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0.1.63

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guanjihuan 2023-12-13 00:30:08 +08:00
parent 0ea6a97c82
commit 150d169681
14 changed files with 241 additions and 301 deletions
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2
PyPI/setup.cfg
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@ -1,7 +1,7 @@
[metadata]
# replace with your username:
name = guan
version = 0.1.62
version = 0.1.63
author = guanjihuan
author_email = guanjihuan@163.com
description = An open source python package

2
PyPI/src/guan.egg-info/PKG-INFO
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@ -1,6 +1,6 @@
Metadata-Version: 2.1
Name: guan
Version: 0.1.62
Version: 0.1.63
Summary: An open source python package
Home-page: https://py.guanjihuan.com
Author: guanjihuan

24
PyPI/src/guan/Fourier_transform.py
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@ -2,7 +2,7 @@
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
@guan.statistics_decorator
def one_dimensional_fourier_transform(k, unit_cell, hopping):
import numpy as np
import cmath
@ -12,7 +12,7 @@ def one_dimensional_fourier_transform(k, unit_cell, hopping):
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
@guan.statistics_decorator
def two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell, hopping_1, hopping_2):
import numpy as np
import cmath
@ -23,7 +23,7 @@ def two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell, hopp
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
@guan.statistics_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
@ -35,7 +35,7 @@ def three_dimensional_fourier_transform_for_cubic_lattice(k1, k2, k3, unit_cell,
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
@guan.statistics_decorator
def one_dimensional_fourier_transform_with_k(unit_cell, hopping):
import functools
import guan
@ -43,7 +43,7 @@ def one_dimensional_fourier_transform_with_k(unit_cell, hopping):
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
@guan.statistics_decorator
def two_dimensional_fourier_transform_for_square_lattice_with_k1_k2(unit_cell, hopping_1, hopping_2):
import functools
import guan
@ -51,7 +51,7 @@ def two_dimensional_fourier_transform_for_square_lattice_with_k1_k2(unit_cell, h
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
@guan.statistics_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
@ -59,14 +59,14 @@ def three_dimensional_fourier_transform_for_cubic_lattice_with_k1_k2_k3(unit_cel
return hamiltonian_function
# 由实空间格矢得到倒空间格矢（一维）
@guan.function_decorator
@guan.statistics_decorator
def calculate_one_dimensional_reciprocal_lattice_vector(a1):
import numpy as np
b1 = 2*np.pi/a1
return b1
# 由实空间格矢得到倒空间格矢（二维）
@guan.function_decorator
@guan.statistics_decorator
def calculate_two_dimensional_reciprocal_lattice_vectors(a1, a2):
import numpy as np
a1 = np.array(a1)
@ -81,7 +81,7 @@ def calculate_two_dimensional_reciprocal_lattice_vectors(a1, a2):
return b1, b2
# 由实空间格矢得到倒空间格矢（三维）
@guan.function_decorator
@guan.statistics_decorator
def calculate_three_dimensional_reciprocal_lattice_vectors(a1, a2, a3):
import numpy as np
a1 = np.array(a1)
@ -93,14 +93,14 @@ def calculate_three_dimensional_reciprocal_lattice_vectors(a1, a2, a3):
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
@guan.statistics_decorator
def calculate_one_dimensional_reciprocal_lattice_vector_with_sympy(a1):
import sympy
b1 = 2*sympy.pi/a1
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
@guan.statistics_decorator
def calculate_two_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2):
import sympy
a1 = sympy.Matrix(1, 3, [a1[0], a1[1], 0])
@ -115,7 +115,7 @@ def calculate_two_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2):
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
@guan.statistics_decorator
def calculate_three_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2, a3):
import sympy
cross_a2_a3 = a2.cross(a3)

24
PyPI/src/guan/Green_functions.py
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@ -2,7 +2,7 @@
import guan
# 输入哈密顿量，得到格林函数
@guan.function_decorator
@guan.statistics_decorator
def green_function(fermi_energy, hamiltonian, broadening, self_energy=0):
import numpy as np
if np.array(hamiltonian).shape==():
@ -13,7 +13,7 @@ def green_function(fermi_energy, hamiltonian, broadening, self_energy=0):
return green
# 在Dyson方程中的一个中间格林函数G_{nn}^{n}
@guan.function_decorator
@guan.statistics_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)
@ -25,14 +25,14 @@ def green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening, se
return green_nn_n
# 在Dyson方程中的一个中间格林函数G_{in}^{n}
@guan.function_decorator
@guan.statistics_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)
return green_in_n
# 在Dyson方程中的一个中间格林函数G_{ni}^{n}
@guan.function_decorator
@guan.statistics_decorator
def green_function_ni_n(green_nn_n, h01, green_ni_n_minus):
import numpy as np
h01 = np.array(h01)
@ -40,14 +40,14 @@ def green_function_ni_n(green_nn_n, h01, green_ni_n_minus):
return green_ni_n
# 在Dyson方程中的一个中间格林函数G_{ii}^{n}
@guan.function_decorator
@guan.statistics_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)
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
@guan.statistics_decorator
def transfer_matrix(fermi_energy, h00, h01):
import numpy as np
h01 = np.array(h01)
@ -63,7 +63,7 @@ def transfer_matrix(fermi_energy, h00, h01):
return transfer
# 计算电极的表面格林函数
@guan.function_decorator
@guan.statistics_decorator
def surface_green_function_of_lead(fermi_energy, h00, h01):
import numpy as np
h01 = np.array(h01)
@ -89,7 +89,7 @@ def surface_green_function_of_lead(fermi_energy, h00, h01):
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
@guan.statistics_decorator
def self_energy_of_lead(fermi_energy, h00, h01):
import numpy as np
import guan
@ -102,7 +102,7 @@ def self_energy_of_lead(fermi_energy, h00, h01):
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
@guan.statistics_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
@ -116,7 +116,7 @@ def self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR):
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
@guan.statistics_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
@ -127,7 +127,7 @@ def self_energy_of_lead_with_h_lead_to_center(fermi_energy, h00, h01, h_lead_to_
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
@guan.statistics_decorator
def green_function_with_leads(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian):
import numpy as np
import guan
@ -137,7 +137,7 @@ def green_function_with_leads(fermi_energy, h00, h01, h_LC, h_CR, center_hamilto
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
@guan.statistics_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

