0.1.83 (取消函数的使用统计)

2024-02-23 03:22:00 +08:00 · 2024-02-23 03:22:00 +08:00 · a2abdd790a
commit a2abdd790a
parent 2602518fed
21 changed files with 38 additions and 288 deletions
--- a/PyPI/dist/guan-0.1.83-py3-none-any.whl
+++ b/PyPI/dist/guan-0.1.83-py3-none-any.whl
--- a/PyPI/dist/guan-0.1.83.tar.gz
+++ b/PyPI/dist/guan-0.1.83.tar.gz
--- a/PyPI/setup.cfg
+++ b/PyPI/setup.cfg
@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.82
+version = 0.1.83
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
--- a/PyPI/src/guan.egg-info/PKG-INFO
+++ b/PyPI/src/guan.egg-info/PKG-INFO
@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: guan
-Version: 0.1.82
+Version: 0.1.83
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan
--- a/PyPI/src/guan.egg-info/SOURCES.txt
+++ b/PyPI/src/guan.egg-info/SOURCES.txt
@ -9,7 +9,7 @@ src/guan/__init__.py
 src/guan/band_structures_and_wave_functions.py
 src/guan/basic_functions.py
 src/guan/data_processing.py
-src/guan/decorator.py
+src/guan/decorators.py
 src/guan/density_of_states.py
 src/guan/figure_plotting.py
 src/guan/file_reading_and_writing.py
--- a/PyPI/src/guan/Fourier_transform.py
+++ b/PyPI/src/guan/Fourier_transform.py
@ -1,8 +1,6 @@
 # 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.statistics_decorator
 def one_dimensional_fourier_transform(k, unit_cell, hopping):
    import numpy as np
    import cmath
@ -12,7 +10,6 @@ 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.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 +20,6 @@ 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.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 +31,6 @@ 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.statistics_decorator
 def one_dimensional_fourier_transform_with_k(unit_cell, hopping):
    import functools
    import guan
@ -43,7 +38,6 @@ 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.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 +45,6 @@ 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.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 +52,12 @@ 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.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.statistics_decorator
 def calculate_two_dimensional_reciprocal_lattice_vectors(a1, a2):
    import numpy as np
    a1 = np.array(a1)
@ -81,7 +72,6 @@ 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.statistics_decorator
 def calculate_three_dimensional_reciprocal_lattice_vectors(a1, a2, a3):
    import numpy as np
    a1 = np.array(a1)
@ -93,14 +83,12 @@ 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.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.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 +103,6 @@ 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.statistics_decorator
 def calculate_three_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2, a3):
    import sympy
    cross_a2_a3 = a2.cross(a3)
--- a/PyPI/src/guan/Green_functions.py
+++ b/PyPI/src/guan/Green_functions.py
@ -1,8 +1,6 @@
 # Module: Green_functions
-import guan

 # 输入哈密顿量，得到格林函数
-@guan.statistics_decorator
 def green_function(fermi_energy, hamiltonian, broadening, self_energy=0):
    import numpy as np
    if np.array(hamiltonian).shape==():
@ -13,7 +11,6 @@ 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.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 +22,12 @@ 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.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.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 +35,12 @@ 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.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.statistics_decorator
 def transfer_matrix(fermi_energy, h00, h01):
    import numpy as np
    h01 = np.array(h01)
@ -63,7 +56,6 @@ def transfer_matrix(fermi_energy, h00, h01):
    return transfer

 # 计算电极的表面格林函数
-@guan.statistics_decorator
 def surface_green_function_of_lead(fermi_energy, h00, h01):
    import numpy as np
    h01 = np.array(h01)
@ -89,7 +81,6 @@ 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.statistics_decorator
 def self_energy_of_lead(fermi_energy, h00, h01):
    import numpy as np
    import guan
@ -102,7 +93,6 @@ 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.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 +106,6 @@ 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.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 +116,6 @@ 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.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 +125,6 @@ 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.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
--- a/PyPI/src/guan/Hamiltonian_of_examples.py
+++ b/PyPI/src/guan/Hamiltonian_of_examples.py
@ -1,8 +1,6 @@
 # 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.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 +21,6 @@ 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.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 +53,6 @@ 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.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 +101,6 @@ 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.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 +118,6 @@ 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.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 +131,6 @@ 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.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 +142,6 @@ 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.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 +162,6 @@ 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.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 +195,6 @@ 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.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 +218,6 @@ 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.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 +241,18 @@ 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.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.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.statistics_decorator
 def hamiltonian_of_square_lattice_in_quasi_one_dimension(k, N=10, period=0):
    import numpy as np
    import guan
@ -284,14 +270,12 @@ 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.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.statistics_decorator
 def hamiltonian_of_ssh_model(k, v=0.6, w=1):
    import numpy as np
    import cmath
@ -301,7 +285,6 @@ 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.statistics_decorator
 def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
    import numpy as np
    import cmath
@ -318,7 +301,6 @@ 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.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 +316,6 @@ 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.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 +331,6 @@ 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.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 +360,6 @@ 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.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 +381,6 @@ 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.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 +435,6 @@ 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.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 +446,6 @@ 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.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 +464,6 @@ 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.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 +478,6 @@ 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.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 +492,6 @@ 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.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 +511,6 @@ 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.statistics_decorator
 def hamiltonian_of_kagome_lattice(kx, ky, t=1):
    import numpy as np
    import math
@ -554,7 +526,6 @@ def hamiltonian_of_kagome_lattice(kx, ky, t=1):
    return hamiltonian

