From 7a9b86f79157dea035dea312d60b2b293fa969ee Mon Sep 17 00:00:00 2001
From: guanjihuan <guanjihuan@163.com>
Date: Mon, 27 Nov 2023 16:42:45 +0800
Subject: [PATCH] =?UTF-8?q?0.1.48=20=E5=A2=9E=E5=8A=A0=E8=A3=85=E9=A5=B0?=
 =?UTF-8?q?=E5=99=A8?=
MIME-Version: 1.0
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Content-Transfer-Encoding: 8bit

---
 PyPI/setup.cfg                                |   2 +-
 PyPI/src/guan.egg-info/PKG-INFO               |   2 +-
 PyPI/src/guan/Fourier_transform.py            |  34 ++--
 PyPI/src/guan/Green_functions.py              |  32 ++-
 PyPI/src/guan/Hamiltonian_of_examples.py      |  76 +++-----
 PyPI/src/guan/__init__.py                     |   8 +
 .../band_structures_and_wave_functions.py     |  32 ++-
 PyPI/src/guan/basic_functions.py              |  48 +++--
 PyPI/src/guan/data_processing.py              | 136 +++++--------
 PyPI/src/guan/density_of_states.py            |  17 +-
 PyPI/src/guan/others.py                       | 184 ++++++++----------
 PyPI/src/guan/quantum_transport.py            |  52 +++--
 PyPI/src/guan/topological_invariant.py        |  27 ++-
 13 files changed, 271 insertions(+), 379 deletions(-)

diff --git a/PyPI/setup.cfg b/PyPI/setup.cfg
index 138d616..5cb64ae 100644
--- a/PyPI/setup.cfg
+++ b/PyPI/setup.cfg
@@ -1,7 +1,7 @@
 [metadata]
 # replace with your username:
 name = guan
-version = 0.1.47
+version = 0.1.48
 author = guanjihuan
 author_email = guanjihuan@163.com
 description = An open source python package
diff --git a/PyPI/src/guan.egg-info/PKG-INFO b/PyPI/src/guan.egg-info/PKG-INFO
index 4ff679e..1e04bb8 100644
--- 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: 0.1.47
+Version: 0.1.48
 Summary: An open source python package
 Home-page: https://py.guanjihuan.com
 Author: guanjihuan
diff --git a/PyPI/src/guan/Fourier_transform.py b/PyPI/src/guan/Fourier_transform.py
index 50912f4..5622d80 100644
--- a/PyPI/src/guan/Fourier_transform.py
+++ b/PyPI/src/guan/Fourier_transform.py
@@ -1,17 +1,18 @@
 # Module: Fourier_transform
+import guan
 
 # 通过元胞和跃迁项得到一维的哈密顿量（需要输入k值）
+@guan.function_decorator
 def one_dimensional_fourier_transform(k, unit_cell, hopping):
     import numpy as np
     import cmath
     unit_cell = np.array(unit_cell)
     hopping = np.array(hopping)
     hamiltonian = unit_cell+hopping*cmath.exp(1j*k)+hopping.transpose().conj()*cmath.exp(-1j*k)
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 通过元胞和跃迁项得到二维方格子的哈密顿量（需要输入k值）
+@guan.function_decorator
 def two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell, hopping_1, hopping_2):
     import numpy as np
     import cmath
@@ -19,11 +20,10 @@ def two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell, hopp
     hopping_1 = np.array(hopping_1)
     hopping_2 = np.array(hopping_2)
     hamiltonian = unit_cell+hopping_1*cmath.exp(1j*k1)+hopping_1.transpose().conj()*cmath.exp(-1j*k1)+hopping_2*cmath.exp(1j*k2)+hopping_2.transpose().conj()*cmath.exp(-1j*k2)
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 通过元胞和跃迁项得到三维立方格子的哈密顿量（需要输入k值）
+@guan.function_decorator
 def three_dimensional_fourier_transform_for_cubic_lattice(k1, k2, k3, unit_cell, hopping_1, hopping_2, hopping_3):
     import numpy as np
     import cmath
@@ -32,43 +32,41 @@ def three_dimensional_fourier_transform_for_cubic_lattice(k1, k2, k3, unit_cell,
     hopping_2 = np.array(hopping_2)
     hopping_3 = np.array(hopping_3)
     hamiltonian = unit_cell+hopping_1*cmath.exp(1j*k1)+hopping_1.transpose().conj()*cmath.exp(-1j*k1)+hopping_2*cmath.exp(1j*k2)+hopping_2.transpose().conj()*cmath.exp(-1j*k2)+hopping_3*cmath.exp(1j*k3)+hopping_3.transpose().conj()*cmath.exp(-1j*k3)
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 通过元胞和跃迁项得到一维的哈密顿量（返回的哈密顿量为携带k的函数）
+@guan.function_decorator
 def one_dimensional_fourier_transform_with_k(unit_cell, hopping):
     import functools
     import guan
     hamiltonian_function = functools.partial(guan.one_dimensional_fourier_transform, unit_cell=unit_cell, hopping=hopping)
-    guan.statistics_of_guan_package()
     return hamiltonian_function
 
 # 通过元胞和跃迁项得到二维方格子的哈密顿量（返回的哈密顿量为携带k的函数）
+@guan.function_decorator
 def two_dimensional_fourier_transform_for_square_lattice_with_k1_k2(unit_cell, hopping_1, hopping_2):
     import functools
     import guan
     hamiltonian_function = functools.partial(guan.two_dimensional_fourier_transform_for_square_lattice, unit_cell=unit_cell, hopping_1=hopping_1, hopping_2=hopping_2)
-    guan.statistics_of_guan_package()
     return hamiltonian_function
 
 # 通过元胞和跃迁项得到三维立方格子的哈密顿量（返回的哈密顿量为携带k的函数）
+@guan.function_decorator
 def three_dimensional_fourier_transform_for_cubic_lattice_with_k1_k2_k3(unit_cell, hopping_1, hopping_2, hopping_3):
     import functools
     import guan
     hamiltonian_function = functools.partial(guan.three_dimensional_fourier_transform_for_cubic_lattice, unit_cell=unit_cell, hopping_1=hopping_1, hopping_2=hopping_2, hopping_3=hopping_3)
-    guan.statistics_of_guan_package()
     return hamiltonian_function
 
 # 由实空间格矢得到倒空间格矢（一维）
+@guan.function_decorator
 def calculate_one_dimensional_reciprocal_lattice_vector(a1):
     import numpy as np
     b1 = 2*np.pi/a1
-    import guan
-    guan.statistics_of_guan_package()
     return b1
 
 # 由实空间格矢得到倒空间格矢（二维）
+@guan.function_decorator
 def calculate_two_dimensional_reciprocal_lattice_vectors(a1, a2):
     import numpy as np
     a1 = np.array(a1)
@@ -80,11 +78,10 @@ def calculate_two_dimensional_reciprocal_lattice_vectors(a1, a2):
     b2 = 2*np.pi*np.cross(a3, a1)/np.dot(a1, np.cross(a2, a3))
     b1 = np.delete(b1, 2)
     b2 = np.delete(b2, 2)
-    import guan
-    guan.statistics_of_guan_package()
     return b1, b2
 
 # 由实空间格矢得到倒空间格矢（三维）
+@guan.function_decorator
 def calculate_three_dimensional_reciprocal_lattice_vectors(a1, a2, a3):
     import numpy as np
     a1 = np.array(a1)
@@ -93,19 +90,17 @@ def calculate_three_dimensional_reciprocal_lattice_vectors(a1, a2, a3):
     b1 = 2*np.pi*np.cross(a2, a3)/np.dot(a1, np.cross(a2, a3))
     b2 = 2*np.pi*np.cross(a3, a1)/np.dot(a1, np.cross(a2, a3))
     b3 = 2*np.pi*np.cross(a1, a2)/np.dot(a1, np.cross(a2, a3))
-    import guan
-    guan.statistics_of_guan_package()
     return b1, b2, b3
 
 # 由实空间格矢得到倒空间格矢（一维），这里为符号运算
+@guan.function_decorator
 def calculate_one_dimensional_reciprocal_lattice_vector_with_sympy(a1):
     import sympy
     b1 = 2*sympy.pi/a1
-    import guan
-    guan.statistics_of_guan_package()
     return b1
 
 # 由实空间格矢得到倒空间格矢（二维），这里为符号运算
+@guan.function_decorator
 def calculate_two_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2):
     import sympy
     a1 = sympy.Matrix(1, 3, [a1[0], a1[1], 0])
@@ -117,11 +112,10 @@ def calculate_two_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2):
     b2 = 2*sympy.pi*cross_a3_a1/a1.dot(cross_a2_a3)
     b1 = sympy.Matrix(1, 2, [b1[0], b1[1]])
     b2 = sympy.Matrix(1, 2, [b2[0], b2[1]])
-    import guan
-    guan.statistics_of_guan_package()
     return b1, b2
 
 # 由实空间格矢得到倒空间格矢（三维），这里为符号运算
+@guan.function_decorator
 def calculate_three_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2, a3):
     import sympy
     cross_a2_a3 = a2.cross(a3)
@@ -130,6 +124,4 @@ def calculate_three_dimensional_reciprocal_lattice_vectors_with_sympy(a1, a2, a3
     b1 = 2*sympy.pi*cross_a2_a3/a1.dot(cross_a2_a3)
     b2 = 2*sympy.pi*cross_a3_a1/a1.dot(cross_a2_a3)
     b3 = 2*sympy.pi*cross_a1_a2/a1.dot(cross_a2_a3)
-    import guan
-    guan.statistics_of_guan_package()
     return b1, b2, b3
diff --git a/PyPI/src/guan/Green_functions.py b/PyPI/src/guan/Green_functions.py
index 7f4713f..6d11942 100644
--- a/PyPI/src/guan/Green_functions.py
+++ b/PyPI/src/guan/Green_functions.py
@@ -1,6 +1,8 @@
 # Module: Green_functions
+import guan
 
 # 输入哈密顿量，得到格林函数
+@guan.function_decorator
 def green_function(fermi_energy, hamiltonian, broadening, self_energy=0):
     import numpy as np
     if np.array(hamiltonian).shape==():
@@ -8,11 +10,10 @@ def green_function(fermi_energy, hamiltonian, broadening, self_energy=0):
     else:
         dim = np.array(hamiltonian).shape[0]
     green = np.linalg.inv((fermi_energy+broadening*1j)*np.eye(dim)-hamiltonian-self_energy)
-    import guan
-    guan.statistics_of_guan_package()
     return green
 
 # 在Dyson方程中的一个中间格林函数G_{nn}^{n}
+@guan.function_decorator
 def green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening, self_energy=0):
     import numpy as np
     h01 = np.array(h01)
@@ -21,36 +22,32 @@ def green_function_nn_n(fermi_energy, h00, h01, green_nn_n_minus, broadening, se
     else:
         dim = np.array(h00).shape[0]   
     green_nn_n = np.linalg.inv((fermi_energy+broadening*1j)*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n_minus), h01)-self_energy)
-    import guan
-    guan.statistics_of_guan_package()
     return green_nn_n
 
 # 在Dyson方程中的一个中间格林函数G_{in}^{n}
+@guan.function_decorator
 def green_function_in_n(green_in_n_minus, h01, green_nn_n):
     import numpy as np
     green_in_n = np.dot(np.dot(green_in_n_minus, h01), green_nn_n)
-    import guan
-    guan.statistics_of_guan_package()
     return green_in_n
 
 # 在Dyson方程中的一个中间格林函数G_{ni}^{n}
+@guan.function_decorator
 def green_function_ni_n(green_nn_n, h01, green_ni_n_minus):
     import numpy as np
     h01 = np.array(h01)
     green_ni_n = np.dot(np.dot(green_nn_n, h01.transpose().conj()), green_ni_n_minus)
-    import guan
-    guan.statistics_of_guan_package()
     return green_ni_n
 
 # 在Dyson方程中的一个中间格林函数G_{ii}^{n}
+@guan.function_decorator
 def green_function_ii_n(green_ii_n_minus, green_in_n_minus, h01, green_nn_n, green_ni_n_minus):
     import numpy as np
     green_ii_n = green_ii_n_minus+np.dot(np.dot(np.dot(np.dot(green_in_n_minus, h01), green_nn_n), h01.transpose().conj()),green_ni_n_minus)
-    import guan
-    guan.statistics_of_guan_package()
     return green_ii_n
 
 # 计算转移矩阵（该矩阵可以用来计算表面格林函数）
+@guan.function_decorator
 def transfer_matrix(fermi_energy, h00, h01):
     import numpy as np
     h01 = np.array(h01)
@@ -63,11 +60,10 @@ def transfer_matrix(fermi_energy, h00, h01):
     transfer[0:dim, dim:2*dim] = np.dot(-1*np.linalg.inv(h01), h01.transpose().conj())
     transfer[dim:2*dim, 0:dim] = np.identity(dim)
     transfer[dim:2*dim, dim:2*dim] = 0
-    import guan
-    guan.statistics_of_guan_package()
     return transfer
 