54
PyPI/src/guan/Hamiltonian_of_examples.py
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@ -2,7 +2,7 @@
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
@guan.statistics_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)
@ -23,7 +23,7 @@ def hamiltonian_of_finite_size_system_along_one_direction(N, on_site=0, hopping=
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
@guan.statistics_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)
@ -56,7 +56,7 @@ def hamiltonian_of_finite_size_system_along_two_directions_for_square_lattice(N1
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
@guan.statistics_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)
@ -105,7 +105,7 @@ def hamiltonian_of_finite_size_system_along_three_directions_for_cubic_lattice(N
return hamiltonian
# 构建有限尺寸的SSH模型哈密顿量
@guan.function_decorator
@guan.statistics_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))
@ -123,7 +123,7 @@ def hamiltonian_of_finite_size_ssh_model(N, v=0.6, w=1, onsite_1=0, onsite_2=0,
return hamiltonian
# 获取Zigzag边的石墨烯条带的元胞间跃迁
@guan.function_decorator
@guan.statistics_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)
@ -137,7 +137,7 @@ def get_hopping_term_of_graphene_ribbon_along_zigzag_direction(N, eta=0):
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
@guan.statistics_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
@ -149,7 +149,7 @@ def hamiltonian_of_finite_size_system_along_two_directions_for_graphene(N1, N2,
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
@guan.statistics_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
@ -170,7 +170,7 @@ def get_onsite_and_hopping_terms_of_2d_effective_graphene_along_one_direction(qy
return h00, h01
# 获取BHZ模型的在位能和跃迁项
@guan.function_decorator
@guan.statistics_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)
@ -204,7 +204,7 @@ def get_onsite_and_hopping_terms_of_bhz_model(A=0.3645/5, B=-0.686/25, C=0, D=-0
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
@guan.statistics_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)
@ -228,7 +228,7 @@ def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_up(A=0.3645/5, B=-0.
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
@guan.statistics_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)
@ -252,21 +252,21 @@ def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_down(A=0.3645/5, B=-
return H0, H1, H2
# 一维链的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_decorator
def hamiltonian_of_simple_chain(k):
import guan
hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=0, hopping=1)
return hamiltonian
# 二维方格子的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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)
return hamiltonian
# 准一维方格子条带的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_decorator
def hamiltonian_of_square_lattice_in_quasi_one_dimension(k, N=10, period=0):
import numpy as np
import guan
@ -284,14 +284,14 @@ def hamiltonian_of_square_lattice_in_quasi_one_dimension(k, N=10, period=0):
return hamiltonian
# 三维立方格子的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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)
return hamiltonian
# SSH模型的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_decorator
def hamiltonian_of_ssh_model(k, v=0.6, w=1):
import numpy as np
import cmath
@ -301,7 +301,7 @@ def hamiltonian_of_ssh_model(k, v=0.6, w=1):
return hamiltonian
# 石墨烯的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_decorator
def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
import numpy as np
import cmath
@ -318,7 +318,7 @@ def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
return hamiltonian
# 石墨烯有效模型的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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)
@ -334,7 +334,7 @@ def effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley
return hamiltonian
# 石墨烯有效模型离散化后的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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)
@ -350,7 +350,7 @@ def effective_hamiltonian_of_graphene_after_discretization(qx, qy, t=1, staggere
return hamiltonian
# 准一维Zigzag边石墨烯条带的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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
@ -380,7 +380,7 @@ def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1
return hamiltonian
# Haldane模型的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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
@ -402,7 +402,7 @@ def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi='default', a='
return hamiltonian
# 准一维Haldane模型条带的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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
@ -457,7 +457,7 @@ def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2
return hamiltonian
# 一个量子反常霍尔效应的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_decorator
def hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1):
import numpy as np
import math
@ -469,7 +469,7 @@ def hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1):
return hamiltonian
# BHZ模型的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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
@ -488,7 +488,7 @@ def hamiltonian_of_bhz_model(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25,
return hamiltonian
# 半BHZ模型的哈密顿量（自旋向上）（倒空间）
@guan.function_decorator
@guan.statistics_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
@ -503,7 +503,7 @@ def hamiltonian_of_half_bhz_model_for_spin_up(kx, ky, A=0.3645/5, B=-0.686/25, C
return hamiltonian
# 半BHZ模型的哈密顿量（自旋向下）（倒空间）
@guan.function_decorator
@guan.statistics_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
@ -518,7 +518,7 @@ def hamiltonian_of_half_bhz_model_for_spin_down(kx, ky, A=0.3645/5, B=-0.686/25,
return hamiltonian
# BBH模型的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_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
@ -538,7 +538,7 @@ def hamiltonian_of_bbh_model(kx, ky, gamma_x=0.5, gamma_y=0.5, lambda_x=1, lambd
return hamiltonian
# Kagome模型的哈密顿量（倒空间）
@guan.function_decorator
@guan.statistics_decorator
def hamiltonian_of_kagome_lattice(kx, ky, t=1):
import numpy as np
import math

20
PyPI/src/guan/__init__.py
View File

@ -1,7 +1,23 @@
# 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):
# 函数的装饰器，用于获取计算时间
def timer_decorator(func, time_type=0):
import time
def wrapper(*args, **kwargs):
start = time.time()
result = func(*args, **kwargs)
end = time.time()
if time_type == 0:
print(f"Running time of {func.__name__}: {end - start} seconds")
elif time_type == 1:
print(f"Running time of {func.__name__}: {(end - start)/60} minutes")
elif time_type == 2:
print(f"Running time of {func.__name__}: {(end - start)/3600} hours")
return result
return wrapper
# 函数的装饰器，用于GUAN软件包的统计
def statistics_decorator(func):
def wrapper(*args, **kwargs):
result = func(*args, **kwargs)
import guan

