):
os.rmdir(root)
except:
pass
import guan
guan.statistics_of_guan_package()
# 将文件目录结构写入Markdown文件
@guan.function_decorator
def write_file_list_in_markdown(directory='./', filename='a', reverse_positive_or_negative=1, starting_from_h1=None, banned_file_format=[], hide_file_format=None, divided_line=None, show_second_number=None, show_third_number=None):
import os
f = open(filename+'.md', 'w', encoding="utf-8")
@@ -578,10 +568,9 @@ def write_file_list_in_markdown(directory='./', filename='a', reverse_positive_o
f.write('#')
f.write('###### '+str(filename6)+'\n\n')
f.close()
import guan
guan.statistics_of_guan_package()
# 从网页的标签中获取内容
@guan.function_decorator
def get_html_from_tags(link, tags=['title', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'p', 'li', 'a']):
from bs4 import BeautifulSoup
import urllib.request
@@ -597,19 +586,17 @@ def get_html_from_tags(link, tags=['title', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6',
content = text
else:
content = content + '\n\n' + text
import guan
guan.statistics_of_guan_package()
return content
# 生成二维码
@guan.function_decorator
def creat_qrcode(data="https://www.guanjihuan.com", filename='a', file_format='.png'):
import qrcode
img = qrcode.make(data)
img.save(filename+file_format)
import guan
guan.statistics_of_guan_package()
# 将PDF文件转成文本
@guan.function_decorator
def pdf_to_text(pdf_path):
from pdfminer.pdfparser import PDFParser, PDFDocument
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
@@ -638,21 +625,19 @@ def pdf_to_text(pdf_path):
for x in layout:
if isinstance(x, LTTextBox):
content = content + x.get_text().strip()
import guan
guan.statistics_of_guan_package()
return content
# 获取PDF文件页数
@guan.function_decorator
def get_pdf_page_number(pdf_path):
import PyPDF2
pdf_file = open(pdf_path, 'rb')
pdf_reader = PyPDF2.PdfReader(pdf_file)
num_pages = len(pdf_reader.pages)
import guan
guan.statistics_of_guan_package()
return num_pages
# 获取PDF文件指定页面的内容
@guan.function_decorator
def pdf_to_txt_for_a_specific_page(pdf_path, page_num=1):
import PyPDF2
pdf_file = open(pdf_path, 'rb')
@@ -663,11 +648,10 @@ def pdf_to_txt_for_a_specific_page(pdf_path, page_num=1):
page = pdf_reader.pages[page_num0]
page_text = page.extract_text()
pdf_file.close()
import guan
guan.statistics_of_guan_package()
return page_text
# 获取PDF文献中的链接。例如: link_starting_form='https://doi.org'
@guan.function_decorator
def get_links_from_pdf(pdf_path, link_starting_form=''):
import PyPDF2
import re
@@ -688,12 +672,11 @@ def get_links_from_pdf(pdf_path, link_starting_form=''):
if u['/A']['/URI'] != old:
links.append(u['/A']['/URI'])
i0 += 1
old = u['/A']['/URI']
import guan
guan.statistics_of_guan_package()
old = u['/A']['/URI']
return links
# 通过Sci-Hub网站下载文献
@guan.function_decorator
def download_with_scihub(address=None, num=1):
from bs4 import BeautifulSoup
import re
@@ -729,10 +712,9 @@ def download_with_scihub(address=None, num=1):
print('Completed!\n')
if num != 1:
print('All completed!\n')
import guan
guan.statistics_of_guan_package()
# 将字符串转成音频
@guan.function_decorator
def str_to_audio(str='hello world', filename='str', rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0):
import pyttsx3
import guan
@@ -754,9 +736,9 @@ def str_to_audio(str='hello world', filename='str', rate=125, voice=1, read=1, s
if read==1:
engine.say(str)
engine.runAndWait()
guan.statistics_of_guan_package()
# 将txt文件转成音频
@guan.function_decorator
def txt_to_audio(txt_path, rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0):
import pyttsx3
import guan
@@ -782,9 +764,9 @@ def txt_to_audio(txt_path, rate=125, voice=1, read=1, save=0, compress=0, bitrat
if read==1:
engine.say(text)
engine.runAndWait()
guan.statistics_of_guan_package()
# 将PDF文件转成音频
@guan.function_decorator
def pdf_to_audio(pdf_path, rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0):
import pyttsx3
import guan
@@ -810,18 +792,17 @@ def pdf_to_audio(pdf_path, rate=125, voice=1, read=1, save=0, compress=0, bitrat
if read==1:
engine.say(text)
engine.runAndWait()
guan.statistics_of_guan_package()
# 将wav音频文件压缩成MP3音频文件
@guan.function_decorator
def compress_wav_to_mp3(wav_path, output_filename='a.mp3', bitrate='16k'):
# Note: Beside the installation of pydub, you may also need download FFmpeg on http://www.ffmpeg.org/download.html and add the bin path to the environment variable.