 # 计算电极的表面格林函数
+@guan.function_decorator
 def surface_green_function_of_lead(fermi_energy, h00, h01):
     import numpy as np
     h01 = np.array(h01)
@@ -90,11 +86,10 @@ def surface_green_function_of_lead(fermi_energy, h00, h01):
     s4 = temp[0:dim, dim:2*dim]
     right_lead_surface = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01, s2), np.linalg.inv(s1)))
     left_lead_surface = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), s3), np.linalg.inv(s4)))
-    import guan
-    guan.statistics_of_guan_package()
     return right_lead_surface, left_lead_surface
 
 # 计算电极的自能（基于Dyson方程的小矩阵形式）
+@guan.function_decorator
 def self_energy_of_lead(fermi_energy, h00, h01):
     import numpy as np
     import guan
@@ -104,10 +99,10 @@ def self_energy_of_lead(fermi_energy, h00, h01):
     left_self_energy = np.dot(np.dot(h01.transpose().conj(), left_lead_surface), h01)
     gamma_right = (right_self_energy - right_self_energy.transpose().conj())*1j
     gamma_left = (left_self_energy - left_self_energy.transpose().conj())*1j
-    guan.statistics_of_guan_package()
     return right_self_energy, left_self_energy, gamma_right, gamma_left
 
 # 计算电极的自能（基于中心区整体的大矩阵形式）
+@guan.function_decorator
 def self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR):
     import numpy as np
     import guan
@@ -118,10 +113,10 @@ def self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR):
     left_self_energy = np.dot(np.dot(h_LC.transpose().conj(), left_lead_surface), h_LC)
     gamma_right = (right_self_energy - right_self_energy.transpose().conj())*1j
     gamma_left = (left_self_energy - left_self_energy.transpose().conj())*1j
-    guan.statistics_of_guan_package()
     return right_self_energy, left_self_energy, gamma_right, gamma_left
 
 # 计算电极的自能（基于中心区整体的大矩阵形式，可适用于多端电导的计算）
+@guan.function_decorator
 def self_energy_of_lead_with_h_lead_to_center(fermi_energy, h00, h01, h_lead_to_center):
     import numpy as np
     import guan
@@ -129,25 +124,24 @@ def self_energy_of_lead_with_h_lead_to_center(fermi_energy, h00, h01, h_lead_to_
     right_lead_surface, left_lead_surface = guan.surface_green_function_of_lead(fermi_energy, h00, h01)
     self_energy = np.dot(np.dot(h_lead_to_center.transpose().conj(), right_lead_surface), h_lead_to_center)
     gamma = (self_energy - self_energy.transpose().conj())*1j
-    guan.statistics_of_guan_package()
     return self_energy, gamma
 
 # 计算考虑电极自能后的中心区的格林函数
+@guan.function_decorator
 def green_function_with_leads(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian):
     import numpy as np
     import guan
     dim = np.array(center_hamiltonian).shape[0]
     right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
     green = np.linalg.inv(fermi_energy*np.identity(dim)-center_hamiltonian-left_self_energy-right_self_energy)
-    guan.statistics_of_guan_package()
     return green, gamma_right, gamma_left
 
 # 计算用于计算局域电流的格林函数G_n
+@guan.function_decorator
 def electron_correlation_function_green_n_for_local_current(fermi_energy, h00, h01, h_LC, h_CR, center_hamiltonian):
     import numpy as np
     import guan
     right_self_energy, left_self_energy, gamma_right, gamma_left = guan.self_energy_of_lead_with_h_LC_and_h_CR(fermi_energy, h00, h01, h_LC, h_CR)
     green = guan.green_function(fermi_energy, center_hamiltonian, broadening=0, self_energy=left_self_energy+right_self_energy)
     G_n = np.imag(np.dot(np.dot(green, gamma_left), green.transpose().conj()))
-    guan.statistics_of_guan_package()
     return G_n
\ No newline at end of file
diff --git a/PyPI/src/guan/Hamiltonian_of_examples.py b/PyPI/src/guan/Hamiltonian_of_examples.py
index e8056b6..cf6e20e 100644
--- a/PyPI/src/guan/Hamiltonian_of_examples.py
+++ b/PyPI/src/guan/Hamiltonian_of_examples.py
@@ -1,6 +1,8 @@
 # Module: Hamiltonian_of_examples
+import guan
 
 # 构建一维的有限尺寸体系哈密顿量（可设置是否为周期边界条件）
+@guan.function_decorator
 def hamiltonian_of_finite_size_system_along_one_direction(N, on_site=0, hopping=1, period=0):
     import numpy as np
     on_site = np.array(on_site)
@@ -18,11 +20,10 @@ def hamiltonian_of_finite_size_system_along_one_direction(N, on_site=0, hopping=
     if period == 1:
         hamiltonian[(N-1)*dim+0:(N-1)*dim+dim, 0:dim] = hopping
         hamiltonian[0:dim, (N-1)*dim+0:(N-1)*dim+dim] = hopping.transpose().conj()
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 构建二维的方格子有限尺寸体系哈密顿量（可设置是否为周期边界条件）
+@guan.function_decorator
 def hamiltonian_of_finite_size_system_along_two_directions_for_square_lattice(N1, N2, on_site=0, hopping_1=1, hopping_2=1, period_1=0, period_2=0):
     import numpy as np
     on_site = np.array(on_site)
@@ -52,11 +53,10 @@ def hamiltonian_of_finite_size_system_along_two_directions_for_square_lattice(N1
         for i1 in range(N1):
             hamiltonian[i1*N2*dim+(N2-1)*dim+0:i1*N2*dim+(N2-1)*dim+dim, i1*N2*dim+0:i1*N2*dim+dim] = hopping_2
             hamiltonian[i1*N2*dim+0:i1*N2*dim+dim, i1*N2*dim+(N2-1)*dim+0:i1*N2*dim+(N2-1)*dim+dim] = hopping_2.transpose().conj()
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 构建三维的立方格子有限尺寸体系哈密顿量（可设置是否为周期边界条件）
+@guan.function_decorator
 def hamiltonian_of_finite_size_system_along_three_directions_for_cubic_lattice(N1, N2, N3, on_site=0, hopping_1=1, hopping_2=1, hopping_3=1, period_1=0, period_2=0, period_3=0):
     import numpy as np
     on_site = np.array(on_site)
@@ -102,11 +102,10 @@ def hamiltonian_of_finite_size_system_along_three_directions_for_cubic_lattice(N
             for i2 in range(N2):
                 hamiltonian[i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+0:i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+dim, i1*N2*N3*dim+i2*N3*dim+0:i1*N2*N3*dim+i2*N3*dim+dim] = hopping_3
                 hamiltonian[i1*N2*N3*dim+i2*N3*dim+0:i1*N2*N3*dim+i2*N3*dim+dim, i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+0:i1*N2*N3*dim+i2*N3*dim+(N3-1)*dim+dim] = hopping_3.transpose().conj()
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 构建有限尺寸的SSH模型哈密顿量
+@guan.function_decorator
 def hamiltonian_of_finite_size_ssh_model(N, v=0.6, w=1, onsite_1=0, onsite_2=0, period=1):
     import numpy as np
     hamiltonian = np.zeros((2*N, 2*N))
@@ -121,11 +120,10 @@ def hamiltonian_of_finite_size_ssh_model(N, v=0.6, w=1, onsite_1=0, onsite_2=0,
     if period==1:
         hamiltonian[0, 2*N-1] = w
         hamiltonian[2*N-1, 0] = w
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 获取Zigzag边的石墨烯条带的元胞间跃迁
+@guan.function_decorator
 def get_hopping_term_of_graphene_ribbon_along_zigzag_direction(N, eta=0):
     import numpy as np
     hopping = np.zeros((4*N, 4*N), dtype=complex)
@@ -136,11 +134,10 @@ def get_hopping_term_of_graphene_ribbon_along_zigzag_direction(N, eta=0):
         hopping[4*i0+3, 4*i0+3] = eta
         hopping[4*i0+1, 4*i0+0] = 1
         hopping[4*i0+2, 4*i0+3] = 1
-    import guan
-    guan.statistics_of_guan_package()
     return hopping
 
 # 构建有限尺寸的石墨烯哈密顿量（可设置是否为周期边界条件）
+@guan.function_decorator
 def hamiltonian_of_finite_size_system_along_two_directions_for_graphene(N1, N2, period_1=0, period_2=0):
     import numpy as np
     import guan
@@ -149,10 +146,10 @@ def hamiltonian_of_finite_size_system_along_two_directions_for_graphene(N1, N2,
     hopping_2 = np.zeros((4, 4), dtype=complex)
     hopping_2[3, 0] = 1
     hamiltonian = guan.hamiltonian_of_finite_size_system_along_two_directions_for_square_lattice(N1, N2, on_site, hopping_1, hopping_2, period_1, period_2)
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 获取石墨烯有效模型沿着x方向的在位能和跃迁项（其中，动量qy为参数）
+@guan.function_decorator
 def get_onsite_and_hopping_terms_of_2d_effective_graphene_along_one_direction(qy, t=1, staggered_potential=0, eta=0, valley_index=0):
     import numpy as np
     constant = -np.sqrt(3)/2
@@ -170,11 +167,10 @@ def get_onsite_and_hopping_terms_of_2d_effective_graphene_along_one_direction(qy
     else:
         h01[0, 1] = constant*t*(1j/2)
         h01[1, 0] = constant*t*(1j/2)
-    import guan
-    guan.statistics_of_guan_package()
     return h00, h01
 
 # 获取BHZ模型的在位能和跃迁项
+@guan.function_decorator
 def get_onsite_and_hopping_terms_of_bhz_model(A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01, a=1):
     import numpy as np
     E_s = C+M-4*(D+B)/(a**2)
@@ -205,11 +201,10 @@ def get_onsite_and_hopping_terms_of_bhz_model(A=0.3645/5, B=-0.686/25, C=0, D=-0
     H2[1, 0] = 1j*np.conj(V_sp)
     H2[2, 3] = -1j*np.conj(V_sp)
     H2[3, 2] = -1j*V_sp
-    import guan
-    guan.statistics_of_guan_package()
     return H0, H1, H2
 
 # 获取半个BHZ模型的在位能和跃迁项（自旋向上）
+@guan.function_decorator
 def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_up(A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01, a=1):
     import numpy as np
     E_s = C+M-4*(D+B)/(a**2)
@@ -230,11 +225,10 @@ def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_up(A=0.3645/5, B=-0.
     H2[1, 1] = V_pp
     H2[0, 1] = 1j*V_sp
     H2[1, 0] = 1j*np.conj(V_sp)
-    import guan
-    guan.statistics_of_guan_package()
     return H0, H1, H2
 
 # 获取半个BHZ模型的在位能和跃迁项（自旋向下）
+@guan.function_decorator
 def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_down(A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01, a=1):
     import numpy as np
     E_s = C+M-4*(D+B)/(a**2)
@@ -255,25 +249,24 @@ def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_down(A=0.3645/5, B=-
     H2[1, 1] = V_pp
     H2[0, 1] = -1j*np.conj(V_sp)
     H2[1, 0] = -1j*V_sp
-    import guan
-    guan.statistics_of_guan_package()
     return H0, H1, H2
 