 # 超蜂窝晶格的哈密顿量（倒空间）
-@guan.statistics_decorator
 def hamiltonian_of_hyperhoneycomb_lattice(kx, ky, kz, t=1, a=1):
    import cmath
    import numpy as np
--- a/PyPI/src/guan/init.py
+++ b/PyPI/src/guan/init.py
@ -1,6 +1,5 @@
 # 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.

-from .decorator import *
 from .basic_functions import *
 from .Fourier_transform import *
 from .Hamiltonian_of_examples import *
@ -14,3 +13,4 @@ from .file_reading_and_writing import *
 from .figure_plotting import *
 from .data_processing import *
 from .others import *
+from .decorators import *
--- a/PyPI/src/guan/band_structures_and_wave_functions.py
+++ b/PyPI/src/guan/band_structures_and_wave_functions.py
@ -1,8 +1,6 @@
 # Module: band_structures_and_wave_functions
-import guan

 # 计算哈密顿量的本征值
-@guan.statistics_decorator
 def calculate_eigenvalue(hamiltonian):
    import numpy as np
    if np.array(hamiltonian).shape==():
@ -12,7 +10,6 @@ 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.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 +33,6 @@ 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.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 +66,12 @@ 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.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.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 +111,6 @@ def find_vector_with_the_same_gauge_with_binary_search(vector_target, vector_ref
    return vector_target

 # 通过乘一个相反的相位角，实现波函数的一个非零分量为实数，从而得到固定规范的波函数
-@guan.statistics_decorator
 def find_vector_with_fixed_gauge_by_making_one_component_real(vector, index=None):
    import numpy as np
    import cmath
@ -129,7 +122,6 @@ def find_vector_with_fixed_gauge_by_making_one_component_real(vector, index=None
    return vector

 # 通过乘一个相反的相位角，实现波函数的一个非零分量为实数，从而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.statistics_decorator
 def find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array):
    import numpy as np
    import guan
@ -143,7 +135,6 @@ def find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array
    return vector_array

 # 循环查找规范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.statistics_decorator
 def loop_find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=0.005, index=None):
    import numpy as np
    import cmath
@ -162,7 +153,6 @@ def loop_find_vector_with_fixed_gauge_by_making_one_component_real(vector, preci
    return vector

 # 循环查找规范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.statistics_decorator
 def loop_find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array, precision=0.005):
    import numpy as np
    import guan
@ -176,7 +166,6 @@ def loop_find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_
    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.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
@ -206,7 +195,6 @@ 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.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
@ -224,7 +212,6 @@ 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.statistics_decorator
 def find_close_values_in_one_array(array, precision=1e-2):
    new_array = []
    i0 = 0
@ -238,7 +225,6 @@ def find_close_values_in_one_array(array, precision=1e-2):
    return new_array

 # 寻找能带的简并点
-@guan.statistics_decorator
 def find_degenerate_points(k_array, eigenvalue_array, precision=1e-2):
    import guan
    degenerate_k_array = []
--- a/PyPI/src/guan/basic_functions.py
+++ b/PyPI/src/guan/basic_functions.py
@ -1,126 +1,104 @@
 # Module: basic_functions
-import guan

 # 测试
-@guan.statistics_decorator
 def test():
    import guan
    current_version = guan.get_current_version('guan')
    print(f'Congratulations on successfully installing Guan package! The installed version is guan-{current_version}.')

 # 泡利矩阵
-@guan.statistics_decorator
 def sigma_0():
    import numpy as np
    return np.eye(2)

-@guan.statistics_decorator
 def sigma_x():
    import numpy as np
    return np.array([[0, 1],[1, 0]])

-@guan.statistics_decorator
 def sigma_y():
    import numpy as np
    return np.array([[0, -1j],[1j, 0]])

-@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.statistics_decorator
 def sigma_00():
    import numpy as np
    import guan
    return np.kron(guan.sigma_0(), guan.sigma_0())

-@guan.statistics_decorator
 def sigma_0x():
    import numpy as np
    import guan
    return np.kron(guan.sigma_0(), guan.sigma_x())

-@guan.statistics_decorator
 def sigma_0y():
    import numpy as np
    import guan
    return np.kron(guan.sigma_0(), guan.sigma_y())

-@guan.statistics_decorator
 def sigma_0z():
    import numpy as np
    import guan
    return np.kron(guan.sigma_0(), guan.sigma_z())

-@guan.statistics_decorator
 def sigma_x0():
    import numpy as np
    import guan
    return np.kron(guan.sigma_x(), guan.sigma_0())