22
PyPI/src/guan/band_structures_and_wave_functions.py
View File

@ -2,7 +2,7 @@
import guan
# 计算哈密顿量的本征值
@guan.function_decorator
@guan.statistics_decorator
def calculate_eigenvalue(hamiltonian):
import numpy as np
if np.array(hamiltonian).shape==():
@ -12,7 +12,7 @@ def calculate_eigenvalue(hamiltonian):
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
@guan.statistics_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]
@ -36,7 +36,7 @@ def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print
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
@guan.statistics_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]
@ -70,14 +70,14 @@ def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_funct
return eigenvalue_array
# 计算哈密顿量的本征矢
@guan.function_decorator
@guan.statistics_decorator
def calculate_eigenvector(hamiltonian):
import numpy as np
eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
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
@guan.statistics_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
@ -117,7 +117,7 @@ def find_vector_with_the_same_gauge_with_binary_search(vector_target, vector_ref
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
@guan.statistics_decorator
def find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=0.005, index=None):
import numpy as np
import cmath
@ -136,7 +136,7 @@ def find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=
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
@guan.statistics_decorator
def find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array, precision=0.005):
import numpy as np
import guan
@ -150,7 +150,7 @@ def find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array
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
@guan.statistics_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
@ -180,7 +180,7 @@ def rotation_of_degenerate_vectors(vector1, vector2, index1=None, index2=None, p
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
@guan.statistics_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
@ -198,7 +198,7 @@ def rotation_of_degenerate_vectors_array(vector1_array, vector2_array, precision
return vector1_array, vector2_array
# 在一组数据中找到数值相近的数
@guan.function_decorator
@guan.statistics_decorator
def find_close_values_in_one_array(array, precision=1e-2):
new_array = []
i0 = 0
@ -212,7 +212,7 @@ def find_close_values_in_one_array(array, precision=1e-2):
return new_array
# 寻找能带的简并点
@guan.function_decorator
@guan.statistics_decorator
def find_degenerate_points(k_array, eigenvalue_array, precision=1e-2):
import guan
degenerate_k_array = []

42
PyPI/src/guan/basic_functions.py
View File

@ -2,7 +2,7 @@
import guan
# 测试
@guan.function_decorator
@guan.statistics_decorator
def test():
import guan
current_version = guan.get_current_version('guan')
@ -13,118 +13,118 @@ def test():
print('\nTest completed.\n')
# 泡利矩阵
@guan.function_decorator
@guan.statistics_decorator
def sigma_0():
import numpy as np
return np.eye(2)
@guan.function_decorator
@guan.statistics_decorator
def sigma_x():
import numpy as np
return np.array([[0, 1],[1, 0]])
@guan.function_decorator
@guan.statistics_decorator
def sigma_y():
import numpy as np
return np.array([[0, -1j],[1j, 0]])
@guan.function_decorator
@guan.statistics_decorator
def sigma_z():
import numpy as np
return np.array([[1, 0],[0, -1]])
# 泡利矩阵的张量积
@guan.function_decorator
@guan.statistics_decorator
def sigma_00():
import numpy as np
import guan
return np.kron(guan.sigma_0(), guan.sigma_0())
@guan.function_decorator
@guan.statistics_decorator
def sigma_0x():
import numpy as np
import guan
return np.kron(guan.sigma_0(), guan.sigma_x())
@guan.function_decorator
@guan.statistics_decorator
def sigma_0y():
import numpy as np
import guan
return np.kron(guan.sigma_0(), guan.sigma_y())
@guan.function_decorator
@guan.statistics_decorator
def sigma_0z():
import numpy as np
import guan
return np.kron(guan.sigma_0(), guan.sigma_z())
@guan.function_decorator
@guan.statistics_decorator
def sigma_x0():
import numpy as np
import guan
return np.kron(guan.sigma_x(), guan.sigma_0())
@guan.function_decorator
@guan.statistics_decorator
def sigma_xx():
import numpy as np
import guan
return np.kron(guan.sigma_x(), guan.sigma_x())
@guan.function_decorator
@guan.statistics_decorator
def sigma_xy():
import numpy as np
import guan
return np.kron(guan.sigma_x(), guan.sigma_y())
@guan.function_decorator
@guan.statistics_decorator
def sigma_xz():
import numpy as np
import guan
return np.kron(guan.sigma_x(), guan.sigma_z())
@guan.function_decorator
@guan.statistics_decorator
def sigma_y0():
import numpy as np
import guan
return np.kron(guan.sigma_y(), guan.sigma_0())
@guan.function_decorator
@guan.statistics_decorator
def sigma_yx():
import numpy as np
import guan
return np.kron(guan.sigma_y(), guan.sigma_x())
@guan.function_decorator
@guan.statistics_decorator
def sigma_yy():
import numpy as np
import guan
return np.kron(guan.sigma_y(), guan.sigma_y())
@guan.function_decorator
@guan.statistics_decorator
def sigma_yz():
import numpy as np
import guan
return np.kron(guan.sigma_y(), guan.sigma_z())
@guan.function_decorator
@guan.statistics_decorator
def sigma_z0():
import numpy as np
import guan
return np.kron(guan.sigma_z(), guan.sigma_0())
@guan.function_decorator
@guan.statistics_decorator
def sigma_zx():
import numpy as np
import guan
return np.kron(guan.sigma_z(), guan.sigma_x())
@guan.function_decorator
@guan.statistics_decorator
def sigma_zy():
import numpy as np
import guan
return np.kron(guan.sigma_z(), guan.sigma_y())
@guan.function_decorator
@guan.statistics_decorator
def sigma_zz():
import numpy as np
import guan