 # 一维链的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_simple_chain(k):
     import guan
     hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=0, hopping=1)
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 二维方格子的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_square_lattice(k1, k2):
     import guan
     hamiltonian = guan.two_dimensional_fourier_transform_for_square_lattice(k1, k2, unit_cell=0, hopping_1=1, hopping_2=1)
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 准一维方格子条带的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_square_lattice_in_quasi_one_dimension(k, N=10, period=0):
     import numpy as np
     import guan
@@ -288,28 +281,27 @@ def hamiltonian_of_square_lattice_in_quasi_one_dimension(k, N=10, period=0):
     for i in range(N):   
         h01[i, i] = 1
     hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=h00, hopping=h01)
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 三维立方格子的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_cubic_lattice(k1, k2, k3):
     import guan
     hamiltonian = guan.three_dimensional_fourier_transform_for_cubic_lattice(k1, k2, k3, unit_cell=0, hopping_1=1, hopping_2=1, hopping_3=1)
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # SSH模型的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_ssh_model(k, v=0.6, w=1):
     import numpy as np
     import cmath
     hamiltonian = np.zeros((2, 2), dtype=complex)
     hamiltonian[0,1] = v+w*cmath.exp(-1j*k)
     hamiltonian[1,0] = v+w*cmath.exp(1j*k)
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 石墨烯的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
     import numpy as np
     import cmath
@@ -323,11 +315,10 @@ def hamiltonian_of_graphene(k1, k2, staggered_potential=0, t=1, a='default'):
     h1[1, 0] = t*(cmath.exp(1j*k2*a)+cmath.exp(1j*math.sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j/2*k2*a))   
     h1[0, 1] = h1[1, 0].conj()
     hamiltonian = h0 + h1
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 石墨烯有效模型的哈密顿量（倒空间）
+@guan.function_decorator
 def effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley_index=0):
     import numpy as np
     hamiltonian = np.zeros((2, 2), dtype=complex)
@@ -340,11 +331,10 @@ def effective_hamiltonian_of_graphene(qx, qy, t=1, staggered_potential=0, valley
     else:
         hamiltonian[0, 1] = constant*t*(-qx-1j*qy)
         hamiltonian[1, 0] = constant*t*(-qx+1j*qy)
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 石墨烯有效模型离散化后的哈密顿量（倒空间）
+@guan.function_decorator
 def effective_hamiltonian_of_graphene_after_discretization(qx, qy, t=1, staggered_potential=0, valley_index=0):
     import numpy as np
     hamiltonian = np.zeros((2, 2), dtype=complex)
@@ -357,11 +347,10 @@ def effective_hamiltonian_of_graphene_after_discretization(qx, qy, t=1, staggere
     else:
         hamiltonian[0, 1] = constant*t*(-np.sin(qx)-1j*np.sin(qy))
         hamiltonian[1, 0] = constant*t*(-np.sin(qx)+1j*np.sin(qy))
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 准一维Zigzag边石墨烯条带的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1, period=0):
     import numpy as np
     import guan
@@ -388,10 +377,10 @@ def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1
         h01[i*4+1, i*4+0] = t
         h01[i*4+2, i*4+3] = t
     hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=h00, hopping=h01)
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # Haldane模型的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi='default', a='default'):
     import numpy as np
     import cmath
@@ -410,11 +399,10 @@ def hamiltonian_of_haldane_model(k1, k2, M=2/3, t1=1, t2=1/3, phi='default', a='
     h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*math.sqrt(3)*k1*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j*3/2*k2*a))
     h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*math.sqrt(3)*k1*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*math.sqrt(3)/2*k1*a-1j*3/2*k2*a))
     hamiltonian = h0 + h1 + h2 + h2.transpose().conj()
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 准一维Haldane模型条带的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2=1/3, phi='default', period=0):
     import numpy as np
     import cmath
@@ -466,11 +454,10 @@ def hamiltonian_of_haldane_model_in_quasi_one_dimension(k, N=10, M=2/3, t1=1, t2
     for i in range(N-1):
         h01[i*4+2, (i+1)*4+0] = t2*cmath.exp(-1j*phi)
     hamiltonian = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 一个量子反常霍尔效应的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1):
     import numpy as np
     import math
@@ -479,11 +466,10 @@ def hamiltonian_of_one_QAH_model(k1, k2, t1=1, t2=1, t3=0.5, m=-1):
     hamiltonian[1, 0] = 2*t1*math.cos(k1)+1j*2*t1*math.cos(k2)
     hamiltonian[0, 0] = m+2*t3*math.sin(k1)+2*t3*math.sin(k2)+2*t2*math.cos(k1+k2)
     hamiltonian[1, 1] = -(m+2*t3*math.sin(k1)+2*t3*math.sin(k2)+2*t2*math.cos(k1+k2))
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # BHZ模型的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_bhz_model(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01):
     import numpy as np
     import math
@@ -499,11 +485,10 @@ def hamiltonian_of_bhz_model(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25,
     hamiltonian[3, 3] = varepsilon-d3
     hamiltonian[2, 3] = -d1_d2 
     hamiltonian[3, 2] = -np.conj(d1_d2)
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 半BHZ模型的哈密顿量（自旋向上）（倒空间）
+@guan.function_decorator
 def hamiltonian_of_half_bhz_model_for_spin_up(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01):
     import numpy as np
     import math
@@ -515,11 +500,10 @@ def hamiltonian_of_half_bhz_model_for_spin_up(kx, ky, A=0.3645/5, B=-0.686/25, C
     hamiltonian[1, 1] = varepsilon-d3
     hamiltonian[0, 1] = np.conj(d1_d2)
     hamiltonian[1, 0] = d1_d2
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # 半BHZ模型的哈密顿量（自旋向下）（倒空间）
+@guan.function_decorator
 def hamiltonian_of_half_bhz_model_for_spin_down(kx, ky, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01):
     import numpy as np
     import math
@@ -531,11 +515,10 @@ def hamiltonian_of_half_bhz_model_for_spin_down(kx, ky, A=0.3645/5, B=-0.686/25,
     hamiltonian[1, 1] = varepsilon-d3
     hamiltonian[0, 1] = -d1_d2 
     hamiltonian[1, 0] = -np.conj(d1_d2)
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # BBH模型的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_bbh_model(kx, ky, gamma_x=0.5, gamma_y=0.5, lambda_x=1, lambda_y=1):
     import numpy as np
     import cmath
@@ -552,11 +535,10 @@ def hamiltonian_of_bbh_model(kx, ky, gamma_x=0.5, gamma_y=0.5, lambda_x=1, lambd
     hamiltonian[3, 1] = np.conj(hamiltonian[1, 3])
     hamiltonian[3, 0] = np.conj(hamiltonian[0, 3])
     hamiltonian[2, 1] = np.conj(hamiltonian[1, 2])
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
 
 # Kagome模型的哈密顿量（倒空间）
+@guan.function_decorator
 def hamiltonian_of_kagome_lattice(kx, ky, t=1):
     import numpy as np
     import math
@@ -569,6 +551,4 @@ def hamiltonian_of_kagome_lattice(kx, ky, t=1):
     hamiltonian[1, 2] = 2*math.cos(k3_dot_a3)
     hamiltonian = hamiltonian + hamiltonian.transpose().conj()
     hamiltonian = -t*hamiltonian
-    import guan
-    guan.statistics_of_guan_package()
     return hamiltonian
\ No newline at end of file
diff --git a/PyPI/src/guan/__init__.py b/PyPI/src/guan/__init__.py
index 01646fb..33759d0 100644
--- a/PyPI/src/guan/__init__.py
+++ b/PyPI/src/guan/__init__.py
@@ -1,5 +1,13 @@
 # Guan is an open-source python package developed and maintained by https://www.guanjihuan.com/about (Ji-Huan Guan, 关济寰). The primary location of this package is on website https://py.guanjihuan.com. The GitHub location of this package is on https://github.com/guanjihuan/py.guanjihuan.com.
 
+# 函数的装饰器，用于软件包的统计
+def function_decorator(func):
+    def wrapper():
+        func()
+        import guan
+        guan.statistics_of_guan_package(func.__name__)
+    return wrapper
+
 from .basic_functions import *
 from .Fourier_transform import *
 from .Hamiltonian_of_examples import *
diff --git a/PyPI/src/guan/band_structures_and_wave_functions.py b/PyPI/src/guan/band_structures_and_wave_functions.py
index 06b3f77..836cca8 100644
--- a/PyPI/src/guan/band_structures_and_wave_functions.py
+++ b/PyPI/src/guan/band_structures_and_wave_functions.py
@@ -1,17 +1,18 @@
 # Module: band_structures_and_wave_functions
+import guan
 
 # 计算哈密顿量的本征值
+@guan.function_decorator
 def calculate_eigenvalue(hamiltonian):
     import numpy as np
     if np.array(hamiltonian).shape==():
         eigenvalue = np.real(hamiltonian)
     else:
         eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
-    import guan
-    guan.statistics_of_guan_package()
     return eigenvalue
 
 # 输入哈密顿量函数（带一组参数），计算一组参数下的本征值，返回本征值向量组
+@guan.function_decorator
 def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print_show=0):
     import numpy as np
     dim_x = np.array(x_array).shape[0]
@@ -32,11 +33,10 @@ def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print
             eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
             eigenvalue_array[i0, :] = eigenvalue
             i0 += 1
-    import guan
-    guan.statistics_of_guan_package()
     return eigenvalue_array
 
 # 输入哈密顿量函数（带两组参数），计算两组参数下的本征值，返回本征值向量组
+@guan.function_decorator
 def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_function, print_show=0, print_show_more=0):  
     import numpy as np
     dim_x = np.array(x_array).shape[0]
@@ -67,19 +67,17 @@ def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_funct
                 eigenvalue_array[i0, j0, :] = eigenvalue
                 j0 += 1
             i0 += 1
-    import guan
-    guan.statistics_of_guan_package()
     return eigenvalue_array
 
 # 计算哈密顿量的本征矢
+@guan.function_decorator
 def calculate_eigenvector(hamiltonian):
     import numpy as np
     eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
-    import guan
-    guan.statistics_of_guan_package()
     return eigenvector
 
 # 通过二分查找的方法获取和相邻波函数一样规范的波函数
+@guan.function_decorator
 def find_vector_with_the_same_gauge_with_binary_search(vector_target, vector_ref, show_error=1, show_times=0, show_phase=0, n_test=1000, precision=1e-6):
     import numpy as np
     import cmath
@@ -116,11 +114,10 @@ def find_vector_with_the_same_gauge_with_binary_search(vector_target, vector_ref
     vector_target = vector_target*cmath.exp(1j*phase)
     if show_phase==1:
         print('Phase=', phase)
-    import guan
-    guan.statistics_of_guan_package()  
     return vector_target
 
 # 通过使得波函数的一个非零分量为实数，得到固定规范的波函数
+@guan.function_decorator
 def find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=0.005, index=None):
     import numpy as np
     import cmath
@@ -136,11 +133,10 @@ def find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=
     vector = vector*cmath.exp(1j*phase)
     if np.real(vector[index]) < 0:
         vector = -vector
-    import guan
-    guan.statistics_of_guan_package()
     return vector
 
 # 通过使得波函数的一个非零分量为实数，得到固定规范的波函数（在一组波函数中选取最大的那个分量）
+@guan.function_decorator
 def find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array, precision=0.005):
     import numpy as np
     import guan
@@ -151,10 +147,10 @@ def find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array
     index = np.argmax(np.abs(vector_sum))
     for i0 in range(Num_k):
         vector_array[i0] = guan.find_vector_with_fixed_gauge_by_making_one_component_real(vector_array[i0], precision=precision, index=index)
-    guan.statistics_of_guan_package()
     return vector_array
 
 # 旋转两个简并的波函数（说明：参数比较多，算法效率不高）
+@guan.function_decorator
 def rotation_of_degenerate_vectors(vector1, vector2, index1=None, index2=None, precision=0.01, criterion=0.01, show_theta=0):
     import numpy as np
     import math
@@ -181,11 +177,10 @@ def rotation_of_degenerate_vectors(vector1, vector2, index1=None, index2=None, p
                     break
             if np.abs(vector1_test[index2])<criterion and np.abs(vector2_test[index1])<criterion:
                 break
-    import guan
-    guan.statistics_of_guan_package()
     return vector1, vector2
 
 # 旋转两个简并的波函数向量组（说明：参数比较多，算法效率不高）
+@guan.function_decorator
 def rotation_of_degenerate_vectors_array(vector1_array, vector2_array, precision=0.01, criterion=0.01, show_theta=0):
     import numpy as np
     import guan
@@ -200,10 +195,10 @@ def rotation_of_degenerate_vectors_array(vector1_array, vector2_array, precision
     index2 = np.argmax(np.abs(vector2_sum))
     for i0 in range(Num_k):
         vector1_array[i0], vector2_array[i0] = guan.rotation_of_degenerate_vectors(vector1=vector1_array[i0], vector2=vector2_array[i0], index1=index1, index2=index2, precision=precision, criterion=criterion, show_theta=show_theta)
-    guan.statistics_of_guan_package()
     return vector1_array, vector2_array
 
 # 在一组数据中找到数值相近的数
+@guan.function_decorator
 def find_close_values_in_one_array(array, precision=1e-2):
     new_array = []
     i0 = 0
@@ -214,11 +209,10 @@ def find_close_values_in_one_array(array, precision=1e-2):
                 new_array.append([a1, a2])
             j0 +=1
         i0 += 1
-    import guan
-    guan.statistics_of_guan_package()
     return new_array
 
 # 寻找能带的简并点
+@guan.function_decorator
 def find_degenerate_points(k_array, eigenvalue_array, precision=1e-2):
     import guan
     degenerate_k_array = []
@@ -230,6 +224,4 @@ def find_degenerate_points(k_array, eigenvalue_array, precision=1e-2):
             degenerate_k_array.append(k)
             degenerate_eigenvalue_array.append(degenerate_points)
         i0 += 1
-    import guan
-    guan.statistics_of_guan_package()
     return degenerate_k_array, degenerate_eigenvalue_array
\ No newline at end of file
diff --git a/PyPI/src/guan/basic_functions.py b/PyPI/src/guan/basic_functions.py
index 91d8f16..e2e4cc6 100644
--- a/PyPI/src/guan/basic_functions.py
+++ b/PyPI/src/guan/basic_functions.py
@@ -1,129 +1,125 @@
 # Module: basic_functions
+import guan
 
 # 测试
+@guan.function_decorator
 def test():
     print('\nSuccess in the installation of Guan package!\n')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 泡利矩阵
+@guan.function_decorator
 def sigma_0():
     import numpy as np
-    import guan
-    guan.statistics_of_guan_package()
     return np.eye(2)
 
+@guan.function_decorator
 def sigma_x():
     import numpy as np
-    import guan
-    guan.statistics_of_guan_package()
     return np.array([[0, 1],[1, 0]])
 
+@guan.function_decorator
 def sigma_y():
     import numpy as np
-    import guan
-    guan.statistics_of_guan_package()
     return np.array([[0, -1j],[1j, 0]])
 
+@guan.function_decorator
 def sigma_z():
     import numpy as np
-    import guan
-    guan.statistics_of_guan_package()
     return np.array([[1, 0],[0, -1]])
 
 # 泡利矩阵的张量积
+@guan.function_decorator
 def sigma_00():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_0(), guan.sigma_0())
 
+@guan.function_decorator
 def sigma_0x():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_0(), guan.sigma_x())
 
+@guan.function_decorator
 def sigma_0y():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_0(), guan.sigma_y())
 