-@guan.statistics_decorator
 def sigma_xx():
    import numpy as np
    import guan
    return np.kron(guan.sigma_x(), guan.sigma_x())

-@guan.statistics_decorator
 def sigma_xy():
    import numpy as np
    import guan
    return np.kron(guan.sigma_x(), guan.sigma_y())

-@guan.statistics_decorator
 def sigma_xz():
    import numpy as np
    import guan
    return np.kron(guan.sigma_x(), guan.sigma_z())

-@guan.statistics_decorator
 def sigma_y0():
    import numpy as np
    import guan
    return np.kron(guan.sigma_y(), guan.sigma_0())

-@guan.statistics_decorator
 def sigma_yx():
    import numpy as np
    import guan
    return np.kron(guan.sigma_y(), guan.sigma_x())

-@guan.statistics_decorator
 def sigma_yy():
    import numpy as np
    import guan
    return np.kron(guan.sigma_y(), guan.sigma_y())

-@guan.statistics_decorator
 def sigma_yz():
    import numpy as np
    import guan
    return np.kron(guan.sigma_y(), guan.sigma_z())

-@guan.statistics_decorator
 def sigma_z0():
    import numpy as np
    import guan
    return np.kron(guan.sigma_z(), guan.sigma_0())

-@guan.statistics_decorator
 def sigma_zx():
    import numpy as np
    import guan
    return np.kron(guan.sigma_z(), guan.sigma_x())

-@guan.statistics_decorator
 def sigma_zy():
    import numpy as np
    import guan
    return np.kron(guan.sigma_z(), guan.sigma_y())

-@guan.statistics_decorator
 def sigma_zz():
    import numpy as np
    import guan
--- a/PyPI/src/guan/data_processing.py
+++ b/PyPI/src/guan/data_processing.py
@ -1,8 +1,6 @@
 # Module: data_processing
-import guan

 # 并行计算前的预处理，把参数分成多份
-@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]
@ -18,20 +16,17 @@ def preprocess_for_parallel_calculations(parameter_array_all, cpus=1, task_index
    return parameter_array

 # 根据子数组的第index个元素对子数组进行排序（index从0开始）
-@guan.statistics_decorator
 def sort_array_by_index_element(original_array, index):
    sorted_array = sorted(original_array, key=lambda x: x[index])
    return sorted_array

 # 随机获得一个整数，左闭右闭
-@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，左闭右开
-@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)
@ -39,7 +34,6 @@ def generate_random_int_number_for_a_specific_seed(seed=0, x_min=0, x_max=10):
    return rand_num

 # 以显示编号的样式，打印数组
-@guan.statistics_decorator
 def print_array_with_index(array, show_index=1, index_type=0):
    if show_index==0:
        for i0 in array:
@ -57,21 +51,18 @@ def print_array_with_index(array, show_index=1, index_type=0):
                print(index, i0)

 # 使用jieba软件包进行分词
-@guan.statistics_decorator
 def divide_text_into_words(text):
    import jieba
    words = jieba.lcut(text)
    return words

 # 根据一定的字符长度来分割文本
-@guan.statistics_decorator
 def split_text(text, wrap_width=3000):  
    import textwrap  
    split_text_list = textwrap.wrap(text, wrap_width)
    return split_text_list

 # 判断某个字符是中文还是英文或其他
-@guan.statistics_decorator
 def check_Chinese_or_English(a):  
    if '\u4e00' <= a <= '\u9fff' :  
        word_type = 'Chinese'  
@ -82,7 +73,6 @@ 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.statistics_decorator
 def count_words(text, include_space=0, show_words=0):
    import jieba
    import guan
@ -110,7 +100,6 @@ def count_words(text, include_space=0, show_words=0):
    return num_words

 # 将RGB转成HEX
-@guan.statistics_decorator
 def rgb_to_hex(rgb, pound=1):
    if pound==0:
        return '%02x%02x%02x' % rgb
@ -118,14 +107,12 @@ def rgb_to_hex(rgb, pound=1):
        return '#%02x%02x%02x' % rgb

 # 将HEX转成RGB
-@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.statistics_decorator
 def encryption_MD5(password, salt=''):
    import hashlib
    password = salt+password
@ -133,7 +120,6 @@ def encryption_MD5(password, salt=''):
    return hashed_password

 # 使用SHA-256进行散列加密
-@guan.statistics_decorator
 def encryption_SHA_256(password, salt=''):
    import hashlib
    password = salt+password
--- a/PyPI/src/guan/decorators.py
+++ b/PyPI/src/guan/decorators.py
@ -1,3 +1,5 @@
+# Module: decorators
+
 # 函数的装饰器，用于获取计算时间（秒）
 def timer_decorator(func):
    import time
@ -31,7 +33,7 @@ def timer_decorator_hours(func):
        return result
    return wrapper