98
PyPI/src/guan/data_processing.py
View File

@ -2,7 +2,7 @@
import guan
# 导入plt, fig, ax
@guan.function_decorator
@guan.statistics_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()
@ -14,7 +14,7 @@ def import_plt_and_start_fig_ax(adjust_bottom=0.2, adjust_left=0.2, labelsize=20
return plt, fig, ax
# 基于plt, fig, ax画图
@guan.function_decorator
@guan.statistics_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)
@ -31,7 +31,7 @@ def plot_without_starting_fig(plt, fig, ax, x_array, y_array, xlabel='x', ylabel
ax.set_ylim(y_min, y_max)
# 画图
@guan.function_decorator
@guan.statistics_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)
@ -52,7 +52,7 @@ def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labels
plt.close('all')
# 一组横坐标数据，两组纵坐标数据画图
@guan.function_decorator
@guan.statistics_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)
@ -78,7 +78,7 @@ def plot_two_array(x_array, y1_array, y2_array, xlabel='x', ylabel='y', title=''
plt.close('all')
# 两组横坐标数据，两组纵坐标数据画图
@guan.function_decorator
@guan.statistics_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)
@ -104,7 +104,7 @@ def plot_two_array_with_two_horizontal_array(x1_array, x2_array, y1_array, y2_ar
plt.close('all')
# 一组横坐标数据，三组纵坐标数据画图
@guan.function_decorator
@guan.statistics_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)
@ -133,7 +133,7 @@ def plot_three_array(x_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='
plt.close('all')
# 三组横坐标数据，三组纵坐标数据画图
@guan.function_decorator
@guan.statistics_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)
@ -162,7 +162,7 @@ def plot_three_array_with_three_horizontal_array(x1_array, x2_array, x3_array, y
plt.close('all')
# 画三维图
@guan.function_decorator
@guan.statistics_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
@ -204,7 +204,7 @@ def plot_3d_surface(x_array, y_array, matrix, xlabel='x', ylabel='y', zlabel='z'
plt.close('all')
# 画Contour图
@guan.function_decorator
@guan.statistics_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
@ -230,7 +230,7 @@ def plot_contour(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fon
plt.close('all')
# 画棋盘图/伪彩色图
@guan.function_decorator
@guan.statistics_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
@ -256,7 +256,7 @@ def plot_pcolor(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', font
plt.close('all')
# 通过坐标画点和线
@guan.function_decorator
@guan.statistics_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
@ -284,7 +284,7 @@ def draw_dots_and_lines(coordinate_array, draw_dots=1, draw_lines=1, max_distanc
plt.savefig(filename+file_format, dpi=dpi)
# 合并两个图片
@guan.function_decorator
@guan.statistics_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]
@ -310,7 +310,7 @@ def combine_two_images(image_path_array, figsize=(16,8), show=0, save=1, filenam
plt.close('all')
# 合并三个图片
@guan.function_decorator
@guan.statistics_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]
@ -340,7 +340,7 @@ def combine_three_images(image_path_array, figsize=(16,5), show=0, save=1, filen
plt.close('all')
# 合并四个图片
@guan.function_decorator
@guan.statistics_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]
@ -374,7 +374,7 @@ def combine_four_images(image_path_array, figsize=(16,16), show=0, save=1, filen
plt.close('all')
# 对某个目录中的txt文件批量读取和画图
@guan.function_decorator
@guan.statistics_decorator
def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
import re
import os
@ -387,7 +387,7 @@ def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
guan.plot(x_array, y_array, xlabel=xlabel, ylabel=ylabel, title=filename, show=0, save=1, filename=filename)
# 将图片制作GIF动画
@guan.function_decorator
@guan.statistics_decorator
def make_gif(image_path_array, filename='a', duration=0.1):
import imageio
images = []
@ -397,20 +397,20 @@ def make_gif(image_path_array, filename='a', duration=0.1):
imageio.mimsave(filename+'.gif', images, 'GIF', duration=duration)
# 选取Matplotlib颜色
@guan.function_decorator
@guan.statistics_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']
return color_array
# 将变量存到文件
@guan.function_decorator
@guan.statistics_decorator
def dump_data(data, filename, file_format='.txt'):
import pickle
with open(filename+file_format, 'wb') as f:
pickle.dump(data, f)
# 从文件中恢复数据到变量
@guan.function_decorator
@guan.statistics_decorator
def load_data(filename, file_format='.txt'):
import pickle
with open(filename+file_format, 'rb') as f:
@ -418,7 +418,7 @@ def load_data(filename, file_format='.txt'):
return data
# 读取文件中的一维数据（一行一组x和y）
@guan.function_decorator
@guan.statistics_decorator
def read_one_dimensional_data(filename='a', file_format='.txt'):
import numpy as np
f = open(filename+file_format, 'r')
@ -442,7 +442,7 @@ def read_one_dimensional_data(filename='a', file_format='.txt'):
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
@guan.statistics_decorator
def read_one_dimensional_complex_data(filename='a', file_format='.txt'):
import numpy as np
f = open(filename+file_format, 'r')
@ -466,7 +466,7 @@ def read_one_dimensional_complex_data(filename='a', file_format='.txt'):
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
@guan.statistics_decorator
def read_two_dimensional_data(filename='a', file_format='.txt'):
import numpy as np
f = open(filename+file_format, 'r')
@ -496,7 +496,7 @@ def read_two_dimensional_data(filename='a', file_format='.txt'):
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
@guan.statistics_decorator
def read_two_dimensional_complex_data(filename='a', file_format='.txt'):
import numpy as np
f = open(filename+file_format, 'r')
@ -526,27 +526,27 @@ def read_two_dimensional_complex_data(filename='a', file_format='.txt'):
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
@guan.statistics_decorator
def read_two_dimensional_data_without_xy_array(filename='a', file_format='.txt'):
import numpy as np
matrix = np.loadtxt(filename+file_format)
return matrix
# 打开文件用于新增内容
@guan.function_decorator
@guan.statistics_decorator
def open_file(filename='a', file_format='.txt'):
f = open(filename+file_format, 'a', encoding='UTF-8')
return f
# 在文件中写入一维数据（一行一组x和y）
@guan.function_decorator
@guan.statistics_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)
# 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
@guan.statistics_decorator
def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
import numpy as np
x_array = np.array(x_array)
@ -563,14 +563,14 @@ def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
i0 += 1
# 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
@guan.statistics_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)
# 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
@guan.statistics_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)
@ -591,14 +591,14 @@ def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f)
i0 += 1
# 在文件中写入二维数据（不包括x和y）
@guan.function_decorator
@guan.statistics_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)
# 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
@guan.statistics_decorator
def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f):
for row in matrix:
for element in row:
@ -607,7 +607,7 @@ def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix,
import guan
# 以显示编号的样式，打印数组
@guan.function_decorator
@guan.statistics_decorator
def print_array_with_index(array, show_index=1, index_type=0):
if show_index==0:
for i0 in array:
@ -625,7 +625,7 @@ def print_array_with_index(array, show_index=1, index_type=0):
print(index, i0)
# 获取目录中的所有文件名
@guan.function_decorator
@guan.statistics_decorator
def get_all_filenames_in_directory(directory='./', file_format=None):
import os
file_list = []
@ -639,7 +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类型的文件路径和内容
@guan.function_decorator
@guan.statistics_decorator
def read_text_files_in_directory(directory='./', file_format='.md'):
import os
file_list = []
@ -654,7 +654,7 @@ def read_text_files_in_directory(directory='./', file_format='.md'):
return file_list, content_array
# 在多个文本文件中查找关键词
@guan.function_decorator
@guan.statistics_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)
@ -666,7 +666,7 @@ def find_words_in_multiple_files(words, directory='./', file_format='.md'):
return file_list_with_words
# 并行计算前的预处理，把参数分成多份
@guan.function_decorator
@guan.statistics_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]
@ -682,14 +682,14 @@ def preprocess_for_parallel_calculations(parameter_array_all, cpus=1, task_index
return parameter_array
# 随机获得一个整数，左闭右闭
@guan.function_decorator
@guan.statistics_decorator
def get_random_number(start=0, end=1):
import random
rand_number = random.randint(start, end) # 左闭右闭 [start, end]
return rand_number
# 选取一个种子生成固定的随机整数
@guan.function_decorator
@guan.statistics_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)
@ -697,14 +697,14 @@ def generate_random_int_number_for_a_specific_seed(seed=0, x_min=0, x_max=10):
return rand_num
# 使用jieba软件包进行分词
@guan.function_decorator
@guan.statistics_decorator
def divide_text_into_words(text):
import jieba
words = jieba.lcut(text)
return words
# 判断某个字符是中文还是英文或其他
@guan.function_decorator
@guan.statistics_decorator
def check_Chinese_or_English(a):
if '\u4e00' <= a <= '\u9fff' :
word_type = 'Chinese'
@ -715,7 +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
@guan.statistics_decorator
def count_words(text, include_space=0, show_words=0):
import jieba
import guan
@ -743,7 +743,7 @@ def count_words(text, include_space=0, show_words=0):
return num_words
# 将RGB转成HEX
@guan.function_decorator
@guan.statistics_decorator
def rgb_to_hex(rgb, pound=1):
if pound==0:
return '%02x%02x%02x' % rgb
@ -751,14 +751,14 @@ def rgb_to_hex(rgb, pound=1):
return '#%02x%02x%02x' % rgb
# 将HEX转成RGB
@guan.function_decorator
@guan.statistics_decorator
def hex_to_rgb(hex):
hex = hex.lstrip('#')
length = len(hex)
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
@guan.statistics_decorator
def encryption_MD5(password, salt=''):
import hashlib
password = salt+password
@ -766,7 +766,7 @@ def encryption_MD5(password, salt=''):
return hashed_password
# 使用SHA-256进行散列加密
@guan.function_decorator
@guan.statistics_decorator
def encryption_SHA_256(password, salt=''):
import hashlib
password = salt+password
@ -774,7 +774,7 @@ def encryption_SHA_256(password, salt=''):
return hashed_password
# 自动先后运行程序
@guan.function_decorator
@guan.statistics_decorator
def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='python ', show_time=0):
import os
import time
@ -794,20 +794,20 @@ def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='pytho
print('Total running time = '+str((end-start)/60)+' min')
# 如果不存在文件夹，则新建文件夹
@guan.function_decorator
@guan.statistics_decorator
def make_directory(directory='./test'):
import os
if not os.path.exists(directory):
os.makedirs(directory)
# 复制一份文件
@guan.function_decorator
@guan.statistics_decorator
def copy_file(file1='./a.txt', file2='./b.txt'):
import shutil
shutil.copy(file1, file2)
# 拼接两个PDF文件
@guan.function_decorator
@guan.statistics_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()