from pydub import AudioSegment
sound = AudioSegment.from_mp3(wav_path)
sound.export(output_filename,format="mp3",bitrate=bitrate)
import guan
guan.statistics_of_guan_package()
# 播放学术单词
@guan.function_decorator
def play_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_translation=1, show_link=1, translation_time=2, rest_time=1):
from bs4 import BeautifulSoup
import re
@@ -880,10 +861,9 @@ def play_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_translati
time.sleep(rest_time)
pygame.mixer.music.stop()
print()
import guan
guan.statistics_of_guan_package()
# 播放挑选过后的学术单词
@guan.function_decorator
def play_selected_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_link=1, rest_time=3):
from bs4 import BeautifulSoup
import re
@@ -938,10 +918,9 @@ def play_selected_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_
time.sleep(rest_time)
pygame.mixer.music.stop()
print()
import guan
guan.statistics_of_guan_package()
# 播放元素周期表上的单词
@guan.function_decorator
def play_element_words(random_on=0, show_translation=1, show_link=1, translation_time=2, rest_time=1):
from bs4 import BeautifulSoup
import re
@@ -995,42 +974,41 @@ def play_element_words(random_on=0, show_translation=1, show_link=1, translation
time.sleep(rest_time)
pygame.mixer.music.stop()
print()
import guan
guan.statistics_of_guan_package()
# Guan软件包的使用统计（不涉及到用户的个人数据）
global_variable_of_first_guan_package_calling = []
def statistics_of_guan_package():
def statistics_of_guan_package(function_name=None):
import guan
function_name = guan.get_calling_function_name(layer=2)
if function_name == None:
function_name = guan.get_calling_function_name(layer=2)
else:
pass
global global_variable_of_first_guan_package_calling
if function_name not in global_variable_of_first_guan_package_calling:
global_variable_of_first_guan_package_calling.append(function_name)
function_calling_name = guan.get_calling_function_name(layer=3)
if function_calling_name == '<module>':
try:
import socket
datetime_date = guan.get_date()
datetime_time = guan.get_time()
current_version = guan.get_current_version('guan')
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.settimeout(0.5)
client_socket.connect(('socket.guanjihuan.com', 12345))
mac_address = guan.get_mac_address()
message = {
'server': 'py.guanjihuan.com',
'date': datetime_date,
'time': datetime_time,
'version': current_version,
'MAC_address': mac_address,
'function_name': function_name
}
import json
send_message = json.dumps(message)
client_socket.send(send_message.encode())
client_socket.close()
except:
pass
try:
import socket
datetime_date = guan.get_date()
datetime_time = guan.get_time()
current_version = guan.get_current_version('guan')
client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
client_socket.settimeout(0.5)
client_socket.connect(('socket.guanjihuan.com', 12345))
mac_address = guan.get_mac_address()
message = {
'server': 'py.guanjihuan.com',
'date': datetime_date,
'time': datetime_time,
'version': current_version,
'MAC_address': mac_address,
'function_name': function_name
}
import json
send_message = json.dumps(message)
client_socket.send(send_message.encode())
client_socket.close()
except:
pass
# 获取当前日期字符串
def get_date(bar=True):
@@ -1055,12 +1033,6 @@ def get_mac_address():
mac_address = '-'.join([mac_address[i:i+2] for i in range(0, 11, 2)])
return mac_address
# 获取函数的源码
def get_function_source(function_name):
import inspect
function_source = inspect.getsource(function_name)
return function_source
# 获取调用本函数的函数名
def get_calling_function_name(layer=1):
import inspect

52
PyPI/src/guan/quantum_transport.py
View File

@@ -1,6 +1,8 @@
# Module: quantum_transport
import guan
# 计算电导
@guan.function_decorator
def calculate_conductance(fermi_energy, h00, h01, length=100):
import numpy as np
import copy
@@ -17,10 +19,10 @@ def calculate_conductance(fermi_energy, h00, h01, length=100):
green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