-# 函数的装饰器，用于GUAN软件包的统计
+# 函数的装饰器，用于GUAN软件包函数的使用统计
 def statistics_decorator(func):
    def wrapper(*args, **kwargs):
        result = func(*args, **kwargs)
--- a/PyPI/src/guan/density_of_states.py
+++ b/PyPI/src/guan/density_of_states.py
@ -1,8 +1,6 @@
 # Module: density_of_states
-import guan

 # 计算体系的总态密度
-@guan.statistics_decorator
 def total_density_of_states(fermi_energy, hamiltonian, broadening=0.01):
    import numpy as np
    import math
@ -12,7 +10,6 @@ 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.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 +24,6 @@ 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.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 +37,6 @@ 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.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 +51,6 @@ def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2,
    return local_dos

 # 使用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.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 +84,6 @@ def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy
    return local_dos

 # 使用Dyson方程，计算方格子的局域态密度，方法二（其中，h00的维度为：dim_h00 = N2*internal_degree）
-@guan.statistics_decorator
 def local_density_of_states_for_square_lattice_using_dyson_equation_with_second_method(fermi_energy, h00, h01, N2, N1, internal_degree=1, broadening=0.01):
    import numpy as np
    import math
@ -125,7 +118,6 @@ def local_density_of_states_for_square_lattice_using_dyson_equation_with_second_
    return local_dos

 # 使用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.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
@ -160,7 +152,6 @@ def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy,
    return local_dos

 # 使用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.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
--- a/PyPI/src/guan/figure_plotting.py
+++ b/PyPI/src/guan/figure_plotting.py
@ -1,8 +1,6 @@
 # Module: figure_plotting
-import guan

 # 导入plt, fig, ax
-@guan.statistics_decorator
 def import_plt_and_start_fig_ax(adjust_bottom=0.2, adjust_left=0.2, labelsize=20, fontfamily='Times New Roman'):
    import matplotlib.pyplot as plt
    fig, ax = plt.subplots()
@ -15,7 +13,6 @@ 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.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, fontfamily='Times New Roman'): 
    if color==None:
        ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
@ -37,7 +34,6 @@ 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.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, fontfamily='Times New Roman'): 
    import guan
    plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize, fontfamily=fontfamily)
@ -63,7 +59,6 @@ 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.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, fontfamily='Times New Roman'): 
    import guan
    plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize, fontfamily=fontfamily) 
@ -94,7 +89,6 @@ 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.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, fontfamily='Times New Roman'): 
    import guan
    plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize, fontfamily=fontfamily) 
@ -125,7 +119,6 @@ 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.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, fontfamily='Times New Roman'): 
    import guan
    plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize, fontfamily=fontfamily) 
@ -159,7 +152,6 @@ 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.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, fontfamily='Times New Roman'): 
    import guan
    plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize, fontfamily=fontfamily) 
@ -193,7 +185,6 @@ def plot_three_array_with_three_horizontal_array(x1_array, x2_array, x3_array, y
    plt.close('all')

 # 画三维图
-@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, fontfamily='Times New Roman'): 
    import numpy as np
    import matplotlib.pyplot as plt
@ -243,7 +234,6 @@ def plot_3d_surface(x_array, y_array, matrix, xlabel='x', ylabel='y', zlabel='z'
    plt.close('all')

 # 画Contour图
-@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, fontfamily='Times New Roman'):
    import numpy as np
    import matplotlib.pyplot as plt
@ -276,7 +266,6 @@ 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.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, fontfamily='Times New Roman'):  
    import numpy as np
    import matplotlib.pyplot as plt
@ -309,7 +298,6 @@ 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.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
@ -337,7 +325,6 @@ 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.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]
@ -363,7 +350,6 @@ 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.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]
@ -393,7 +379,6 @@ 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.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]
@ -427,7 +412,6 @@ 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.statistics_decorator
 def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
    import re
    import os
@ -440,7 +424,6 @@ 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.statistics_decorator
 def make_gif(image_path_array, filename='a', duration=0.1):
    import imageio
    images = []
@ -450,7 +433,6 @@ 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.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
--- a/PyPI/src/guan/file_reading_and_writing.py
+++ b/PyPI/src/guan/file_reading_and_writing.py
@ -1,15 +1,12 @@
 # Module: file_reading_and_writing
-import guan

 # 使用pickle将变量保存到文件（支持几乎所有对象类型）
-@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)

 # 使用pickle从文件中恢复数据到变量（支持几乎所有对象类型）
-@guan.statistics_decorator
 def load_data(filename, file_format='.txt'):
    import pickle
    with open(filename+file_format, 'rb') as f:
@ -17,40 +14,34 @@ def load_data(filename, file_format='.txt'):
    return data

 # 使用NumPy保存数组变量到npy文件（二进制文件）
-@guan.statistics_decorator
 def save_npy_data(data, filename):
    import numpy as np
    np.save(filename+'.npy', data)

 # 使用NumPy从npy文件恢复数据到数组变量（二进制文件）
-@guan.statistics_decorator
 def load_npy_data(filename):
    import numpy as np
    data = np.load(filename+'.npy')
    return data