+@guan.function_decorator
 def sigma_0z():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_0(), guan.sigma_z())
 
+@guan.function_decorator
 def sigma_x0():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_x(), guan.sigma_0())
 
+@guan.function_decorator
 def sigma_xx():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_x(), guan.sigma_x())
 
+@guan.function_decorator
 def sigma_xy():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_x(), guan.sigma_y())
 
+@guan.function_decorator
 def sigma_xz():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_x(), guan.sigma_z())
 
+@guan.function_decorator
 def sigma_y0():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_y(), guan.sigma_0())
 
+@guan.function_decorator
 def sigma_yx():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_y(), guan.sigma_x())
 
+@guan.function_decorator
 def sigma_yy():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_y(), guan.sigma_y())
 
+@guan.function_decorator
 def sigma_yz():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_y(), guan.sigma_z())
 
+@guan.function_decorator
 def sigma_z0():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_z(), guan.sigma_0())
 
+@guan.function_decorator
 def sigma_zx():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_z(), guan.sigma_x())
 
+@guan.function_decorator
 def sigma_zy():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_z(), guan.sigma_y())
 
+@guan.function_decorator
 def sigma_zz():
     import numpy as np
     import guan
-    guan.statistics_of_guan_package()
     return np.kron(guan.sigma_z(), guan.sigma_z())
\ No newline at end of file
diff --git a/PyPI/src/guan/data_processing.py b/PyPI/src/guan/data_processing.py
index 6c1f25d..a9e5d4b 100644
--- a/PyPI/src/guan/data_processing.py
+++ b/PyPI/src/guan/data_processing.py
@@ -1,6 +1,8 @@
 # Module: data_processing (including figure-plotting and file-reading/writing)
+import guan
 
 # 导入plt, fig, ax
+@guan.function_decorator
 def import_plt_and_start_fig_ax(adjust_bottom=0.2, adjust_left=0.2, labelsize=20):
     import matplotlib.pyplot as plt
     fig, ax = plt.subplots()
@@ -9,11 +11,10 @@ def import_plt_and_start_fig_ax(adjust_bottom=0.2, adjust_left=0.2, labelsize=20
     ax.tick_params(labelsize=labelsize) 
     labels = ax.get_xticklabels() + ax.get_yticklabels()
     [label.set_fontname('Times New Roman') for label in labels]
-    import guan
-    guan.statistics_of_guan_package()
     return plt, fig, ax
 
 # 基于plt, fig, ax画图
+@guan.function_decorator
 def plot_without_starting_fig(plt, fig, ax, x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, style='', y_min=None, y_max=None, linewidth=None, markersize=None, color=None): 
     if color==None:
         ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
@@ -28,10 +29,9 @@ def plot_without_starting_fig(plt, fig, ax, x_array, y_array, xlabel='x', ylabel
         if y_max==None:
             y_max=max(y_array)
         ax.set_ylim(y_min, y_max)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 画图
+@guan.function_decorator
 def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2): 
     import guan
     plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize)
@@ -50,9 +50,9 @@ def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labels
     if show == 1:
         plt.show()
     plt.close('all')
-    guan.statistics_of_guan_package()
 
 # 一组横坐标数据，两组纵坐标数据画图
+@guan.function_decorator
 def plot_two_array(x_array, y1_array, y2_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, markersize_1=None, markersize_2=None, adjust_bottom=0.2, adjust_left=0.2): 
     import guan
     plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize) 
@@ -76,9 +76,9 @@ def plot_two_array(x_array, y1_array, y2_array, xlabel='x', ylabel='y', title=''
     if show == 1:
         plt.show()
     plt.close('all')
-    guan.statistics_of_guan_package()
 
 # 两组横坐标数据，两组纵坐标数据画图
+@guan.function_decorator
 def plot_two_array_with_two_horizontal_array(x1_array, x2_array, y1_array, y2_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, markersize_1=None, markersize_2=None, adjust_bottom=0.2, adjust_left=0.2): 
     import guan
     plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize) 
@@ -102,9 +102,9 @@ def plot_two_array_with_two_horizontal_array(x1_array, x2_array, y1_array, y2_ar
     if show == 1:
         plt.show()
     plt.close('all')
-    guan.statistics_of_guan_package()
 
 # 一组横坐标数据，三组纵坐标数据画图
+@guan.function_decorator
 def plot_three_array(x_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', style_3='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, linewidth_3=None,markersize_1=None, markersize_2=None, markersize_3=None, adjust_bottom=0.2, adjust_left=0.2): 
     import guan
     plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize) 
@@ -131,9 +131,9 @@ def plot_three_array(x_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='
     if show == 1:
         plt.show()
     plt.close('all')
-    guan.statistics_of_guan_package()
 
 # 三组横坐标数据，三组纵坐标数据画图
+@guan.function_decorator
 def plot_three_array_with_three_horizontal_array(x1_array, x2_array, x3_array, y1_array, y2_array, y3_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', file_format='.jpg', dpi=300, style_1='', style_2='', style_3='', y_min=None, y_max=None, linewidth_1=None, linewidth_2=None, linewidth_3=None,markersize_1=None, markersize_2=None, markersize_3=None, adjust_bottom=0.2, adjust_left=0.2): 
     import guan
     plt, fig, ax = guan.import_plt_and_start_fig_ax(adjust_bottom=adjust_bottom, adjust_left=adjust_left, labelsize=labelsize) 
@@ -160,9 +160,9 @@ def plot_three_array_with_three_horizontal_array(x1_array, x2_array, x3_array, y
     if show == 1:
         plt.show()
     plt.close('all')
-    guan.statistics_of_guan_package()
 
 # 画三维图
+@guan.function_decorator
 def plot_3d_surface(x_array, y_array, matrix, xlabel='x', ylabel='y', zlabel='z', title='', fontsize=20, labelsize=15, show=1, save=0, filename='a', file_format='.jpg', dpi=300, z_min=None, z_max=None, rcount=100, ccount=100): 
     import numpy as np
     import matplotlib.pyplot as plt
@@ -202,10 +202,9 @@ def plot_3d_surface(x_array, y_array, matrix, xlabel='x', ylabel='y', zlabel='z'
     if show == 1:
         plt.show()
     plt.close('all')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 画Contour图
+@guan.function_decorator
 def plot_contour(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=15, cmap='jet', levels=None, show=1, save=0, filename='a', file_format='.jpg', dpi=300):
     import numpy as np
     import matplotlib.pyplot as plt
@@ -229,10 +228,9 @@ def plot_contour(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fon
     if show == 1:
         plt.show()
     plt.close('all')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 画棋盘图/伪彩色图
+@guan.function_decorator
 def plot_pcolor(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=15, cmap='jet', levels=None, show=1, save=0, filename='a', file_format='.jpg', dpi=300):  
     import numpy as np
     import matplotlib.pyplot as plt
@@ -256,10 +254,9 @@ def plot_pcolor(x_array, y_array, matrix, xlabel='x', ylabel='y', title='', font
     if show == 1:
         plt.show()
     plt.close('all')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 通过坐标画点和线
+@guan.function_decorator
 def draw_dots_and_lines(coordinate_array, draw_dots=1, draw_lines=1, max_distance=1.1, line_style='-k', linewidth=1, dot_style='ro', markersize=3, show=1, save=0, filename='a', file_format='.eps', dpi=300):
     import numpy as np
     import matplotlib.pyplot as plt
@@ -285,10 +282,9 @@ def draw_dots_and_lines(coordinate_array, draw_dots=1, draw_lines=1, max_distanc
             plt.savefig(filename+file_format)
         else:
             plt.savefig(filename+file_format, dpi=dpi)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 合并两个图片
+@guan.function_decorator
 def combine_two_images(image_path_array, figsize=(16,8), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
     import numpy as np
     num = np.array(image_path_array).shape[0]
@@ -312,10 +308,9 @@ def combine_two_images(image_path_array, figsize=(16,8), show=0, save=1, filenam
         if save == 1:
             plt.savefig(filename+file_format, dpi=dpi)
         plt.close('all')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 合并三个图片
+@guan.function_decorator
 def combine_three_images(image_path_array, figsize=(16,5), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
     import numpy as np
     num = np.array(image_path_array).shape[0]
@@ -343,10 +338,9 @@ def combine_three_images(image_path_array, figsize=(16,5), show=0, save=1, filen
         if save == 1:
             plt.savefig(filename+file_format, dpi=dpi)
         plt.close('all')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 合并四个图片
+@guan.function_decorator
 def combine_four_images(image_path_array, figsize=(16,16), show=0, save=1, filename='a', file_format='.jpg', dpi=300):
     import numpy as np
     num = np.array(image_path_array).shape[0]
@@ -378,10 +372,9 @@ def combine_four_images(image_path_array, figsize=(16,16), show=0, save=1, filen
         if save == 1:
             plt.savefig(filename+file_format, dpi=dpi)
         plt.close('all')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 对某个目录中的txt文件批量读取和画图
+@guan.function_decorator
 def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
     import re
     import os
@@ -392,9 +385,9 @@ def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'):
                 filename = file[:-4]
                 x_array, y_array = guan.read_one_dimensional_data(filename=filename)
                 guan.plot(x_array, y_array, xlabel=xlabel, ylabel=ylabel, title=filename, show=0, save=1, filename=filename)
-    guan.statistics_of_guan_package()
 
 # 将图片制作GIF动画
+@guan.function_decorator
 def make_gif(image_path_array, filename='a', duration=0.1):
     import imageio
     images = []
@@ -402,34 +395,30 @@ def make_gif(image_path_array, filename='a', duration=0.1):
         im = imageio.imread(image_path)
         images.append(im)
     imageio.mimsave(filename+'.gif', images, 'GIF', duration=duration)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 选取Matplotlib颜色
+@guan.function_decorator
 def color_matplotlib():
     color_array = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']
-    import guan
-    guan.statistics_of_guan_package()
     return color_array
 
 # 将变量存到文件
+@guan.function_decorator
 def dump_data(data, filename, file_format='.txt'):
     import pickle
     with open(filename+file_format, 'wb') as f:
         pickle.dump(data, f)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 从文件中恢复数据到变量
+@guan.function_decorator
 def load_data(filename, file_format='.txt'):
     import pickle
     with open(filename+file_format, 'rb') as f:
         data = pickle.load(f)
-    import guan
-    guan.statistics_of_guan_package()
     return data
 
 # 读取文件中的一维数据（一行一组x和y）
+@guan.function_decorator
 def read_one_dimensional_data(filename='a', file_format='.txt'): 
     import numpy as np
     f = open(filename+file_format, 'r')
@@ -450,11 +439,10 @@ def read_one_dimensional_data(filename='a', file_format='.txt'):
                 y_array = [y_row]
             else:
                 y_array = np.append(y_array, [y_row], axis=0)
-    import guan
-    guan.statistics_of_guan_package()
     return x_array, y_array
 
 # 读取文件中的一维数据（一行一组x和y）（支持复数形式）
+@guan.function_decorator
 def read_one_dimensional_complex_data(filename='a', file_format='.txt'): 
     import numpy as np
     f = open(filename+file_format, 'r')
@@ -475,11 +463,10 @@ def read_one_dimensional_complex_data(filename='a', file_format='.txt'):
                 y_array = [y_row]
             else:
                 y_array = np.append(y_array, [y_row], axis=0)
-    import guan
-    guan.statistics_of_guan_package()
     return x_array, y_array
 
 # 读取文件中的二维数据（第一行和第一列分别为横纵坐标）
+@guan.function_decorator
 def read_two_dimensional_data(filename='a', file_format='.txt'): 
     import numpy as np
     f = open(filename+file_format, 'r')
@@ -506,11 +493,10 @@ def read_two_dimensional_data(filename='a', file_format='.txt'):
                 matrix = [matrix_row]
             else:
                 matrix = np.append(matrix, [matrix_row], axis=0)
-    import guan
-    guan.statistics_of_guan_package()
     return x_array, y_array, matrix
 
 # 读取文件中的二维数据（第一行和第一列分别为横纵坐标）（支持复数形式）
+@guan.function_decorator
 def read_two_dimensional_complex_data(filename='a', file_format='.txt'): 
     import numpy as np
     f = open(filename+file_format, 'r')
@@ -537,34 +523,30 @@ def read_two_dimensional_complex_data(filename='a', file_format='.txt'):
                 matrix = [matrix_row]
             else:
                 matrix = np.append(matrix, [matrix_row], axis=0)
-    import guan
-    guan.statistics_of_guan_package()
     return x_array, y_array, matrix
 
 # 读取文件中的二维数据（不包括x和y）
+@guan.function_decorator
 def read_two_dimensional_data_without_xy_array(filename='a', file_format='.txt'):
     import numpy as np
     matrix = np.loadtxt(filename+file_format)
-    import guan
-    guan.statistics_of_guan_package()
     return matrix
 
 # 打开文件用于新增内容
+@guan.function_decorator
 def open_file(filename='a', file_format='.txt'):
     f = open(filename+file_format, 'a', encoding='UTF-8')
-    import guan
-    guan.statistics_of_guan_package()
     return f
 