guan.statistics_of_guan_package()
return conductance
# 计算不同费米能下的电导
@guan.function_decorator
def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01, length=100, print_show=0):
import numpy as np
import guan
@@ -32,10 +34,10 @@ def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01,
if print_show == 1:
print(fermi_energy, conductance_array[i0])
i0 += 1
guan.statistics_of_guan_package()
return conductance_array
# 计算在势垒散射下的电导
@guan.function_decorator
def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barrier_length=20, barrier_potential=1):
import numpy as np
import copy
@@ -56,10 +58,10 @@ def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barri
green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
guan.statistics_of_guan_package()
return conductance
# 计算在无序散射下的电导
@guan.function_decorator
def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100, calculation_times=1):
import numpy as np
import copy
@@ -85,10 +87,10 @@ def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensi
conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
conductance_averaged += conductance
conductance_averaged = conductance_averaged/calculation_times
guan.statistics_of_guan_package()
return conductance_averaged
# 计算在无序垂直切片的散射下的电导
@guan.function_decorator
def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100):
import numpy as np
import copy
@@ -109,10 +111,10 @@ def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_i
green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
guan.statistics_of_guan_package()
return conductance
# 计算在无序水平切片的散射下的电导
@guan.function_decorator
def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translational_symmetry(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100):
import numpy as np
import copy
@@ -134,10 +136,10 @@ def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translation
green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
guan.statistics_of_guan_package()
return conductance
# 计算在随机空位的散射下的电导
@guan.function_decorator
def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100):
import numpy as np
import copy
@@ -159,10 +161,10 @@ def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_co
green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
guan.statistics_of_guan_package()
return conductance
# 计算在不同无序散射强度下的电导
@guan.function_decorator
def calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01, disorder_intensity_array, disorder_concentration=1.0, length=100, calculation_times=1, print_show=0):
import numpy as np
import guan
@@ -176,10 +178,10 @@ def calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01,
print(disorder_intensity, conductance_array[i0]/calculation_times)
i0 += 1
conductance_array = conductance_array/calculation_times
guan.statistics_of_guan_package()
return conductance_array
# 计算在不同无序浓度下的电导
@guan.function_decorator
def calculate_conductance_with_disorder_concentration_array(fermi_energy, h00, h01, disorder_concentration_array, disorder_intensity=2.0, length=100, calculation_times=1, print_show=0):
import numpy as np
import guan
@@ -193,10 +195,10 @@ def calculate_conductance_with_disorder_concentration_array(fermi_energy, h00, h
print(disorder_concentration, conductance_array[i0]/calculation_times)
i0 += 1
conductance_array = conductance_array/calculation_times
guan.statistics_of_guan_package()
return conductance_array
# 计算在不同无序散射长度下的电导
@guan.function_decorator
def calculate_conductance_with_scattering_length_array(fermi_energy, h00, h01, length_array, disorder_intensity=2.0, disorder_concentration=1.0, calculation_times=1, print_show=0):
import numpy as np
import guan
@@ -210,10 +212,10 @@ def calculate_conductance_with_scattering_length_array(fermi_energy, h00, h01, l
print(length, conductance_array[i0]/calculation_times)
i0 += 1
conductance_array = conductance_array/calculation_times
guan.statistics_of_guan_package()
return conductance_array
# 计算得到Gamma矩阵和格林函数，用于计算六端口的量子输运
@guan.function_decorator