14
PyPI/src/guan/density_of_states.py
View File

@ -2,7 +2,7 @@
import guan
# 计算体系的总态密度
@guan.function_decorator
@guan.statistics_decorator
def total_density_of_states(fermi_energy, hamiltonian, broadening=0.01):
import numpy as np
import math
@ -12,7 +12,7 @@ def total_density_of_states(fermi_energy, hamiltonian, broadening=0.01):
return total_dos
# 对于不同费米能，计算体系的总态密度
@guan.function_decorator
@guan.statistics_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
@ -27,7 +27,7 @@ def total_density_of_states_with_fermi_energy_array(fermi_energy_array, hamilton
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
@guan.statistics_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
@ -41,7 +41,7 @@ def local_density_of_states_for_square_lattice(fermi_energy, hamiltonian, N1, N2
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
@guan.statistics_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
@ -56,7 +56,7 @@ def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2,
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
@guan.statistics_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
@ -90,7 +90,7 @@ def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy
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
@guan.statistics_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
@ -125,7 +125,7 @@ def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy,
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
@guan.statistics_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

16
PyPI/src/guan/machine_learning.py
View File

@ -2,7 +2,7 @@
import guan
# 全连接神经网络模型（包含一个隐藏层）（模型的类定义成全局的）
@guan.function_decorator
@guan.statistics_decorator
def fully_connected_neural_network_with_one_hidden_layer(input_size=1, hidden_size=10, output_size=1, activation='relu'):
import torch
global model_class_of_fully_connected_neural_network_with_one_hidden_layer
@ -28,7 +28,7 @@ def fully_connected_neural_network_with_one_hidden_layer(input_size=1, hidden_si
return model
# 全连接神经网络模型（包含两个隐藏层）（模型的类定义成全局的）
@guan.function_decorator
@guan.statistics_decorator
def fully_connected_neural_network_with_two_hidden_layers(input_size=1, hidden_size_1=10, hidden_size_2=10, output_size=1, activation_1='relu', activation_2='relu'):
import torch
global model_class_of_fully_connected_neural_network_with_two_hidden_layers
@ -67,7 +67,7 @@ def fully_connected_neural_network_with_two_hidden_layers(input_size=1, hidden_s
return model
# 全连接神经网络模型（包含三个隐藏层）（模型的类定义成全局的）
@guan.function_decorator
@guan.statistics_decorator
def fully_connected_neural_network_with_three_hidden_layers(input_size=1, hidden_size_1=10, hidden_size_2=10, hidden_size_3=10, output_size=1, activation_1='relu', activation_2='relu', activation_3='relu'):
import torch
global model_class_of_fully_connected_neural_network_with_three_hidden_layers
@ -118,7 +118,7 @@ def fully_connected_neural_network_with_three_hidden_layers(input_size=1, hidden
return model
# 使用优化器训练模型
@guan.function_decorator
@guan.statistics_decorator
def train_model(model, x_data, y_data, optimizer='Adam', learning_rate=0.001, criterion='MSELoss', num_epochs=1000, print_show=1):
import torch
if optimizer == 'Adam':
@ -145,26 +145,26 @@ def train_model(model, x_data, y_data, optimizer='Adam', learning_rate=0.001, cr
return model, losses
# 保存模型参数到文件
@guan.function_decorator
@guan.statistics_decorator
def save_model_parameters(model, filename='./model_parameters.pth'):
import torch
torch.save(model.state_dict(), filename)
# 保存完整模型到文件（保存时需要模型的类可访问）
@guan.function_decorator
@guan.statistics_decorator
def save_model(model, filename='./model.pth'):
import torch
torch.save(model, filename)
# 加载模型参数（需要输入模型，加载后，原输入的模型参数也会改变）
@guan.function_decorator
@guan.statistics_decorator
def load_model_parameters(model, filename='./model_parameters.pth'):
import torch
model.load_state_dict(torch.load(filename))
return model
# 加载完整模型（不需要输入模型，但加载时需要原定义的模型的类可访问）
@guan.function_decorator
@guan.statistics_decorator
def load_model(filename='./model.pth'):
import torch
model = torch.load(filename)