 # 使用NumPy保存数组变量到TXT文件（文本文件）
-@guan.statistics_decorator
 def save_txt_data(data, filename):
    import numpy as np
    np.savetxt(filename+'.txt', data)

 # 使用NumPy从TXT文件恢复数据到数组变量（文本文件）
-@guan.statistics_decorator
 def load_txt_data(filename):
    import numpy as np
    data = np.loadtxt(filename+'.txt')
    return data

 # 如果不存在文件夹，则新建文件夹
-@guan.statistics_decorator
 def make_directory(directory='./test'):
    import os
    if not os.path.exists(directory):
        os.makedirs(directory)

 # 如果不存在文件，则新建空文件
-@guan.statistics_decorator
 def make_file(file_path='./a.txt'):
    import os
    if not os.path.exists(file_path):
@ -58,7 +49,6 @@ def make_file(file_path='./a.txt'):
            pass

 # 打开文件用于写入，默认为新增内容
-@guan.statistics_decorator
 def open_file(filename='a', file_format='.txt', mode='add'):
    if mode == 'add':
        f = open(filename+file_format, 'a', encoding='UTF-8')
@ -67,7 +57,6 @@ def open_file(filename='a', file_format='.txt', mode='add'):
    return f

 # 读取文本文件内容，如果不存在，则新建空文件，并返回空字符串
-@guan.statistics_decorator
 def read_text_file(file_path='./a.txt'):
    import os
    if not os.path.exists(file_path):
@ -80,7 +69,6 @@ def read_text_file(file_path='./a.txt'):
        return content

 # 获取目录中的所有文件名
-@guan.statistics_decorator
 def get_all_filenames_in_directory(directory='./', file_format=None):
    import os
    file_list = []
@ -94,7 +82,6 @@ def get_all_filenames_in_directory(directory='./', file_format=None):
    return file_list

 # 获取目录中的所有文件名（不包括子目录）
-@guan.statistics_decorator
 def get_all_filenames_in_directory_without_subdirectory(directory='./', file_format=None):
    import os
    file_list = []
@ -109,7 +96,6 @@ def get_all_filenames_in_directory_without_subdirectory(directory='./', file_for
    return file_list

 # 获取文件夹中某种文本类型的文件以及读取所有内容
-@guan.statistics_decorator
 def read_text_files_in_directory(directory='./', file_format='.md'):
    import os
    file_list = []
@ -124,7 +110,6 @@ 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.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)
@ -136,13 +121,11 @@ def find_words_in_multiple_files(words, directory='./', file_format='.md'):
    return file_list_with_words

 # 复制一份文件
-@guan.statistics_decorator
 def copy_file(file1='./a.txt', file2='./b.txt'):
    import shutil
    shutil.copy(file1, file2)

 # 打开文件，替代某字符串
-@guan.statistics_decorator
 def open_file_and_replace_str(file_path='./a.txt', old_str='', new_str=''):
    import guan
    content = guan.read_text_file(file_path=file_path)
@ -152,7 +135,6 @@ def open_file_and_replace_str(file_path='./a.txt', old_str='', new_str=''):
    f.close()

 # 复制一份文件，然后再替代某字符串
-@guan.statistics_decorator
 def copy_file_and_replace_str(old_file='./a.txt', new_file='./b.txt', old_str='', new_str=''):
    import guan
    guan.copy_file(file1=old_file, file2=new_file)
@ -163,7 +145,6 @@ def copy_file_and_replace_str(old_file='./a.txt', new_file='./b.txt', old_str=''
    f.close()

 # 拼接两个PDF文件
-@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()
@ -179,7 +160,6 @@ def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_fil
        output_pdf.write(combined_file)

 # 读取文件中的一维数据（一行一组x和y）
-@guan.statistics_decorator
 def read_one_dimensional_data(filename='a', file_format='.txt'): 
    import numpy as np
    f = open(filename+file_format, 'r')
@ -203,7 +183,6 @@ 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.statistics_decorator
 def read_one_dimensional_complex_data(filename='a', file_format='.txt'): 
    import numpy as np
    f = open(filename+file_format, 'r')
@ -227,7 +206,6 @@ 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.statistics_decorator
 def read_two_dimensional_data(filename='a', file_format='.txt'): 
    import numpy as np
    f = open(filename+file_format, 'r')
@ -257,7 +235,6 @@ 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.statistics_decorator
 def read_two_dimensional_complex_data(filename='a', file_format='.txt'): 
    import numpy as np
    f = open(filename+file_format, 'r')
@ -287,21 +264,18 @@ 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.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

 # 在文件中写入一维数据（一行一组x和y）
-@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.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)
@ -318,14 +292,12 @@ 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.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.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)
@ -346,14 +318,12 @@ 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.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.statistics_decorator
 def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f):
    for row in matrix:
        for element in row:
--- a/PyPI/src/guan/machine_learning.py
+++ b/PyPI/src/guan/machine_learning.py
@ -1,8 +1,6 @@
 # Module: machine_learning
-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.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 +26,6 @@ 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.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 +64,6 @@ 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.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 +114,6 @@ 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.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':
@ -144,7 +139,6 @@ def train_model(model, x_data, y_data, optimizer='Adam', learning_rate=0.001, cr
    return model, losses