 # 在文件中写入一维数据（一行一组x和y）
+@guan.function_decorator
 def write_one_dimensional_data(x_array, y_array, filename='a', file_format='.txt'):
     import guan
     with open(filename+file_format, 'w', encoding='UTF-8') as f:
         guan.write_one_dimensional_data_without_opening_file(x_array, y_array, f)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 在文件中写入一维数据（一行一组x和y）（需要输入已打开的文件）
+@guan.function_decorator
 def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
     import numpy as np
     x_array = np.array(x_array)
@@ -579,17 +561,16 @@ def write_one_dimensional_data_without_opening_file(x_array, y_array, f):
                 f.write(str(y_array[i0, j0])+'   ')
             f.write('\n')
         i0 += 1
-    import guan
-    guan.statistics_of_guan_package()
 
 # 在文件中写入二维数据（第一行和第一列分别为横纵坐标）
+@guan.function_decorator
 def write_two_dimensional_data(x_array, y_array, matrix, filename='a', file_format='.txt'):
     import guan
     with open(filename+file_format, 'w', encoding='UTF-8') as f:
         guan.write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f)
-    guan.statistics_of_guan_package()
 
 # 在文件中写入二维数据（第一行和第一列分别为横纵坐标）（需要输入已打开的文件）
+@guan.function_decorator
 def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f):
     import numpy as np
     x_array = np.array(x_array)
@@ -608,26 +589,25 @@ def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f)
             j0 += 1
         f.write('\n')
         i0 += 1
-    import guan
-    guan.statistics_of_guan_package()
 
 # 在文件中写入二维数据（不包括x和y）
+@guan.function_decorator
 def write_two_dimensional_data_without_xy_array(matrix, filename='a', file_format='.txt'):
     import guan
     with open(filename+file_format, 'w', encoding='UTF-8') as f:
         guan.write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f)
-    guan.statistics_of_guan_package()
 
 # 在文件中写入二维数据（不包括x和y）（需要输入已打开的文件）
+@guan.function_decorator
 def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f):
     for row in matrix:
         for element in row:
             f.write(str(element)+'   ')
         f.write('\n')
     import guan
-    guan.statistics_of_guan_package()
 
 # 以显示编号的样式，打印数组
+@guan.function_decorator
 def print_array_with_index(array, show_index=1, index_type=0):
     if show_index==0:
         for i0 in array:
@@ -643,10 +623,9 @@ def print_array_with_index(array, show_index=1, index_type=0):
             for i0 in array:
                 index += 1
                 print(index, i0)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 获取目录中的所有文件名
+@guan.function_decorator
 def get_all_filenames_in_directory(directory='./', file_format=None):
     import os
     file_list = []
@@ -660,6 +639,7 @@ def get_all_filenames_in_directory(directory='./', file_format=None):
     return file_list
 
 # 读取文件夹中某种文本文件类型的文件路径和内容
+@guan.function_decorator
 def read_text_files_in_directory(directory='./', file_format='.md'):
     import os
     file_list = []
@@ -671,11 +651,10 @@ def read_text_files_in_directory(directory='./', file_format='.md'):
     for file in file_list:
         with open(file, 'r') as f:
             content_array.append(f.read())
-    import guan
-    guan.statistics_of_guan_package()
     return file_list, content_array
 
 # 在多个文本文件中查找关键词
+@guan.function_decorator
 def find_words_in_multiple_files(words, directory='./', file_format='.md'):
     import guan
     file_list, content_array = guan.read_text_files_in_directory(directory=directory, file_format=file_format)
@@ -684,10 +663,10 @@ def find_words_in_multiple_files(words, directory='./', file_format='.md'):
     for i0 in range(num_files):
         if words in content_array[i0]:
             file_list_with_words.append(file_list[i0])
-    guan.statistics_of_guan_package()
     return file_list_with_words
 
 # 并行计算前的预处理，把参数分成多份
+@guan.function_decorator
 def preprocess_for_parallel_calculations(parameter_array_all, cpus=1, task_index=0):
     import numpy as np
     num_all = np.array(parameter_array_all).shape[0]
@@ -700,36 +679,32 @@ def preprocess_for_parallel_calculations(parameter_array_all, cpus=1, task_index
             parameter_array = parameter_array_all[task_index*num_parameter:(task_index+1)*num_parameter]
         else:
             parameter_array = parameter_array_all[task_index*num_parameter:num_all]
-    import guan
-    guan.statistics_of_guan_package()
     return parameter_array
 
 # 随机获得一个整数，左闭右闭
+@guan.function_decorator
 def get_random_number(start=0, end=1):
     import random
     rand_number = random.randint(start, end)   # 左闭右闭 [start, end]
-    import guan
-    guan.statistics_of_guan_package()
     return rand_number
 
 # 选取一个种子生成固定的随机整数
+@guan.function_decorator
 def generate_random_int_number_for_a_specific_seed(seed=0, x_min=0, x_max=10):
     import numpy as np
     np.random.seed(seed)
     rand_num = np.random.randint(x_min, x_max) # 左闭右开[x_min, x_max)
-    import guan
-    guan.statistics_of_guan_package()
     return rand_num
 
 # 使用jieba软件包进行分词
+@guan.function_decorator
 def divide_text_into_words(text):
     import jieba
     words = jieba.lcut(text)
-    import guan
-    guan.statistics_of_guan_package()
     return words
 
 # 判断某个字符是中文还是英文或其他
+@guan.function_decorator
 def check_Chinese_or_English(a):  
     if '\u4e00' <= a <= '\u9fff' :  
         word_type = 'Chinese'  
@@ -740,6 +715,7 @@ def check_Chinese_or_English(a):
     return word_type
 
 # 统计中英文文本的字数，默认不包括空格
+@guan.function_decorator
 def count_words(text, include_space=0, show_words=0):
     import jieba
     import guan
@@ -764,46 +740,41 @@ def count_words(text, include_space=0, show_words=0):
             new_words_2.append(word)
     if show_words == 1:
         print(new_words_2)
-    import guan
-    guan.statistics_of_guan_package()
     return num_words
 
 # 将RGB转成HEX
+@guan.function_decorator
 def rgb_to_hex(rgb, pound=1):
-    import guan
-    guan.statistics_of_guan_package()
     if pound==0:
         return '%02x%02x%02x' % rgb
     else:
         return '#%02x%02x%02x' % rgb
 
 # 将HEX转成RGB
+@guan.function_decorator
 def hex_to_rgb(hex):
     hex = hex.lstrip('#')
     length = len(hex)
-    import guan
-    guan.statistics_of_guan_package()
     return tuple(int(hex[i:i+length//3], 16) for i in range(0, length, length//3))
 
 # 使用MD5进行散列加密
+@guan.function_decorator
 def encryption_MD5(password, salt=''):
     import hashlib
     password = salt+password
     hashed_password = hashlib.md5(password.encode()).hexdigest()
-    import guan
-    guan.statistics_of_guan_package()
     return hashed_password
 
 # 使用SHA-256进行散列加密
+@guan.function_decorator
 def encryption_SHA_256(password, salt=''):
     import hashlib
     password = salt+password
     hashed_password = hashlib.sha256(password.encode()).hexdigest()
-    import guan
-    guan.statistics_of_guan_package()
     return hashed_password
 
 # 自动先后运行程序
+@guan.function_decorator
 def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='python ', show_time=0):
     import os
     import time
@@ -821,25 +792,22 @@ def run_programs_sequentially(program_files=['./a.py', './b.py'], execute='pytho
     if show_time == 1:
         end = time.time()
         print('Total running time = '+str((end-start)/60)+' min')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 如果不存在文件夹，则新建文件夹
+@guan.function_decorator
 def make_directory(directory='./test'):
     import os
     if not os.path.exists(directory):
         os.makedirs(directory)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 复制一份文件
+@guan.function_decorator
 def copy_file(file1='./a.txt', file2='./b.txt'):
     import shutil
     shutil.copy(file1, file2)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 拼接两个PDF文件
+@guan.function_decorator
 def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_file='combined_file.pdf'):
     import PyPDF2
     output_pdf = PyPDF2.PdfWriter()
@@ -852,6 +820,4 @@ def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_fil
         for page in range(len(pdf2.pages)):
             output_pdf.add_page(pdf2.pages[page])
     with open(output_file, 'wb') as combined_file:
-        output_pdf.write(combined_file)
-    import guan
-    guan.statistics_of_guan_package()
\ No newline at end of file
+        output_pdf.write(combined_file)
\ No newline at end of file
diff --git a/PyPI/src/guan/density_of_states.py b/PyPI/src/guan/density_of_states.py
index ed4b48f..684715e 100644
--- a/PyPI/src/guan/density_of_states.py
+++ b/PyPI/src/guan/density_of_states.py
@@ -1,16 +1,18 @@
 # Module: density_of_states
+import guan
 
 # 计算体系的总态密度
+@guan.function_decorator
 def total_density_of_states(fermi_energy, hamiltonian, broadening=0.01):
     import numpy as np
     import math
     import guan
     green = guan.green_function(fermi_energy, hamiltonian, broadening)
     total_dos = -np.trace(np.imag(green))/math.pi
-    guan.statistics_of_guan_package()
     return total_dos
 
 # 对于不同费米能，计算体系的总态密度
+@guan.function_decorator
 def total_density_of_states_with_fermi_energy_array(fermi_energy_array, hamiltonian, broadening=0.01, print_show=0):
     import numpy as np
     import guan
@@ -22,10 +24,10 @@ def total_density_of_states_with_fermi_energy_array(fermi_energy_array, hamilton
             print(fermi_energy)
         total_dos_array[i0] = guan.total_density_of_states(fermi_energy, hamiltonian, broadening)
         i0 += 1
-    guan.statistics_of_guan_package()
     return total_dos_array
 
 # 计算方格子的局域态密度（其中，哈密顿量的维度为：dim_hamiltonian = N1*N2*internal_degree）
+@guan.function_decorator
 def local_density_of_states_for_square_lattice(fermi_energy, hamiltonian, N1, N2, internal_degree=1, broadening=0.01):
     import numpy as np
     import math
@@ -36,10 +38,10 @@ def local_density_of_states_for_square_lattice(fermi_energy, hamiltonian, N1, N2
         for i2 in range(N2):
             for i in range(internal_degree): 
                 local_dos[i2, i1] = local_dos[i2, i1]-np.imag(green[i1*N2*internal_degree+i2*internal_degree+i, i1*N2*internal_degree+i2*internal_degree+i])/math.pi
-    guan.statistics_of_guan_package()
     return local_dos
 
 # 计算立方格子的局域态密度（其中，哈密顿量的维度为：dim_hamiltonian = N1*N2*N3*internal_degree）
+@guan.function_decorator
 def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2, N3, internal_degree=1, broadening=0.01):
     import numpy as np
     import math
@@ -51,10 +53,10 @@ def local_density_of_states_for_cubic_lattice(fermi_energy, hamiltonian, N1, N2,
             for i3 in range(N3):
                 for i in range(internal_degree): 
                     local_dos[i3, i2, i1] = local_dos[i3, i2, i1]-np.imag(green[i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i, i1*N2*N3*internal_degree+i2*N3*internal_degree+i3*internal_degree+i])/math.pi
-    guan.statistics_of_guan_package()
     return local_dos
 
 # 利用Dyson方程，计算方格子的局域态密度（其中，h00的维度为：dim_h00 = N2*internal_degree）
+@guan.function_decorator
 def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy, h00, h01, N2, N1, internal_degree=1, broadening=0.01):
     import numpy as np
     import math
@@ -85,10 +87,10 @@ def local_density_of_states_for_square_lattice_using_dyson_equation(fermi_energy
         for i2 in range(N2):
             for i in range(internal_degree):
                 local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/math.pi
-    guan.statistics_of_guan_package()
     return local_dos
 
 # 利用Dyson方程，计算立方格子的局域态密度（其中，h00的维度为：dim_h00 = N2*N3*internal_degree）
+@guan.function_decorator
 def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy, h00, h01, N3, N2, N1, internal_degree=1, broadening=0.01):
     import numpy as np
     import math
@@ -119,11 +121,11 @@ def local_density_of_states_for_cubic_lattice_using_dyson_equation(fermi_energy,
         for i2 in range(N2):
             for i3 in range(N3):
                 for i in range(internal_degree):
-                    local_dos[i3, i2, i1] = local_dos[i3, i2, i1] -np.imag(green_ii_n_minus[i2*N3*internal_degree+i3*internal_degree+i, i2*N3*internal_degree+i3*internal_degree+i])/math.pi       
-    guan.statistics_of_guan_package()
+                    local_dos[i3, i2, i1] = local_dos[i3, i2, i1] -np.imag(green_ii_n_minus[i2*N3*internal_degree+i3*internal_degree+i, i2*N3*internal_degree+i3*internal_degree+i])/math.pi
     return local_dos
 
 # 利用Dyson方程，计算方格子条带（考虑了电极自能）的局域态密度（其中，h00的维度为：dim_h00 = N2*internal_degree）
+@guan.function_decorator
 def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equation(fermi_energy, h00, h01, N2, N1, right_self_energy, left_self_energy, internal_degree=1, broadening=0.01):
     import numpy as np
     import math
@@ -160,5 +162,4 @@ def local_density_of_states_for_square_lattice_with_self_energy_using_dyson_equa
         for i2 in range(N2):
             for i in range(internal_degree):
                 local_dos[i2, i1] = local_dos[i2, i1] - np.imag(green_ii_n_minus[i2*internal_degree+i, i2*internal_degree+i])/math.pi
-    guan.statistics_of_guan_package()
     return local_dos
diff --git a/PyPI/src/guan/others.py b/PyPI/src/guan/others.py
index 2425348..5c56d02 100644
--- a/PyPI/src/guan/others.py
+++ b/PyPI/src/guan/others.py
@@ -1,6 +1,8 @@
 # Module: others
+import guan
 