158
PyPI/src/guan/others.py
View File

@ -2,14 +2,14 @@
import guan
# 获取CPU使用率
@guan.function_decorator
@guan.statistics_decorator
def get_cpu_usage(interval=1):
import psutil
cpu_usage = psutil.cpu_percent(interval=interval)
return cpu_usage
# 获取内存信息
@guan.function_decorator
@guan.statistics_decorator
def get_memory_info():
import psutil
memory_info = psutil.virtual_memory()
@ -20,7 +20,7 @@ def get_memory_info():
return total_memory, used_memory, available_memory, used_memory_percent
# 将WordPress导出的XML格式文件转换成多个MarkDown格式的文件
@guan.function_decorator
@guan.statistics_decorator
def convert_wordpress_xml_to_markdown(xml_file='./a.xml', convert_content=1, replace_more=[]):
import xml.etree.ElementTree as ET
import re
@ -58,7 +58,7 @@ def convert_wordpress_xml_to_markdown(xml_file='./a.xml', convert_content=1, rep
md_file.write(markdown_content)
# 获取运行的日期和时间并写入文件
@guan.function_decorator
@guan.statistics_decorator
def statistics_with_day_and_time(content='', filename='a', file_format='.txt'):
import datetime
datetime_today = str(datetime.date.today())
@ -70,7 +70,7 @@ def statistics_with_day_and_time(content='', filename='a', file_format='.txt'):
f2.write(datetime_today+' '+datetime_time+' '+content+'\n')
# 统计Python文件中import的数量并排序
@guan.function_decorator
@guan.statistics_decorator
def count_number_of_import_statements(filename, file_format='.py', num=1000):
with open(filename+file_format, 'r') as file:
lines = file.readlines()
@ -84,14 +84,14 @@ def count_number_of_import_statements(filename, file_format='.py', num=1000):
return import_statement_counter
# 根据一定的字符长度来分割文本
@guan.function_decorator
@guan.statistics_decorator
def split_text(text, wrap_width=3000):
import textwrap
split_text_list = textwrap.wrap(text, wrap_width)
return split_text_list
# 获取本月的所有日期
@guan.function_decorator
@guan.statistics_decorator
def get_days_of_the_current_month(str_or_datetime='str'):
import datetime
today = datetime.date.today()
@ -111,7 +111,7 @@ def get_days_of_the_current_month(str_or_datetime='str'):
return day_array
# 获取上个月份
@guan.function_decorator
@guan.statistics_decorator
def get_last_month():
import datetime
today = datetime.date.today()
@ -124,7 +124,7 @@ def get_last_month():
return year_of_last_month, last_month
# 获取上上个月份
@guan.function_decorator
@guan.statistics_decorator
def get_the_month_before_last():
import datetime
today = datetime.date.today()
@ -142,7 +142,7 @@ def get_the_month_before_last():
return year_of_the_month_before_last, the_month_before_last
# 获取上个月的所有日期
@guan.function_decorator
@guan.statistics_decorator
def get_days_of_the_last_month(str_or_datetime='str'):
import datetime
import guan
@ -164,7 +164,7 @@ def get_days_of_the_last_month(str_or_datetime='str'):
return day_array
# 获取上上个月的所有日期
@guan.function_decorator
@guan.statistics_decorator
def get_days_of_the_month_before_last(str_or_datetime='str'):
import datetime
import guan
@ -186,7 +186,7 @@ def get_days_of_the_month_before_last(str_or_datetime='str'):
return day_array
# 获取所有股票
@guan.function_decorator
@guan.statistics_decorator
def all_stocks():
import numpy as np
import akshare as ak
@ -196,7 +196,7 @@ def all_stocks():
return title, stock_data
# 获取所有股票的代码
@guan.function_decorator
@guan.statistics_decorator
def all_stock_symbols():
import guan
title, stock_data = guan.all_stocks()
@ -204,7 +204,7 @@ def all_stock_symbols():
return stock_symbols
# 从股票代码获取股票名称
@guan.function_decorator
@guan.statistics_decorator
def find_stock_name_from_symbol(symbol='000002'):
import guan
title, stock_data = guan.all_stocks()
@ -214,7 +214,7 @@ def find_stock_name_from_symbol(symbol='000002'):
return stock_name
# 获取单个股票的历史数据
@guan.function_decorator
@guan.statistics_decorator
def history_data_of_one_stock(symbol='000002', period='daily', start_date="19000101", end_date='21000101'):
# period = 'daily'
# period = 'weekly'
@ -227,7 +227,7 @@ def history_data_of_one_stock(symbol='000002', period='daily', start_date="19000
return title, stock_data
# 获取软件包中的所有模块名
@guan.function_decorator
@guan.statistics_decorator
def get_all_modules_in_one_package(package_name='guan'):
import pkgutil
package = __import__(package_name)
@ -235,7 +235,7 @@ def get_all_modules_in_one_package(package_name='guan'):
return module_names
# 获取软件包中一个模块的所有函数名
@guan.function_decorator
@guan.statistics_decorator
def get_all_functions_in_one_module(module_name, package_name='guan'):
import inspect
function_names = []
@ -246,7 +246,7 @@ def get_all_functions_in_one_module(module_name, package_name='guan'):
return function_names
# 获取软件包中的所有函数名
@guan.function_decorator
@guan.statistics_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)
@ -264,7 +264,7 @@ def get_all_functions_in_one_package(package_name='guan', print_show=1):
return all_function_names
# 获取包含某个字符的进程PID值
@guan.function_decorator
@guan.statistics_decorator
def get_PID(name):
import subprocess
command = "ps -ef | grep "+name
@ -277,90 +277,14 @@ def get_PID(name):
return id_running
# 获取函数的源码
@guan.function_decorator
@guan.statistics_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
import guan
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as client_socket:
client_socket.connect(('socket.guanjihuan.com', 12345))
function_source = guan.get_function_source(function_name)
message = {
'server': "python",
'function_name': function_name.__name__,
'function_source': function_source,
'args': str(args),
'get_print': get_print,
}
send_message = json.dumps(message)
client_socket.send(send_message.encode())
return_data = None
while True:
try:
data = client_socket.recv(1024)
return_text = data.decode()
return_dict = json.loads(return_text)
return_data = return_dict['return_data']
print_data = return_dict['print_data']
end_message = return_dict['end_message']
if get_print == 1:
print(print_data)
if return_show == 1:
print(return_data)
if end_message == 1 or return_text == '':
break
except:
break