 # 使用优化器批量训练模型
-@guan.statistics_decorator
 def batch_train_model(model, train_loader, optimizer='Adam', learning_rate=0.001, criterion='MSELoss', num_epochs=1000, print_show=1):
    import torch
    if optimizer == 'Adam':
@ -171,33 +165,28 @@ def batch_train_model(model, train_loader, optimizer='Adam', learning_rate=0.001
    return model, losses

 # 保存模型参数到文件
-@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.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.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.statistics_decorator
 def load_model(filename='./model.pth'):
    import torch
    model = torch.load(filename)
    return model

 # 加载训练数据，用于批量加载训练
-@guan.statistics_decorator
 def load_train_data(x_train, y_train, batch_size=32):
    from torch.utils.data import DataLoader, TensorDataset
    train_dataset = TensorDataset(x_train, y_train)
--- a/PyPI/src/guan/others.py
+++ b/PyPI/src/guan/others.py
@ -1,5 +1,4 @@
 # Module: others
-import guan

 # 获取当前日期字符串
 def get_date(bar=True):
@ -18,7 +17,6 @@ def get_time(colon=True):
    return datetime_time

 # 自动先后运行程序
-@guan.statistics_decorator
 def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='python ', show_time=0):
    import os
    import time
@ -38,14 +36,12 @@ def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='pytho
        print('Total running time = '+str((end-start)/60)+' min')

 # 获取CPU使用率
-@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.statistics_decorator
 def get_memory_info():
    import psutil
    memory_info = psutil.virtual_memory()
@ -56,7 +52,6 @@ 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.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
@ -94,7 +89,6 @@ 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.statistics_decorator
 def statistics_with_day_and_time(content='', filename='a', file_format='.txt'):
    import datetime
    datetime_today = str(datetime.date.today())
@ -106,7 +100,6 @@ 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.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()
@ -120,7 +113,6 @@ def count_number_of_import_statements(filename, file_format='.py', num=1000):
    return import_statement_counter

 # 获取本月的所有日期
-@guan.statistics_decorator
 def get_days_of_the_current_month(str_or_datetime='str'):
    import datetime
    today = datetime.date.today()
@ -140,7 +132,6 @@ def get_days_of_the_current_month(str_or_datetime='str'):
    return day_array

 # 获取上个月份
-@guan.statistics_decorator
 def get_last_month():
    import datetime
    today = datetime.date.today()
@ -153,7 +144,6 @@ def get_last_month():
    return year_of_last_month, last_month

 # 获取上上个月份
-@guan.statistics_decorator
 def get_the_month_before_last():
    import datetime
    today = datetime.date.today()
@ -171,7 +161,6 @@ 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.statistics_decorator
 def get_days_of_the_last_month(str_or_datetime='str'):
    import datetime
    import guan
@ -193,7 +182,6 @@ 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.statistics_decorator
 def get_days_of_the_month_before_last(str_or_datetime='str'):
    import datetime
    import guan
@ -215,7 +203,6 @@ def get_days_of_the_month_before_last(str_or_datetime='str'):
    return day_array

 # 获取所有股票
-@guan.statistics_decorator
 def all_stocks():
    import numpy as np
    import akshare as ak
@ -225,7 +212,6 @@ def all_stocks():
    return title, stock_data

 # 获取所有股票的代码
-@guan.statistics_decorator
 def all_stock_symbols():
    import guan
    title, stock_data = guan.all_stocks()
@ -233,7 +219,6 @@ def all_stock_symbols():
    return stock_symbols

 # 股票代码的分类
-@guan.statistics_decorator
 def stock_symbols_classification():
    import guan
    import re
@ -290,7 +275,6 @@ def stock_symbols_classification():
    return stock_symbols_60, stock_symbols_00, stock_symbols_30, stock_symbols_68, stock_symbols_8_4, stock_symbols_others

 # 股票代码各个分类的数量
-@guan.statistics_decorator
 def statistics_of_stock_symbols_classification():
    import guan
    stock_symbols_60, stock_symbols_00, stock_symbols_30, stock_symbols_68, stock_symbols_8_4, stock_symbols_others = guan.stock_symbols_classification()
@ -303,7 +287,6 @@ def statistics_of_stock_symbols_classification():
    return num_stocks_60, num_stocks_00, num_stocks_30, num_stocks_68, num_stocks_8_4, num_stocks_others

 # 从股票代码获取股票名称
-@guan.statistics_decorator
 def find_stock_name_from_symbol(symbol='000002'):
    import guan
    title, stock_data = guan.all_stocks()
@ -313,7 +296,6 @@ def find_stock_name_from_symbol(symbol='000002'):
    return stock_name