def get_gamma_array_and_green_for_six_terminal_transmission(fermi_energy, h00_for_lead_4, h01_for_lead_4, h00_for_lead_2, h01_for_lead_2, center_hamiltonian, width=10, length=50, internal_degree=1, moving_step_of_leads=10):
import numpy as np
import guan
@@ -257,10 +259,10 @@ def get_gamma_array_and_green_for_six_terminal_transmission(fermi_energy, h00_fo
self_energy6, gamma6 = guan.self_energy_of_lead_with_h_lead_to_center(fermi_energy, h00_for_lead_6, h01_for_lead_1, h_lead6_to_center)
gamma_array = [gamma1, gamma2, gamma3, gamma4, gamma5, gamma6]
green = np.linalg.inv(fermi_energy*np.eye(internal_degree*width*length)-center_hamiltonian-self_energy1-self_energy2-self_energy3-self_energy4-self_energy5-self_energy6)
guan.statistics_of_guan_package()
return gamma_array, green
# 计算六端口的透射矩阵
@guan.function_decorator
def calculate_six_terminal_transmission_matrix(fermi_energy, h00_for_lead_4, h01_for_lead_4, h00_for_lead_2, h01_for_lead_2, center_hamiltonian, width=10, length=50, internal_degree=1, moving_step_of_leads=10):
import numpy as np
import guan
@@ -282,10 +284,10 @@ def calculate_six_terminal_transmission_matrix(fermi_energy, h00_for_lead_4, h01
if j0!=i0:
transmission_matrix[i0, i0] = transmission_matrix[i0, i0]-transmission_matrix[i0, j0]
transmission_matrix = np.real(transmission_matrix)
guan.statistics_of_guan_package()
return transmission_matrix
# 计算从电极1出发的透射系数
@guan.function_decorator
def calculate_six_terminal_transmissions_from_lead_1(fermi_energy, h00_for_lead_4, h01_for_lead_4, h00_for_lead_2, h01_for_lead_2, center_hamiltonian, width=10, length=50, internal_degree=1, moving_step_of_leads=10):
import numpy as np
import guan
@@ -295,21 +297,20 @@ def calculate_six_terminal_transmissions_from_lead_1(fermi_energy, h00_for_lead_
transmission_14 = np.real(np.trace(np.dot(np.dot(np.dot(gamma_array[0], green), gamma_array[3]), green.transpose().conj())))
transmission_15 = np.real(np.trace(np.dot(np.dot(np.dot(gamma_array[0], green), gamma_array[4]), green.transpose().conj())))
transmission_16 = np.real(np.trace(np.dot(np.dot(np.dot(gamma_array[0], green), gamma_array[5]), green.transpose().conj())))
guan.statistics_of_guan_package()
return transmission_12, transmission_13, transmission_14, transmission_15, transmission_16
# 通过动量k的虚部，判断通道为传播通道还是衰减通道
@guan.function_decorator
def if_active_channel(k_of_channel):
import numpy as np
if np.abs(np.imag(k_of_channel))<1e-6:
if_active = 1
else:
if_active = 0
import guan
guan.statistics_of_guan_package()
return if_active
# 获取通道的动量和速度，用于计算散射矩阵
@guan.function_decorator
def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
import numpy as np
import math
@@ -343,10 +344,10 @@ def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
velocity_of_channel[dim0] = eigenvalue[dim0]*np.dot(np.dot(temp[0:dim, :].transpose().conj(), h01),temp[0:dim, :])[dim0, dim0]
velocity_of_channel = -2*np.imag(velocity_of_channel)
eigenvector = copy.deepcopy(temp)
guan.statistics_of_guan_package()
return k_of_channel, velocity_of_channel, eigenvalue, eigenvector
# 获取分类后的动量和速度，以及U和F，用于计算散射矩阵
@guan.function_decorator
def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01):
import numpy as np
import guan
@@ -396,10 +397,10 @@ def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01):
lambda_matrix_left = np.diag(lambda_left)
f_right = np.dot(np.dot(u_right, lambda_matrix_right), np.linalg.inv(u_right))
f_left = np.dot(np.dot(u_left, lambda_matrix_left), np.linalg.inv(u_left))
guan.statistics_of_guan_package()
return k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active
# 计算散射矩阵
@guan.function_decorator
def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
import numpy as np
import math
@@ -442,10 +443,10 @@ def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
for sum_of_tran_refl in sum_of_tran_refl_array:
if sum_of_tran_refl > 1.001:
print('Error Alert: scattering matrix is not normalized!')