client_socket.close()
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
response = ''
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as client_socket:
client_socket.settimeout(15)
client_socket.connect(('socket.guanjihuan.com', 12345))
message = {
'server': "chat.guanjihuan.com",
'prompt': prompt,
'top_p': top_p,
'temperature': temperature,
}
send_message = json.dumps(message)
client_socket.send(send_message.encode())
while True:
try:
data = client_socket.recv(1024)
stream_response = data.decode()
response_dict = json.loads(stream_response)
stream_response = response_dict['response']
end_message = response_dict['end_message']
if end_message == 1:
break
elif stream_response == '':
break
else:
if stream_show == 1:
print(stream_response)
print('\n---\n')
response = stream_response
except:
break
client_socket.close()
return response
# 查找文件名相同的文件
@guan.function_decorator
@guan.statistics_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
@ -385,7 +309,7 @@ def find_repeated_file_with_same_filename(directory='./', ignored_directory_with
return repeated_file
# 统计各个子文件夹中的文件数量
@guan.function_decorator
@guan.statistics_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
@ -439,7 +363,7 @@ def count_file_in_sub_directory(directory='./', sort=0, reverse=1, print_show=1,
return sub_directory, num_in_sub_directory
# 改变当前的目录位置
@guan.function_decorator
@guan.statistics_decorator
def change_directory_by_replacement(current_key_word='code', new_key_word='data'):
import os
code_path = os.getcwd()
@ -450,7 +374,7 @@ def change_directory_by_replacement(current_key_word='code', new_key_word='data'
os.chdir(data_path)
# 在多个子文件夹中产生必要的文件，例如 readme.md
@guan.function_decorator
@guan.statistics_decorator
def creat_necessary_file(directory, filename='readme', file_format='.md', content='', overwrite=None, ignored_directory_with_words=[]):
import os
directory_with_file = []
@ -480,7 +404,7 @@ def creat_necessary_file(directory, filename='readme', file_format='.md', conten
f.close()
# 删除特定文件名的文件（慎用）
@guan.function_decorator
@guan.statistics_decorator
def delete_file_with_specific_name(directory, filename='readme', file_format='.md'):
import os
for root, dirs, files in os.walk(directory):
@ -489,7 +413,7 @@ def delete_file_with_specific_name(directory, filename='readme', file_format='.m
os.remove(root+'/'+files[i0])
# 将所有文件移到根目录（慎用）
@guan.function_decorator
@guan.statistics_decorator
def move_all_files_to_root_directory(directory):
import os
import shutil
@ -504,7 +428,7 @@ def move_all_files_to_root_directory(directory):
pass
# 将文件目录结构写入Markdown文件
@guan.function_decorator
@guan.statistics_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")
@ -608,7 +532,7 @@ def write_file_list_in_markdown(directory='./', filename='a', reverse_positive_o
f.close()
# 从网页的标签中获取内容
@guan.function_decorator
@guan.statistics_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
@ -627,14 +551,14 @@ def get_html_from_tags(link, tags=['title', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6',
return content
# 生成二维码
@guan.function_decorator
@guan.statistics_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)
# 将PDF文件转成文本
@guan.function_decorator
@guan.statistics_decorator
def pdf_to_text(pdf_path):
from pdfminer.pdfparser import PDFParser, PDFDocument
from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
@ -666,7 +590,7 @@ def pdf_to_text(pdf_path):
return content
# 获取PDF文件页数
@guan.function_decorator
@guan.statistics_decorator
def get_pdf_page_number(pdf_path):
import PyPDF2
pdf_file = open(pdf_path, 'rb')
@ -675,7 +599,7 @@ def get_pdf_page_number(pdf_path):
return num_pages
# 获取PDF文件指定页面的内容
@guan.function_decorator
@guan.statistics_decorator
def pdf_to_txt_for_a_specific_page(pdf_path, page_num=1):
import PyPDF2
pdf_file = open(pdf_path, 'rb')
@ -689,7 +613,7 @@ def pdf_to_txt_for_a_specific_page(pdf_path, page_num=1):
return page_text
# 获取PDF文献中的链接。例如: link_starting_form='https://doi.org'
@guan.function_decorator
@guan.statistics_decorator
def get_links_from_pdf(pdf_path, link_starting_form=''):
import PyPDF2
import re
@ -714,7 +638,7 @@ def get_links_from_pdf(pdf_path, link_starting_form=''):
return links
# 通过Sci-Hub网站下载文献
@guan.function_decorator
@guan.statistics_decorator
def download_with_scihub(address=None, num=1):
from bs4 import BeautifulSoup
import re
@ -752,7 +676,7 @@ def download_with_scihub(address=None, num=1):
print('All completed!\n')
# 将字符串转成音频
@guan.function_decorator
@guan.statistics_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
@ -776,7 +700,7 @@ def str_to_audio(str='hello world', filename='str', rate=125, voice=1, read=1, s
engine.runAndWait()
# 将txt文件转成音频
@guan.function_decorator
@guan.statistics_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
@ -804,7 +728,7 @@ def txt_to_audio(txt_path, rate=125, voice=1, read=1, save=0, compress=0, bitrat
engine.runAndWait()
# 将PDF文件转成音频
@guan.function_decorator
@guan.statistics_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
@ -832,7 +756,7 @@ def pdf_to_audio(pdf_path, rate=125, voice=1, read=1, save=0, compress=0, bitrat
engine.runAndWait()
# 将wav音频文件压缩成MP3音频文件
@guan.function_decorator
@guan.statistics_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
@ -840,7 +764,7 @@ def compress_wav_to_mp3(wav_path, output_filename='a.mp3', bitrate='16k'):
sound.export(output_filename,format="mp3",bitrate=bitrate)
# 播放学术单词
@guan.function_decorator
@guan.statistics_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
@ -901,7 +825,7 @@ def play_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_translati
print()
# 播放挑选过后的学术单词
@guan.function_decorator
@guan.statistics_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
@ -958,7 +882,7 @@ def play_selected_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_
print()
# 播放元素周期表上的单词
@guan.function_decorator
@guan.statistics_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