 # 市值排序
-@guan.statistics_decorator
 def sorted_market_capitalization(num=10):
    import numpy as np
    import guan
@ -338,7 +320,6 @@ def sorted_market_capitalization(num=10):
    return sorted_array

 # 美股市值排序
-@guan.statistics_decorator
 def sorted_market_capitalization_us(num=10):
    import akshare as ak
    import numpy as np
@ -364,7 +345,6 @@ def sorted_market_capitalization_us(num=10):
    return sorted_array

 # 获取单个股票的历史数据
-@guan.statistics_decorator
 def history_data_of_one_stock(symbol='000002', period='daily', start_date="19000101", end_date='21000101'):
    # period = 'daily'
    # period = 'weekly'
@ -377,7 +357,6 @@ def history_data_of_one_stock(symbol='000002', period='daily', start_date="19000
    return title, stock_data

 # 绘制股票图
-@guan.statistics_decorator
 def plot_stock_line(date_array, opening_array, closing_array, high_array, low_array, lw_open_close=6, lw_high_low=2, xlabel='date', ylabel='price', title='', fontsize=20, labelsize=20, 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)
@ -395,7 +374,6 @@ def plot_stock_line(date_array, opening_array, closing_array, high_array, low_ar
    plt.close('all')

 # 获取软件包中的所有模块名
-@guan.statistics_decorator
 def get_all_modules_in_one_package(package_name='guan'):
    import pkgutil
    package = __import__(package_name)
@ -403,7 +381,6 @@ 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.statistics_decorator
 def get_all_functions_in_one_module(module_name, package_name='guan'):
    import inspect
    function_names = []
@ -414,7 +391,6 @@ 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.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)
@ -432,7 +408,6 @@ 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.statistics_decorator
 def get_PID(name):
    import subprocess
    command = "ps -ef | grep "+name
@ -445,14 +420,12 @@ def get_PID(name):
    return id_running

 # 获取函数的源码
-@guan.statistics_decorator
 def get_function_source(function_name):
    import inspect
    function_source = inspect.getsource(function_name)
    return function_source

 # 查找文件名相同的文件
-@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
@ -477,7 +450,6 @@ 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.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
@ -531,7 +503,6 @@ 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.statistics_decorator
 def change_directory_by_replacement(current_key_word='code', new_key_word='data'):
    import os
    code_path = os.getcwd()
@ -542,7 +513,6 @@ 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.statistics_decorator
 def creat_necessary_file(directory, filename='readme', file_format='.md', content='', overwrite=None, ignored_directory_with_words=[]):
    import os
    directory_with_file = []
@ -571,8 +541,7 @@ def creat_necessary_file(directory, filename='readme', file_format='.md', conten
        f.write(content)
        f.close()

-# 删除特定文件名的文件（慎用）
-@guan.statistics_decorator
+# 删除特定文件名的文件（谨慎使用）
 def delete_file_with_specific_name(directory, filename='readme', file_format='.md'):
    import os
    for root, dirs, files in os.walk(directory):
@ -580,8 +549,7 @@ def delete_file_with_specific_name(directory, filename='readme', file_format='.m
            if files[i0] == filename+file_format:
                os.remove(root+'/'+files[i0])

-# 将所有文件移到根目录（慎用）
-@guan.statistics_decorator
+# 将所有文件移到根目录（谨慎使用）
 def move_all_files_to_root_directory(directory):
    import os
    import shutil
@ -596,7 +564,6 @@ def move_all_files_to_root_directory(directory):
                pass

 # 将文件目录结构写入Markdown文件
-@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")
@ -700,7 +667,6 @@ 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.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
@ -719,14 +685,12 @@ def get_html_from_tags(link, tags=['title', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6',
    return content

 # 生成二维码
-@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.statistics_decorator
 def pdf_to_text(pdf_path):
    from pdfminer.pdfparser import PDFParser, PDFDocument
    from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
@ -758,7 +722,6 @@ def pdf_to_text(pdf_path):
    return content

 # 获取PDF文件页数
-@guan.statistics_decorator
 def get_pdf_page_number(pdf_path):
    import PyPDF2
    pdf_file = open(pdf_path, 'rb')
@ -767,7 +730,6 @@ def get_pdf_page_number(pdf_path):
    return num_pages

 # 获取PDF文件指定页面的内容
-@guan.statistics_decorator
 def pdf_to_txt_for_a_specific_page(pdf_path, page_num=1):
    import PyPDF2
    pdf_file = open(pdf_path, 'rb')
@ -781,7 +743,6 @@ 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.statistics_decorator
 def get_links_from_pdf(pdf_path, link_starting_form=''):
    import PyPDF2
    import re
@ -806,7 +767,6 @@ def get_links_from_pdf(pdf_path, link_starting_form=''):
    return links

 # 通过Sci-Hub网站下载文献
-@guan.statistics_decorator
 def download_with_scihub(address=None, num=1):
    from bs4 import BeautifulSoup
    import re
@ -844,7 +804,6 @@ def download_with_scihub(address=None, num=1):
        print('All completed!\n')