 # 获取运行的日期和时间并写入文件
+@guan.function_decorator
 def statistics_with_day_and_time(content='', filename='a', file_format='.txt'):
     import datetime
     datetime_today = str(datetime.date.today())
@@ -10,10 +12,9 @@ def statistics_with_day_and_time(content='', filename='a', file_format='.txt'):
            f2.write(datetime_today+' '+datetime_time+'\n')
        else:
            f2.write(datetime_today+' '+datetime_time+' '+content+'\n')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 统计Python文件中import的数量并排序
+@guan.function_decorator
 def count_number_of_import_statements(filename, file_format='.py', num=1000):
     with open(filename+file_format, 'r') as file:
         lines = file.readlines()
@@ -24,27 +25,24 @@ def count_number_of_import_statements(filename, file_format='.py', num=1000):
             import_array.append(line)
     from collections import Counter
     import_statement_counter = Counter(import_array).most_common(num)
-    import guan
-    guan.statistics_of_guan_package()
     return import_statement_counter
 
 # 根据一定的字符长度来分割文本
+@guan.function_decorator
 def split_text(text, wrap_width=3000):  
     import textwrap  
     split_text_list = textwrap.wrap(text, wrap_width)
-    import guan
-    guan.statistics_of_guan_package()
     return split_text_list
 
 # 获取CPU使用率
+@guan.function_decorator
 def get_cpu_usage(interval=1):
     import psutil
     cpu_usage = psutil.cpu_percent(interval=interval)
-    import guan
-    guan.statistics_of_guan_package()
     return cpu_usage
 
 # 获取内存信息
+@guan.function_decorator
 def get_memory_info():
     import psutil
     memory_info = psutil.virtual_memory()
@@ -55,6 +53,7 @@ def get_memory_info():
     return total_memory, used_memory, available_memory, used_memory_percent
 
 # 获取本月的所有日期
+@guan.function_decorator
 def get_days_of_the_current_month(str_or_datetime='str'):
     import datetime
     today = datetime.date.today()
@@ -71,11 +70,10 @@ def get_days_of_the_current_month(str_or_datetime='str'):
         elif str_or_datetime=='datetime':
             day_array.append(current_date)
         current_date += datetime.timedelta(days=1)
-    import guan
-    guan.statistics_of_guan_package()
     return day_array
 
 # 获取上个月份
+@guan.function_decorator
 def get_last_month():
     import datetime
     today = datetime.date.today()
@@ -85,11 +83,10 @@ def get_last_month():
         year_of_last_month = today.year - 1
     else:
         year_of_last_month = today.year
-    import guan
-    guan.statistics_of_guan_package()
     return year_of_last_month, last_month
 
 # 获取上上个月份
+@guan.function_decorator
 def get_the_month_before_last():
     import datetime
     today = datetime.date.today()
@@ -104,11 +101,10 @@ def get_the_month_before_last():
         year_of_the_month_before_last = today.year - 1
     else:
         year_of_the_month_before_last = today.year
-    import guan
-    guan.statistics_of_guan_package()
     return year_of_the_month_before_last, the_month_before_last
 
 # 获取上个月的所有日期
+@guan.function_decorator
 def get_days_of_the_last_month(str_or_datetime='str'):
     import datetime
     import guan
@@ -127,10 +123,10 @@ def get_days_of_the_last_month(str_or_datetime='str'):
         elif str_or_datetime=='datetime':
             day_array.append(current_date)
         current_date += datetime.timedelta(days=1)
-    guan.statistics_of_guan_package()
     return day_array
 
 # 获取上上个月的所有日期
+@guan.function_decorator
 def get_days_of_the_month_before_last(str_or_datetime='str'):
     import datetime
     import guan
@@ -149,39 +145,38 @@ def get_days_of_the_month_before_last(str_or_datetime='str'):
         elif str_or_datetime=='datetime':
             day_array.append(current_date)
         current_date += datetime.timedelta(days=1)
-    guan.statistics_of_guan_package()
     return day_array
 
 # 获取所有股票
+@guan.function_decorator
 def all_stocks():
     import numpy as np
     import akshare as ak
     stocks = ak.stock_zh_a_spot_em()
     title = np.array(stocks.columns)
     stock_data = stocks.values
-    import guan
-    guan.statistics_of_guan_package()
     return title, stock_data
 
 # 获取所有股票的代码
+@guan.function_decorator
 def all_stock_symbols():
     import guan
     title, stock_data = guan.all_stocks()
     stock_symbols = stock_data[:, 1]
-    guan.statistics_of_guan_package()
     return stock_symbols
 
 # 从股票代码获取股票名称
+@guan.function_decorator
 def find_stock_name_from_symbol(symbol='000002'):
     import guan
     title, stock_data = guan.all_stocks()
     for stock in stock_data:
         if symbol in stock:
            stock_name = stock[2]
-    guan.statistics_of_guan_package()
     return stock_name
 
 # 获取单个股票的历史数据
+@guan.function_decorator
 def history_data_of_one_stock(symbol='000002', period='daily', start_date="19000101", end_date='21000101'):
     # period = 'daily'
     # period = 'weekly'
@@ -191,20 +186,18 @@ def history_data_of_one_stock(symbol='000002', period='daily', start_date="19000
     stock = ak.stock_zh_a_hist(symbol=symbol, period=period, start_date=start_date, end_date=end_date)
     title = np.array(stock.columns)
     stock_data = stock.values[::-1]
-    import guan
-    guan.statistics_of_guan_package()
     return title, stock_data
 
 # 获取软件包中的所有模块名
+@guan.function_decorator
 def get_all_modules_in_one_package(package_name='guan'):
     import pkgutil
     package = __import__(package_name)
     module_names = [name for _, name, _ in pkgutil.iter_modules(package.__path__)]
-    import guan
-    guan.statistics_of_guan_package()
     return module_names
 
 # 获取软件包中一个模块的所有函数名
+@guan.function_decorator
 def get_all_functions_in_one_module(module_name, package_name='guan'):
     import inspect
     function_names = []
@@ -212,11 +205,10 @@ def get_all_functions_in_one_module(module_name, package_name='guan'):
     for name, obj in inspect.getmembers(module):
         if inspect.isfunction(obj):
             function_names.append(name)
-    import guan
-    guan.statistics_of_guan_package()
     return function_names
 
 # 获取软件包中的所有函数名
+@guan.function_decorator
 def get_all_functions_in_one_package(package_name='guan', print_show=1):
     import guan
     module_names = guan.get_all_modules_in_one_package(package_name=package_name)
@@ -231,10 +223,10 @@ def get_all_functions_in_one_package(package_name='guan', print_show=1):
                 print('function:', name)
         if print_show == 1:
             print()
-    guan.statistics_of_guan_package()
     return all_function_names
 
 # 获取包含某个字符的进程PID值
+@guan.function_decorator
 def get_PID(name):
     import subprocess
     command = "ps -ef | grep "+name
@@ -244,11 +236,17 @@ def get_PID(name):
     import re
     ps_ef = re.split(r'\s+', ps_ef)
     id_running = ps_ef[1]
-    import guan
-    guan.statistics_of_guan_package()
     return id_running
 
+# 获取函数的源码
+@guan.function_decorator
+def get_function_source(function_name):
+    import inspect
+    function_source = inspect.getsource(function_name)
+    return function_source
+
 # 在服务器上运行函数（说明：接口服务可能为关闭状态，如果无法使用请联系管理员。目前仅支持长度较短的函数，此外由于服务器只获取一个函数内的代码，因此需要函数是独立的可运行的代码。需要注意的是：返回的值是字符串类型，需要自行转换成数字类型。）
+@guan.function_decorator
 def run(function_name, args=(), return_show=0, get_print=1):
     import socket
     import json
@@ -283,10 +281,10 @@ def run(function_name, args=(), return_show=0, get_print=1):
             except:
                 break
         client_socket.close()
-    guan.statistics_of_guan_package()
     return return_data
 
 # 在服务器上运行大语言模型，通过Python函数调用（说明：接口服务可能为关闭状态，如果无法使用请联系管理员）
+@guan.function_decorator
 def chat(prompt='你好', stream_show=1, top_p=0.8, temperature=0.8):
     import socket
     import json
@@ -321,11 +319,10 @@ def chat(prompt='你好', stream_show=1, top_p=0.8, temperature=0.8):
             except:
                 break
         client_socket.close()
-    import guan
-    guan.statistics_of_guan_package()
     return response
 
 # 查找文件名相同的文件
+@guan.function_decorator
 def find_repeated_file_with_same_filename(directory='./', ignored_directory_with_words=[], ignored_file_with_words=[], num=1000):
     import os
     from collections import Counter
@@ -347,11 +344,10 @@ def find_repeated_file_with_same_filename(directory='./', ignored_directory_with
     for item in count_file:
         if item[1]>1:
             repeated_file.append(item)
-    import guan
-    guan.statistics_of_guan_package()
     return repeated_file
 
 # 统计各个子文件夹中的文件数量
+@guan.function_decorator
 def count_file_in_sub_directory(directory='./', sort=0, reverse=1, print_show=1, smaller_than_num=None):
     import os
     import numpy as np
@@ -402,12 +398,10 @@ def count_file_in_sub_directory(directory='./', sort=0, reverse=1, print_show=1,
                         print(dirs_list[i0])
                         print(count_file_array[i0])
                         print()
-    
-    import guan
-    guan.statistics_of_guan_package()
     return sub_directory, num_in_sub_directory
 
 # 改变当前的目录位置
+@guan.function_decorator
 def change_directory_by_replacement(current_key_word='code', new_key_word='data'):
     import os
     code_path = os.getcwd()
@@ -416,10 +410,9 @@ def change_directory_by_replacement(current_key_word='code', new_key_word='data'
     if os.path.exists(data_path) == False:
         os.makedirs(data_path)
     os.chdir(data_path)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 在多个子文件夹中产生必要的文件，例如 readme.md
+@guan.function_decorator
 def creat_necessary_file(directory, filename='readme', file_format='.md', content='', overwrite=None, ignored_directory_with_words=[]):
     import os
     directory_with_file = []
@@ -447,20 +440,18 @@ def creat_necessary_file(directory, filename='readme', file_format='.md', conten
         f = open(filename+file_format, 'w', encoding="utf-8")
         f.write(content)
         f.close()
-    import guan
-    guan.statistics_of_guan_package()
 
 # 删除特定文件名的文件（慎用）
+@guan.function_decorator
 def delete_file_with_specific_name(directory, filename='readme', file_format='.md'):
     import os
     for root, dirs, files in os.walk(directory):
         for i0 in range(len(files)):
             if files[i0] == filename+file_format:
                 os.remove(root+'/'+files[i0])
-    import guan
-    guan.statistics_of_guan_package()
 
 # 将所有文件移到根目录（慎用）
+@guan.function_decorator
 def move_all_files_to_root_directory(directory):
     import os
     import shutil
@@ -473,10 +464,9 @@ def move_all_files_to_root_directory(directory):
                 os.rmdir(root) 
             except:
                 pass
-    import guan
-    guan.statistics_of_guan_package()
 
 # 将文件目录结构写入Markdown文件
+@guan.function_decorator
 def write_file_list_in_markdown(directory='./', filename='a', reverse_positive_or_negative=1, starting_from_h1=None, banned_file_format=[], hide_file_format=None, divided_line=None, show_second_number=None, show_third_number=None): 
     import os
     f = open(filename+'.md', 'w', encoding="utf-8")
@@ -578,10 +568,9 @@ def write_file_list_in_markdown(directory='./', filename='a', reverse_positive_o
                                                         f.write('#')
                                                     f.write('###### '+str(filename6)+'\n\n')
     f.close()
-    import guan
-    guan.statistics_of_guan_package()
 
 # 从网页的标签中获取内容
+@guan.function_decorator
 def get_html_from_tags(link, tags=['title', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'p', 'li', 'a']):
     from bs4 import BeautifulSoup
     import urllib.request
@@ -597,19 +586,17 @@ def get_html_from_tags(link, tags=['title', 'h1', 'h2', 'h3', 'h4', 'h5', 'h6',
             content = text
         else:
             content = content + '\n\n' + text
-    import guan
-    guan.statistics_of_guan_package()
     return content
 
 # 生成二维码
+@guan.function_decorator
 def creat_qrcode(data="https://www.guanjihuan.com", filename='a', file_format='.png'):
     import qrcode
     img = qrcode.make(data)
     img.save(filename+file_format)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 将PDF文件转成文本
+@guan.function_decorator
 def pdf_to_text(pdf_path):
     from pdfminer.pdfparser import PDFParser, PDFDocument
     from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
@@ -638,21 +625,19 @@ def pdf_to_text(pdf_path):
             for x in layout:
                 if isinstance(x, LTTextBox):
                     content  = content + x.get_text().strip()
-    import guan
-    guan.statistics_of_guan_package()
     return content
 