46
PyPI/src/guan/quantum_transport.py
View File

@ -2,7 +2,7 @@
import guan
# 计算电导
@guan.function_decorator
@guan.statistics_decorator
def calculate_conductance(fermi_energy, h00, h01, length=100):
import numpy as np
import copy
@ -22,7 +22,7 @@ def calculate_conductance(fermi_energy, h00, h01, length=100):
return conductance
# 计算不同费米能下的电导
@guan.function_decorator
@guan.statistics_decorator
def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01, length=100, print_show=0):
import numpy as np
import guan
@ -37,7 +37,7 @@ def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01,
return conductance_array
# 计算在势垒散射下的电导
@guan.function_decorator
@guan.statistics_decorator
def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barrier_length=20, barrier_potential=1):
import numpy as np
import copy
@ -61,7 +61,7 @@ def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barri
return conductance
# 计算在无序散射下的电导
@guan.function_decorator
@guan.statistics_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
@ -90,7 +90,7 @@ def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensi
return conductance_averaged
# 计算在无序垂直切片的散射下的电导
@guan.function_decorator
@guan.statistics_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
@ -114,7 +114,7 @@ def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_i
return conductance
# 计算在无序水平切片的散射下的电导
@guan.function_decorator
@guan.statistics_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
@ -139,7 +139,7 @@ def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translation
return conductance
# 计算在随机空位的散射下的电导
@guan.function_decorator
@guan.statistics_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
@ -164,7 +164,7 @@ def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_co
return conductance
# 计算在不同无序散射强度下的电导
@guan.function_decorator
@guan.statistics_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
@ -181,7 +181,7 @@ def calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01,
return conductance_array
# 计算在不同无序浓度下的电导
@guan.function_decorator
@guan.statistics_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
@ -198,7 +198,7 @@ def calculate_conductance_with_disorder_concentration_array(fermi_energy, h00, h
return conductance_array
# 计算在不同无序散射长度下的电导
@guan.function_decorator
@guan.statistics_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
@ -215,7 +215,7 @@ def calculate_conductance_with_scattering_length_array(fermi_energy, h00, h01, l
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
@guan.statistics_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
@ -262,7 +262,7 @@ def get_gamma_array_and_green_for_six_terminal_transmission(fermi_energy, h00_fo
return gamma_array, green
# 计算六端口的透射矩阵
@guan.function_decorator
@guan.statistics_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
@ -287,7 +287,7 @@ def calculate_six_terminal_transmission_matrix(fermi_energy, h00_for_lead_4, h01
return transmission_matrix
# 计算从电极1出发的透射系数
@guan.function_decorator
@guan.statistics_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
@ -300,7 +300,7 @@ def calculate_six_terminal_transmissions_from_lead_1(fermi_energy, h00_for_lead_
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
@guan.statistics_decorator
def if_active_channel(k_of_channel):
import numpy as np
if np.abs(np.imag(k_of_channel))<1e-6:
@ -310,7 +310,7 @@ def if_active_channel(k_of_channel):
return if_active
# 获取通道的动量和速度，用于计算散射矩阵
@guan.function_decorator
@guan.statistics_decorator
def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
import numpy as np
import math
@ -347,7 +347,7 @@ def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
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
@guan.statistics_decorator
def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01):
import numpy as np
import guan
@ -400,7 +400,7 @@ def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01):
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
@guan.statistics_decorator
def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
import numpy as np
import math
@ -446,7 +446,7 @@ def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
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
@guan.statistics_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==():
@ -468,7 +468,7 @@ def information_of_scattering_matrix(transmission_matrix, reflection_matrix, k_r
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
@guan.statistics_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)
@ -478,7 +478,7 @@ def calculate_scattering_matrix_and_get_information(fermi_energy, h00, h01, leng
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
@guan.statistics_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)
@ -523,7 +523,7 @@ def print_or_write_scattering_matrix_with_information_of_scattering_matrix(numbe
f.write('Total conductance = '+str(total_conductance)+'\n')
# 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
@guan.statistics_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)
@ -533,7 +533,7 @@ 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)
# 在无序下，计算散射矩阵
@guan.function_decorator
@guan.statistics_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
@ -583,7 +583,7 @@ def calculate_scattering_matrix_with_disorder(fermi_energy, h00, h01, length=100
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
@guan.statistics_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

20
PyPI/src/guan/topological_invariant.py
View File

@ -2,7 +2,7 @@
import guan
# 通过高效法计算方格子的陈数
@guan.function_decorator
@guan.statistics_decorator
def calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_function, precision=100, print_show=0):
import numpy as np
import math
@ -41,7 +41,7 @@ def calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_
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
@guan.statistics_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
@ -111,7 +111,7 @@ def calculate_chern_number_for_square_lattice_with_efficient_method_for_degenera
return chern_number
# 通过Wilson loop方法计算方格子的陈数
@guan.function_decorator
@guan.statistics_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
@ -156,7 +156,7 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_funct
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
@guan.statistics_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
@ -220,7 +220,7 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_ca
return chern_number
# 通过高效法计算贝利曲率
@guan.function_decorator
@guan.statistics_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
@ -267,7 +267,7 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=
return k_array, berry_curvature_array
# 通过高效法计算贝利曲率（可计算简并的情况）
@guan.function_decorator
@guan.statistics_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
@ -346,7 +346,7 @@ def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamilton
return k_array, berry_curvature_array
# 通过Wilson loop方法计算贝里曲率
@guan.function_decorator
@guan.statistics_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
@ -403,7 +403,7 @@ def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min='defa
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
@guan.statistics_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
@ -475,7 +475,7 @@ def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_f
return k_array, berry_curvature_array
# 计算蜂窝格子的陈数（高效法）
@guan.function_decorator
@guan.statistics_decorator
def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0):
import numpy as np
import math
@ -519,7 +519,7 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
return chern_number
# 计算Wilson loop
@guan.function_decorator
@guan.statistics_decorator
def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
import numpy as np
import guan