 # 将字符串转成音频
-@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
@ -868,7 +827,6 @@ 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.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
@ -896,7 +854,6 @@ 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.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
@ -924,7 +881,6 @@ 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.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
@ -946,7 +902,6 @@ def get_calling_function_name(layer=1):
    return calling_function_name

 # 获取Python软件包的最新版本
-@guan.statistics_decorator
 def get_latest_version(package_name='guan', timeout=5):
    import requests
    url = f"https://pypi.org/pypi/{package_name}/json"
@ -971,7 +926,6 @@ def get_current_version(package_name='guan'):
        return None

 # Guan软件包升级检查和提示
-@guan.statistics_decorator
 def notification_of_upgrade(timeout=5):
    try:
        import guan
@ -983,17 +937,9 @@ def notification_of_upgrade(timeout=5):
    except:
        pass

-# 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 = []
+# Guan软件包的使用统计
 def statistics_of_guan_package(function_name=None):
    import guan
-    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)
    try:
        import socket
        datetime_date = guan.get_date()
@ -1003,6 +949,15 @@ def statistics_of_guan_package(function_name=None):
        client_socket.settimeout(0.5)
        client_socket.connect(('socket.guanjihuan.com', 12345))
        mac_address = guan.get_mac_address()
+        if function_name == None:
+            message = {
+                'server': 'py.guanjihuan.com',
+                'date': datetime_date,
+                'time': datetime_time,
+                'version': current_version,
+                'MAC_address': mac_address,
+            }
+        else:
            message = {
                'server': 'py.guanjihuan.com',
                'date': datetime_date,
@ -1017,3 +972,5 @@ def statistics_of_guan_package(function_name=None):
        client_socket.close()
    except:
        pass
+
+statistics_of_guan_package(function_name=None)
--- a/PyPI/src/guan/quantum_transport.py
+++ b/PyPI/src/guan/quantum_transport.py
@ -1,8 +1,6 @@
 # Module: quantum_transport
-import guan

 # 计算电导
-@guan.statistics_decorator
 def calculate_conductance(fermi_energy, h00, h01, length=100):
    import numpy as np
    import copy
@ -22,7 +20,6 @@ def calculate_conductance(fermi_energy, h00, h01, length=100):
    return conductance

 # 计算不同费米能下的电导
-@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 +34,6 @@ 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.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 +57,6 @@ 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.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 +85,6 @@ def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensi
    return conductance_averaged

 # 计算在无序散射下的电导（需要输入无序数组）
-@guan.statistics_decorator
 def calculate_conductance_with_disorder_array(fermi_energy, h00, h01, disorder_array, length=100):
    import numpy as np
    import copy
@ -114,7 +108,6 @@ def calculate_conductance_with_disorder_array(fermi_energy, h00, h01, disorder_a
    return conductance

 # 计算在无序垂直切片的散射下的电导
-@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
@ -138,7 +131,6 @@ 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.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
@ -163,7 +155,6 @@ 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.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
@ -188,7 +179,6 @@ 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.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
@ -205,7 +195,6 @@ 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.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
@ -222,7 +211,6 @@ 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.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
@ -239,7 +227,6 @@ 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.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
@ -286,7 +273,6 @@ 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.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
@ -311,7 +297,6 @@ 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.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
@ -324,7 +309,6 @@ 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.statistics_decorator
 def if_active_channel(k_of_channel):
    import numpy as np
    if np.abs(np.imag(k_of_channel))<1e-6:
@ -334,7 +318,6 @@ 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.statistics_decorator
 def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
    import numpy as np
    import math
@ -371,7 +354,6 @@ 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.statistics_decorator
 def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01):
    import numpy as np
    import guan
@ -424,7 +406,6 @@ 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.statistics_decorator
 def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
    import numpy as np
    import math
@ -470,7 +451,6 @@ 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.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==():
@ -492,7 +472,6 @@ 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.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)
@ -502,7 +481,6 @@ 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.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)
@ -547,7 +525,6 @@ 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.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)
@ -557,7 +534,6 @@ 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.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
@ -607,7 +583,6 @@ 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.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
--- a/PyPI/src/guan/topological_invariant.py
+++ b/PyPI/src/guan/topological_invariant.py
@ -1,8 +1,6 @@
 # Module: topological_invariant
-import guan

 # 通过高效法计算方格子的陈数
-@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 +39,6 @@ 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.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 +108,6 @@ 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.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 +152,6 @@ 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.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 +215,6 @@ 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.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 +261,6 @@ 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.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 +339,6 @@ 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.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 +395,6 @@ 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.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 +466,6 @@ 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.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 +509,6 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
    return chern_number

 # 计算Wilson loop
-@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
--- a/README.md
+++ b/README.md
@ -14,7 +14,6 @@ import guan

 ## Summary of API Reference

-+ decorator
 + basic functions
 + Fourier transform
 + Hamiltonian of examples
@ -28,6 +27,7 @@ import guan
 + figure plotting
 + data processing
 + others
+ decorators

 ## About this package