 # 获取PDF文件页数
+@guan.function_decorator
 def get_pdf_page_number(pdf_path):
     import PyPDF2
     pdf_file = open(pdf_path, 'rb')
     pdf_reader = PyPDF2.PdfReader(pdf_file)
     num_pages = len(pdf_reader.pages)
-    import guan
-    guan.statistics_of_guan_package()
     return num_pages
 
 # 获取PDF文件指定页面的内容
+@guan.function_decorator
 def pdf_to_txt_for_a_specific_page(pdf_path, page_num=1):
     import PyPDF2
     pdf_file = open(pdf_path, 'rb')
@@ -663,11 +648,10 @@ def pdf_to_txt_for_a_specific_page(pdf_path, page_num=1):
             page = pdf_reader.pages[page_num0]
             page_text = page.extract_text()
     pdf_file.close()
-    import guan
-    guan.statistics_of_guan_package()
     return page_text
 
 # 获取PDF文献中的链接。例如: link_starting_form='https://doi.org'
+@guan.function_decorator
 def get_links_from_pdf(pdf_path, link_starting_form=''):
     import PyPDF2
     import re
@@ -688,12 +672,11 @@ def get_links_from_pdf(pdf_path, link_starting_form=''):
                         if u['/A']['/URI'] != old:
                             links.append(u['/A']['/URI']) 
                             i0 += 1
-                            old = u['/A']['/URI']        
-    import guan
-    guan.statistics_of_guan_package()
+                            old = u['/A']['/URI']
     return links
 
 # 通过Sci-Hub网站下载文献
+@guan.function_decorator
 def download_with_scihub(address=None, num=1):
     from bs4 import BeautifulSoup
     import re
@@ -729,10 +712,9 @@ def download_with_scihub(address=None, num=1):
         print('Completed!\n')
     if num != 1:
         print('All completed!\n')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 将字符串转成音频
+@guan.function_decorator
 def str_to_audio(str='hello world', filename='str', rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0):
     import pyttsx3
     import guan
@@ -754,9 +736,9 @@ def str_to_audio(str='hello world', filename='str', rate=125, voice=1, read=1, s
     if read==1:
         engine.say(str)
         engine.runAndWait()
-    guan.statistics_of_guan_package()
 
 # 将txt文件转成音频
+@guan.function_decorator
 def txt_to_audio(txt_path, rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0):
     import pyttsx3
     import guan
@@ -782,9 +764,9 @@ def txt_to_audio(txt_path, rate=125, voice=1, read=1, save=0, compress=0, bitrat
     if read==1:
         engine.say(text)
         engine.runAndWait()
-    guan.statistics_of_guan_package()
 
 # 将PDF文件转成音频
+@guan.function_decorator
 def pdf_to_audio(pdf_path, rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0):
     import pyttsx3
     import guan
@@ -810,18 +792,17 @@ def pdf_to_audio(pdf_path, rate=125, voice=1, read=1, save=0, compress=0, bitrat
     if read==1:
         engine.say(text)
         engine.runAndWait()
-    guan.statistics_of_guan_package()
 
 # 将wav音频文件压缩成MP3音频文件
+@guan.function_decorator
 def compress_wav_to_mp3(wav_path, output_filename='a.mp3', bitrate='16k'):
     # Note: Beside the installation of pydub, you may also need download FFmpeg on http://www.ffmpeg.org/download.html and add the bin path to the environment variable.
     from pydub import AudioSegment
     sound = AudioSegment.from_mp3(wav_path)
     sound.export(output_filename,format="mp3",bitrate=bitrate)
-    import guan
-    guan.statistics_of_guan_package()
 
 # 播放学术单词
+@guan.function_decorator
 def play_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_translation=1, show_link=1, translation_time=2, rest_time=1):
     from bs4 import BeautifulSoup
     import re
@@ -880,10 +861,9 @@ def play_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_translati
                 time.sleep(rest_time)
                 pygame.mixer.music.stop()
                 print()
-    import guan
-    guan.statistics_of_guan_package()
 
 # 播放挑选过后的学术单词
+@guan.function_decorator
 def play_selected_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_link=1, rest_time=3):
     from bs4 import BeautifulSoup
     import re
@@ -938,10 +918,9 @@ def play_selected_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_
                 time.sleep(rest_time)
                 pygame.mixer.music.stop()
                 print()
-    import guan
-    guan.statistics_of_guan_package()
 
 # 播放元素周期表上的单词
+@guan.function_decorator
 def play_element_words(random_on=0, show_translation=1, show_link=1, translation_time=2, rest_time=1):
     from bs4 import BeautifulSoup
     import re
@@ -995,42 +974,41 @@ def play_element_words(random_on=0, show_translation=1, show_link=1, translation
                 time.sleep(rest_time)
                 pygame.mixer.music.stop()
                 print()
-    import guan
-    guan.statistics_of_guan_package()
 
 # Guan软件包的使用统计（不涉及到用户的个人数据）
 global_variable_of_first_guan_package_calling = []
-def statistics_of_guan_package():
+def statistics_of_guan_package(function_name=None):
     import guan
-    function_name = guan.get_calling_function_name(layer=2)
+    if function_name == None:
+        function_name = guan.get_calling_function_name(layer=2)
+    else:
+        pass
     global global_variable_of_first_guan_package_calling
     if function_name not in global_variable_of_first_guan_package_calling:
         global_variable_of_first_guan_package_calling.append(function_name)
-        function_calling_name = guan.get_calling_function_name(layer=3)
-        if function_calling_name == '<module>':
-            try:
-                import socket
-                datetime_date = guan.get_date()
-                datetime_time = guan.get_time()
-                current_version = guan.get_current_version('guan')
-                client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-                client_socket.settimeout(0.5)
-                client_socket.connect(('socket.guanjihuan.com', 12345))
-                mac_address = guan.get_mac_address()
-                message = {
-                    'server': 'py.guanjihuan.com',
-                    'date': datetime_date,
-                    'time': datetime_time,
-                    'version': current_version,
-                    'MAC_address': mac_address,
-                    'function_name': function_name
-                }
-                import json
-                send_message = json.dumps(message)
-                client_socket.send(send_message.encode())
-                client_socket.close()
-            except:
-                pass
+        try:
+            import socket
+            datetime_date = guan.get_date()
+            datetime_time = guan.get_time()
+            current_version = guan.get_current_version('guan')
+            client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+            client_socket.settimeout(0.5)
+            client_socket.connect(('socket.guanjihuan.com', 12345))
+            mac_address = guan.get_mac_address()
+            message = {
+                'server': 'py.guanjihuan.com',
+                'date': datetime_date,
+                'time': datetime_time,
+                'version': current_version,
+                'MAC_address': mac_address,
+                'function_name': function_name
+            }
+            import json
+            send_message = json.dumps(message)
+            client_socket.send(send_message.encode())
+            client_socket.close()
+        except:
+            pass
 
 # 获取当前日期字符串
 def get_date(bar=True):
@@ -1055,12 +1033,6 @@ def get_mac_address():
     mac_address = '-'.join([mac_address[i:i+2] for i in range(0, 11, 2)])
     return mac_address
 
-# 获取函数的源码
-def get_function_source(function_name):
-    import inspect
-    function_source = inspect.getsource(function_name)
-    return function_source
-
 # 获取调用本函数的函数名
 def get_calling_function_name(layer=1):
     import inspect
diff --git a/PyPI/src/guan/quantum_transport.py b/PyPI/src/guan/quantum_transport.py
index 4df3b65..7e3b0d6 100644
--- a/PyPI/src/guan/quantum_transport.py
+++ b/PyPI/src/guan/quantum_transport.py
@@ -1,6 +1,8 @@
 # Module: quantum_transport
+import guan
 
 # 计算电导
+@guan.function_decorator
 def calculate_conductance(fermi_energy, h00, h01, length=100):
     import numpy as np
     import copy
@@ -17,10 +19,10 @@ def calculate_conductance(fermi_energy, h00, h01, length=100):
             green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
             green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
     conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
-    guan.statistics_of_guan_package()
     return conductance
 
 # 计算不同费米能下的电导
+@guan.function_decorator
 def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01, length=100, print_show=0):
     import numpy as np
     import guan
@@ -32,10 +34,10 @@ def calculate_conductance_with_fermi_energy_array(fermi_energy_array, h00, h01,
         if print_show == 1:
             print(fermi_energy, conductance_array[i0])
         i0 += 1
-    guan.statistics_of_guan_package()
     return conductance_array
 
 # 计算在势垒散射下的电导
+@guan.function_decorator
 def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barrier_length=20, barrier_potential=1):
     import numpy as np
     import copy
@@ -56,10 +58,10 @@ def calculate_conductance_with_barrier(fermi_energy, h00, h01, length=100, barri
             green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
             green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
     conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
-    guan.statistics_of_guan_package()
     return conductance
 
 # 计算在无序散射下的电导
+@guan.function_decorator
 def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100, calculation_times=1):
     import numpy as np
     import copy
@@ -85,10 +87,10 @@ def calculate_conductance_with_disorder(fermi_energy, h00, h01, disorder_intensi
         conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
         conductance_averaged += conductance
     conductance_averaged = conductance_averaged/calculation_times
-    guan.statistics_of_guan_package()
     return conductance_averaged
 
 # 计算在无序垂直切片的散射下的电导
+@guan.function_decorator
 def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100):
     import numpy as np
     import copy
@@ -109,10 +111,10 @@ def calculate_conductance_with_slice_disorder(fermi_energy, h00, h01, disorder_i
             green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
             green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
     conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
-    guan.statistics_of_guan_package()
     return conductance
 
 # 计算在无序水平切片的散射下的电导
+@guan.function_decorator
 def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translational_symmetry(fermi_energy, h00, h01, disorder_intensity=2.0, disorder_concentration=1.0, length=100):
     import numpy as np
     import copy
@@ -134,10 +136,10 @@ def calculate_conductance_with_disorder_inside_unit_cell_which_keeps_translation
             green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
             green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
     conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
-    guan.statistics_of_guan_package()
     return conductance
 
 # 计算在随机空位的散射下的电导
+@guan.function_decorator
 def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_concentration=0.5, vacancy_potential=1e9, length=100):
     import numpy as np
     import copy
@@ -159,10 +161,10 @@ def calculate_conductance_with_random_vacancy(fermi_energy, h00, h01, vacancy_co
             green_nn_n = guan.green_function_nn_n(fermi_energy, h00, h01, green_nn_n, broadening=0, self_energy=right_self_energy)
             green_0n_n = guan.green_function_in_n(green_0n_n, h01, green_nn_n)
     conductance = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
-    guan.statistics_of_guan_package()
     return conductance
 
 # 计算在不同无序散射强度下的电导
+@guan.function_decorator
 def calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01, disorder_intensity_array, disorder_concentration=1.0, length=100, calculation_times=1, print_show=0):
     import numpy as np
     import guan
@@ -176,10 +178,10 @@ def calculate_conductance_with_disorder_intensity_array(fermi_energy, h00, h01,
             print(disorder_intensity, conductance_array[i0]/calculation_times)
         i0 += 1
     conductance_array = conductance_array/calculation_times
-    guan.statistics_of_guan_package()
     return conductance_array
 
 # 计算在不同无序浓度下的电导
+@guan.function_decorator
 def calculate_conductance_with_disorder_concentration_array(fermi_energy, h00, h01, disorder_concentration_array, disorder_intensity=2.0, length=100, calculation_times=1, print_show=0):
     import numpy as np
     import guan
@@ -193,10 +195,10 @@ def calculate_conductance_with_disorder_concentration_array(fermi_energy, h00, h
             print(disorder_concentration, conductance_array[i0]/calculation_times)
         i0 += 1
     conductance_array = conductance_array/calculation_times
-    guan.statistics_of_guan_package()
     return conductance_array
 
 # 计算在不同无序散射长度下的电导
+@guan.function_decorator
 def calculate_conductance_with_scattering_length_array(fermi_energy, h00, h01, length_array, disorder_intensity=2.0, disorder_concentration=1.0, calculation_times=1, print_show=0):
     import numpy as np
     import guan
@@ -210,10 +212,10 @@ def calculate_conductance_with_scattering_length_array(fermi_energy, h00, h01, l
             print(length, conductance_array[i0]/calculation_times)
         i0 += 1
     conductance_array = conductance_array/calculation_times
-    guan.statistics_of_guan_package()
     return conductance_array
 
 # 计算得到Gamma矩阵和格林函数，用于计算六端口的量子输运
+@guan.function_decorator
 def get_gamma_array_and_green_for_six_terminal_transmission(fermi_energy, h00_for_lead_4, h01_for_lead_4, h00_for_lead_2, h01_for_lead_2, center_hamiltonian, width=10, length=50, internal_degree=1, moving_step_of_leads=10):
     import numpy as np
     import guan
@@ -257,10 +259,10 @@ def get_gamma_array_and_green_for_six_terminal_transmission(fermi_energy, h00_fo
     self_energy6, gamma6 = guan.self_energy_of_lead_with_h_lead_to_center(fermi_energy, h00_for_lead_6, h01_for_lead_1, h_lead6_to_center)
     gamma_array = [gamma1, gamma2, gamma3, gamma4, gamma5, gamma6]
     green = np.linalg.inv(fermi_energy*np.eye(internal_degree*width*length)-center_hamiltonian-self_energy1-self_energy2-self_energy3-self_energy4-self_energy5-self_energy6)
-    guan.statistics_of_guan_package()
     return gamma_array, green
 