guan.statistics_of_guan_package()
return transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active
# 从散射矩阵中，获取散射矩阵的信息
@guan.function_decorator
def information_of_scattering_matrix(transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active):
import numpy as np
if np.array(transmission_matrix).shape==():
@@ -464,22 +465,20 @@ def information_of_scattering_matrix(transmission_matrix, reflection_matrix, k_r
total_conductance = np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])))
total_reflection_of_channels = np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0)
sum_of_transmission_and_reflection_of_channels = np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0) + np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0)
import guan
guan.statistics_of_guan_package()
return number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels
# 已知h00和h01，计算散射矩阵并获得散射矩阵的信息
@guan.function_decorator
def calculate_scattering_matrix_and_get_information(fermi_energy, h00, h01, length=100):
import guan
transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active = guan.calculate_scattering_matrix(fermi_energy, h00, h01, length=length)
number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels = guan.information_of_scattering_matrix(transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active)
guan.statistics_of_guan_package()
return number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels
# 从散射矩阵中打印出散射矩阵的信息
@guan.function_decorator
def print_or_write_scattering_matrix_with_information_of_scattering_matrix(number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels, print_show=1, write_file=0, filename='a', file_format='.txt'):
if print_show == 1:
print('\nActive channel (left or right) = ', number_of_active_channels)
@@ -522,10 +521,9 @@ def print_or_write_scattering_matrix_with_information_of_scattering_matrix(numbe
f.write('\n')
f.write('Total transmission of channels:\n'+str(total_transmission_of_channels)+'\n')
f.write('Total conductance = '+str(total_conductance)+'\n')
import guan
guan.statistics_of_guan_package()
# 已知h00和h01，计算散射矩阵并打印出散射矩阵的信息
@guan.function_decorator
def print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_show=1, write_file=0, filename='a', file_format='.txt'):
import guan
transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active = guan.calculate_scattering_matrix(fermi_energy, h00, h01, length=length)
@@ -534,9 +532,8 @@ def print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_s
guan.print_or_write_scattering_matrix_with_information_of_scattering_matrix(number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels, print_show=print_show, write_file=write_file, filename=filename, file_format=file_format)
guan.statistics_of_guan_package()
# 在无序下，计算散射矩阵
@guan.function_decorator
def calculate_scattering_matrix_with_disorder(fermi_energy, h00, h01, length=100, disorder_intensity=2.0, disorder_concentration=1.0):
import numpy as np
import math
@@ -583,10 +580,10 @@ def calculate_scattering_matrix_with_disorder(fermi_energy, h00, h01, length=100
for sum_of_tran_refl in sum_of_tran_refl_array:
if sum_of_tran_refl > 1.001:
print('Error Alert: scattering matrix is not normalized!')