 # 计算六端口的透射矩阵
+@guan.function_decorator
 def calculate_six_terminal_transmission_matrix(fermi_energy, h00_for_lead_4, h01_for_lead_4, h00_for_lead_2, h01_for_lead_2, center_hamiltonian, width=10, length=50, internal_degree=1, moving_step_of_leads=10):
     import numpy as np
     import guan
@@ -282,10 +284,10 @@ def calculate_six_terminal_transmission_matrix(fermi_energy, h00_for_lead_4, h01
             if j0!=i0:
                 transmission_matrix[i0, i0] = transmission_matrix[i0, i0]-transmission_matrix[i0, j0]
     transmission_matrix = np.real(transmission_matrix)
-    guan.statistics_of_guan_package()
     return transmission_matrix
 
 # 计算从电极1出发的透射系数
+@guan.function_decorator
 def calculate_six_terminal_transmissions_from_lead_1(fermi_energy, h00_for_lead_4, h01_for_lead_4, h00_for_lead_2, h01_for_lead_2, center_hamiltonian, width=10, length=50, internal_degree=1, moving_step_of_leads=10):
     import numpy as np
     import guan
@@ -295,21 +297,20 @@ def calculate_six_terminal_transmissions_from_lead_1(fermi_energy, h00_for_lead_
     transmission_14 = np.real(np.trace(np.dot(np.dot(np.dot(gamma_array[0], green), gamma_array[3]), green.transpose().conj())))
     transmission_15 = np.real(np.trace(np.dot(np.dot(np.dot(gamma_array[0], green), gamma_array[4]), green.transpose().conj())))
     transmission_16 = np.real(np.trace(np.dot(np.dot(np.dot(gamma_array[0], green), gamma_array[5]), green.transpose().conj())))
-    guan.statistics_of_guan_package()
     return transmission_12, transmission_13, transmission_14, transmission_15, transmission_16
 
 # 通过动量k的虚部，判断通道为传播通道还是衰减通道
+@guan.function_decorator
 def if_active_channel(k_of_channel):
     import numpy as np
     if np.abs(np.imag(k_of_channel))<1e-6:
         if_active = 1
     else:
         if_active = 0
-    import guan
-    guan.statistics_of_guan_package()
     return if_active
 
 # 获取通道的动量和速度，用于计算散射矩阵
+@guan.function_decorator
 def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
     import numpy as np
     import math
@@ -343,10 +344,10 @@ def get_k_and_velocity_of_channel(fermi_energy, h00, h01):
         velocity_of_channel[dim0] = eigenvalue[dim0]*np.dot(np.dot(temp[0:dim, :].transpose().conj(), h01),temp[0:dim, :])[dim0, dim0]
     velocity_of_channel = -2*np.imag(velocity_of_channel)
     eigenvector = copy.deepcopy(temp) 
-    guan.statistics_of_guan_package()
     return k_of_channel, velocity_of_channel, eigenvalue, eigenvector
 
 # 获取分类后的动量和速度，以及U和F，用于计算散射矩阵
+@guan.function_decorator
 def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01):
     import numpy as np
     import guan
@@ -396,10 +397,10 @@ def get_classified_k_velocity_u_and_f(fermi_energy, h00, h01):
     lambda_matrix_left = np.diag(lambda_left)
     f_right = np.dot(np.dot(u_right, lambda_matrix_right), np.linalg.inv(u_right))
     f_left = np.dot(np.dot(u_left, lambda_matrix_left), np.linalg.inv(u_left))
-    guan.statistics_of_guan_package()
     return k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active
 
 # 计算散射矩阵
+@guan.function_decorator
 def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
     import numpy as np
     import math
@@ -442,10 +443,10 @@ def calculate_scattering_matrix(fermi_energy, h00, h01, length=100):
     for sum_of_tran_refl in sum_of_tran_refl_array:
         if sum_of_tran_refl > 1.001:
             print('Error Alert: scattering matrix is not normalized!')
-    guan.statistics_of_guan_package()
     return transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active
 
 # 从散射矩阵中，获取散射矩阵的信息
+@guan.function_decorator
 def information_of_scattering_matrix(transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active):
     import numpy as np
     if np.array(transmission_matrix).shape==():
@@ -464,22 +465,20 @@ def information_of_scattering_matrix(transmission_matrix, reflection_matrix, k_r
     total_conductance = np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])))
     total_reflection_of_channels = np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0)
     sum_of_transmission_and_reflection_of_channels = np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0) + np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0)
-    import guan
-    guan.statistics_of_guan_package()
     return number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels
 
 # 已知h00和h01，计算散射矩阵并获得散射矩阵的信息
+@guan.function_decorator
 def calculate_scattering_matrix_and_get_information(fermi_energy, h00, h01, length=100):
     import guan
     transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active = guan.calculate_scattering_matrix(fermi_energy, h00, h01, length=length)
 
     number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels = guan.information_of_scattering_matrix(transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active)
 
-    guan.statistics_of_guan_package()
-
     return number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels
 
 # 从散射矩阵中打印出散射矩阵的信息
+@guan.function_decorator
 def print_or_write_scattering_matrix_with_information_of_scattering_matrix(number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels, print_show=1, write_file=0, filename='a', file_format='.txt'):
     if print_show == 1:
         print('\nActive channel (left or right) = ', number_of_active_channels)
@@ -522,10 +521,9 @@ def print_or_write_scattering_matrix_with_information_of_scattering_matrix(numbe
             f.write('\n')
             f.write('Total transmission of channels:\n'+str(total_transmission_of_channels)+'\n')
             f.write('Total conductance = '+str(total_conductance)+'\n')
-    import guan
-    guan.statistics_of_guan_package()
 
 # 已知h00和h01，计算散射矩阵并打印出散射矩阵的信息
+@guan.function_decorator
 def print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_show=1, write_file=0, filename='a', file_format='.txt'):
     import guan
     transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active = guan.calculate_scattering_matrix(fermi_energy, h00, h01, length=length)
@@ -534,9 +532,8 @@ def print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_s
 
     guan.print_or_write_scattering_matrix_with_information_of_scattering_matrix(number_of_active_channels, number_of_evanescent_channels, k_of_right_moving_active_channels, k_of_left_moving_active_channels, velocity_of_right_moving_active_channels, velocity_of_left_moving_active_channels, transmission_matrix_for_active_channels, reflection_matrix_for_active_channels, total_transmission_of_channels, total_conductance, total_reflection_of_channels, sum_of_transmission_and_reflection_of_channels, print_show=print_show, write_file=write_file, filename=filename, file_format=file_format)
 
-    guan.statistics_of_guan_package()
-
 # 在无序下，计算散射矩阵
+@guan.function_decorator
 def calculate_scattering_matrix_with_disorder(fermi_energy, h00, h01, length=100, disorder_intensity=2.0, disorder_concentration=1.0):
     import numpy as np
     import math
@@ -583,10 +580,10 @@ def calculate_scattering_matrix_with_disorder(fermi_energy, h00, h01, length=100
     for sum_of_tran_refl in sum_of_tran_refl_array:
         if sum_of_tran_refl > 1.001:
             print('Error Alert: scattering matrix is not normalized!')
-    guan.statistics_of_guan_package()
     return transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active
 
 # 在无序下，计算散射矩阵，并获取散射矩阵多次计算的平均信息
+@guan.function_decorator
 def calculate_scattering_matrix_with_disorder_and_get_averaged_information(fermi_energy, h00, h01, length=100, disorder_intensity=2.0, disorder_concentration=1.0, calculation_times=1):
     import guan
     transmission_matrix_for_active_channels_averaged = 0
@@ -600,5 +597,4 @@ def calculate_scattering_matrix_with_disorder_and_get_averaged_information(fermi
         reflection_matrix_for_active_channels_averaged += reflection_matrix_for_active_channels
     transmission_matrix_for_active_channels_averaged = transmission_matrix_for_active_channels_averaged/calculation_times
     reflection_matrix_for_active_channels_averaged = reflection_matrix_for_active_channels_averaged/calculation_times
-    guan.statistics_of_guan_package()
     return transmission_matrix_for_active_channels_averaged, reflection_matrix_for_active_channels_averaged
\ No newline at end of file
diff --git a/PyPI/src/guan/topological_invariant.py b/PyPI/src/guan/topological_invariant.py
index 325c5c6..596b634 100644
--- a/PyPI/src/guan/topological_invariant.py
+++ b/PyPI/src/guan/topological_invariant.py
@@ -1,6 +1,8 @@
 # Module: topological_invariant
+import guan
 
 # 通过高效法计算方格子的陈数
+@guan.function_decorator
 def calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_function, precision=100, print_show=0):
     import numpy as np
     import math
@@ -36,10 +38,10 @@ def calculate_chern_number_for_square_lattice_with_efficient_method(hamiltonian_
                 F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
                 chern_number[i] = chern_number[i] + F
     chern_number = chern_number/(2*math.pi*1j)
-    guan.statistics_of_guan_package()
     return chern_number
 
 # 通过高效法计算方格子的陈数（可计算简并的情况）
+@guan.function_decorator
 def calculate_chern_number_for_square_lattice_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision=100, print_show=0): 
     import numpy as np
     import math
@@ -106,11 +108,10 @@ def calculate_chern_number_for_square_lattice_with_efficient_method_for_degenera
             det_value= det_value*dot_matrix
             chern_number += cmath.log(det_value)
     chern_number = chern_number/(2*math.pi*1j)
-    import guan
-    guan.statistics_of_guan_package()
     return chern_number
 
 # 通过Wilson loop方法计算方格子的陈数
+@guan.function_decorator
 def calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_function, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
     import numpy as np
     import math
@@ -152,11 +153,10 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop(hamiltonian_funct
             arg = np.log(np.diagonal(wilson_loop))/1j
             chern_number = chern_number + arg
     chern_number = chern_number/(2*math.pi)
-    import guan
-    guan.statistics_of_guan_package()
     return chern_number
 
 # 通过Wilson loop方法计算方格子的陈数（可计算简并的情况）
+@guan.function_decorator
 def calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
     import numpy as np
     import math
@@ -217,11 +217,10 @@ def calculate_chern_number_for_square_lattice_with_wilson_loop_for_degenerate_ca
             arg = np.log(wilson_loop)/1j
             chern_number = chern_number + arg
     chern_number = chern_number/(2*math.pi)
-    import guan
-    guan.statistics_of_guan_package()
     return chern_number
 
 # 通过高效法计算贝利曲率
+@guan.function_decorator
 def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
     import numpy as np
     import cmath
@@ -265,10 +264,10 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=
                 berry_curvature_array[j0, i0, i] = berry_curvature
             j0 += 1
         i0 += 1
-    guan.statistics_of_guan_package()
     return k_array, berry_curvature_array
 
 # 通过高效法计算贝利曲率（可计算简并的情况）
+@guan.function_decorator
 def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min='default', k_max='default', precision=100, print_show=0):
     import numpy as np
     import cmath
@@ -344,11 +343,10 @@ def calculate_berry_curvature_with_efficient_method_for_degenerate_case(hamilton
             berry_curvature_array[j00, i00] = berry_curvature
             j00 += 1
         i00 += 1
-    import guan
-    guan.statistics_of_guan_package()
     return k_array, berry_curvature_array
 
 # 通过Wilson loop方法计算贝里曲率
+@guan.function_decorator
 def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
     import numpy as np
     import math
@@ -402,11 +400,10 @@ def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min='defa
             berry_curvature_array[j00, i00, :]=berry_curvature
             j00 += 1
         i00 += 1
-    import guan
-    guan.statistics_of_guan_package()
     return k_array, berry_curvature_array
 
 # 通过Wilson loop方法计算贝里曲率（可计算简并的情况）
+@guan.function_decorator
 def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min='default', k_max='default', precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0):
     import numpy as np
     import math
@@ -475,11 +472,10 @@ def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_f
             berry_curvature_array[j000, i000]=berry_curvature
             j000 += 1
         i000 += 1
-    import guan
-    guan.statistics_of_guan_package()
     return k_array, berry_curvature_array
 
 # 计算蜂窝格子的陈数（高效法）
+@guan.function_decorator
 def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, precision=300, print_show=0):
     import numpy as np
     import math
@@ -520,10 +516,10 @@ def calculate_chern_number_for_honeycomb_lattice(hamiltonian_function, a=1, prec
                     F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
                     chern_number[i] = chern_number[i] + F
     chern_number = chern_number/(2*math.pi*1j)
-    guan.statistics_of_guan_package()
     return chern_number
 
 # 计算Wilson loop
+@guan.function_decorator
 def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default', precision=100, print_show=0):
     import numpy as np
     import guan
@@ -548,5 +544,4 @@ def calculate_wilson_loop(hamiltonian_function, k_min='default', k_max='default'
         for i0 in range(precision-1):
             F = np.dot(eigenvector_array[i0+1].transpose().conj(), eigenvector_array[i0])
             wilson_loop_array[i] = np.dot(F, wilson_loop_array[i])
-    guan.statistics_of_guan_package()
     return wilson_loop_array
\ No newline at end of file