guan.statistics_of_guan_package()
return transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active
# 在无序下，计算散射矩阵，并获取散射矩阵多次计算的平均信息
@guan.function_decorator
def calculate_scattering_matrix_with_disorder_and_get_averaged_information(fermi_energy, h00, h01, length=100, disorder_intensity=2.0, disorder_concentration=1.0, calculation_times=1):
import guan
transmission_matrix_for_active_channels_averaged = 0
@@ -600,5 +597,4 @@ def calculate_scattering_matrix_with_disorder_and_get_averaged_information(fermi
reflection_matrix_for_active_channels_averaged += reflection_matrix_for_active_channels
transmission_matrix_for_active_channels_averaged = transmission_matrix_for_active_channels_averaged/calculation_times
reflection_matrix_for_active_channels_averaged = reflection_matrix_for_active_channels_averaged/calculation_times
guan.statistics_of_guan_package()
return transmission_matrix_for_active_channels_averaged, reflection_matrix_for_active_channels_averaged

27
PyPI/src/guan/topological_invariant.py
View File

@@ -1,6 +1,8 @@
# Module: topological_invariant
import guan
# 通过高效法计算方格子的陈数
@guan.function_decorator
def calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_function, precision=100, print_show=0):
import numpy as np
import math
@@ -36,10 +38,10 @@ def calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_
F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
chern_number[i] = chern_number[i] + F
chern_number = chern_number/(2*math.pi*1j)
guan.statistics_of_guan_package()
return chern_number
# 通过高效法计算方格子的陈数（可计算简并的情况）
@guan.function_decorator
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):
import numpy as np
import math
@@ -106,11 +108,10 @@ def calculate_chern_number_for_square_lattice_with_efficient_method_for_degenera
det_value= det_value*dot_matrix
chern_number += cmath.log(det_value)
chern_number = chern_number/(2*math.pi*1j)
import guan
guan.statistics_of_guan_package()
return chern_number
# 通过Wilson loop方法计算方格子的陈数
@guan.function_decorator
def calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
import numpy as np
import math
@@ -152,11 +153,10 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_funct
arg = np.log(np.diagonal(wilson_loop))/1j
chern_number = chern_number + arg
chern_number = chern_number/(2*math.pi)
import guan
guan.statistics_of_guan_package()
return chern_number
# 通过Wilson loop方法计算方格子的陈数（可计算简并的情况）
@guan.function_decorator
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):
import numpy as np
import math
@@ -217,11 +217,10 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_ca
arg = np.log(wilson_loop)/1j
chern_number = chern_number + arg
chern_number = chern_number/(2*math.pi)
import guan
guan.statistics_of_guan_package()
return chern_number
# 通过高效法计算贝利曲率
@guan.function_decorator
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
@@ -265,10 +264,10 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=
berry_curvature_array[j0, i0, i] = berry_curvature
j0 += 1
i0 += 1
guan.statistics_of_guan_package()
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）
@guan.function_decorator
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
@@ -344,11 +343,10 @@ def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamilton
berry_curvature_array[j00, i00] = berry_curvature
j00 += 1
i00 += 1
import guan
guan.statistics_of_guan_package()
return k_array, berry_curvature_array
# 通过Wilson loop方法计算贝里曲率
@guan.function_decorator
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
@@ -402,11 +400,10 @@ def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min='defa
berry_curvature_array[j00, i00, :]=berry_curvature
j00 += 1
i00 += 1
import guan
guan.statistics_of_guan_package()
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）
@guan.function_decorator
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
@@ -475,11 +472,10 @@ def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_f
berry_curvature_array[j000, i000]=berry_curvature
j000 += 1
i000 += 1
import guan
guan.statistics_of_guan_package()
return k_array, berry_curvature_array
# 计算蜂窝格子的陈数（高效法）
@guan.function_decorator
def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0):
import numpy as np
import math
@@ -520,10 +516,10 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
chern_number[i] = chern_number[i] + F
chern_number = chern_number/(2*math.pi*1j)
guan.statistics_of_guan_package()
return chern_number
# 计算Wilson loop
@guan.function_decorator
def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
import numpy as np
import guan
@@ -548,5 +544,4 @@ def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default'
for i0 in range(precision-1):
F = np.dot(eigenvector_array[i0+1].transpose().conj(), eigenvector_array[i0])
wilson_loop_array[i] = np.dot(F, wilson_loop_array[i])
guan.statistics_of_guan_package()
return wilson_loop_array