From deb8e3e3912933961f963e3f7a0532d3bbe116e5 Mon Sep 17 00:00:00 2001 From: guanjihuan Date: Mon, 23 Oct 2023 16:55:56 +0800 Subject: [PATCH] guan-0.1.1 --- API_Reference/API_Reference.py | 17 +- Source_Code/PyPI/setup.cfg | 2 +- Source_Code/PyPI/src/guan.egg-info/PKG-INFO | 2 +- Source_Code/PyPI/src/guan/__init__.py | 409 +++++++++++++++++--- 4 files changed, 372 insertions(+), 58 deletions(-) diff --git a/API_Reference/API_Reference.py b/API_Reference/API_Reference.py index 0a645f4..42031c4 100644 --- a/API_Reference/API_Reference.py +++ b/API_Reference/API_Reference.py @@ -1,4 +1,4 @@ -# API Reference shows all functions in Guan package. The current version is guan-0.0.194, updated on December 23, 2023. +# API Reference shows all functions in Guan package. The current version is guan-0.1.1 updated on December 23, 2023. import guan @@ -978,18 +978,19 @@ guan.play_selected_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show # 播放元素周期表上的单词 guan.play_element_words(random_on=0, show_translation=1, show_link=1, translation_time=2, rest_time=1) -# 获取当前函数名 -current_function_name = guan.get_current_function_name() - -# 获取调用本函数的函数名 -calling_function_name = guan.get_calling_function_name(layer=1) - # 获取Guan软件包当前模块的所有函数名 function_names = guan.get_all_function_names_in_current_module() # 统计Guan软件包中的函数数量 num_functions = guan.count_functions_in_current_module() - +# 获取当前函数名 +current_function_name = guan.get_current_function_name() + +# 获取调用本函数的函数名 +calling_function_name = guan.get_calling_function_name(layer=1) + +# Guan软件包的使用统计（不涉及到用户的个人数据） +guan.statistics_of_guan_package() diff --git a/Source_Code/PyPI/setup.cfg b/Source_Code/PyPI/setup.cfg index b63d48e..db4cb64 100644 --- a/Source_Code/PyPI/setup.cfg +++ b/Source_Code/PyPI/setup.cfg @@ -1,7 +1,7 @@ [metadata] # replace with your username: name = guan -version = 0.1.0 +version = 0.1.1 author = guanjihuan author_email = guanjihuan@163.com description = An open source python package diff --git a/Source_Code/PyPI/src/guan.egg-info/PKG-INFO b/Source_Code/PyPI/src/guan.egg-info/PKG-INFO index 7af2a9e..5a611ad 100644 --- a/Source_Code/PyPI/src/guan.egg-info/PKG-INFO +++ b/Source_Code/PyPI/src/guan.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: guan -Version: 0.1.0 +Version: 0.1.1 Summary: An open source python package Home-page: https://py.guanjihuan.com Author: guanjihuan diff --git a/Source_Code/PyPI/src/guan/__init__.py b/Source_Code/PyPI/src/guan/__init__.py index ebaa366..33d9096 100644 --- a/Source_Code/PyPI/src/guan/__init__.py +++ b/Source_Code/PyPI/src/guan/__init__.py @@ -1,6 +1,6 @@ # 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. GitHub link: https://github.com/guanjihuan/py.guanjihuan.com. -# The current version is guan-0.1.0, updated on December 23, 2023. +# The current version is guan-0.1.1, updated on December 23, 2023. # Installation: pip install --upgrade guan @@ -60,103 +60,129 @@ # 测试 def test(): print('\nSuccess in the installation of Guan package!\n') + import guan + guan.statistics_of_guan_package() # 泡利矩阵 def sigma_0(): import numpy as np + import guan + guan.statistics_of_guan_package() return np.eye(2) def sigma_x(): import numpy as np + import guan + guan.statistics_of_guan_package() return np.array([[0, 1],[1, 0]]) def sigma_y(): import numpy as np + import guan + guan.statistics_of_guan_package() return np.array([[0, -1j],[1j, 0]]) 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 def sigma_00(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_0(), guan.sigma_0()) def sigma_0x(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_0(), guan.sigma_x()) def sigma_0y(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_0(), guan.sigma_y()) def sigma_0z(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_0(), guan.sigma_z()) def sigma_x0(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_x(), guan.sigma_0()) def sigma_xx(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_x(), guan.sigma_x()) def sigma_xy(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_x(), guan.sigma_y()) def sigma_xz(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_x(), guan.sigma_z()) def sigma_y0(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_y(), guan.sigma_0()) def sigma_yx(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_y(), guan.sigma_x()) def sigma_yy(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_y(), guan.sigma_y()) def sigma_yz(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_y(), guan.sigma_z()) def sigma_z0(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_z(), guan.sigma_0()) def sigma_zx(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_z(), guan.sigma_x()) def sigma_zy(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_z(), guan.sigma_y()) def sigma_zz(): import numpy as np import guan + guan.statistics_of_guan_package() return np.kron(guan.sigma_z(), guan.sigma_z()) @@ -201,6 +227,8 @@ def one_dimensional_fourier_transform(k, unit_cell, hopping): 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） @@ -211,6 +239,8 @@ 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） @@ -222,6 +252,8 @@ 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） @@ -229,6 +261,7 @@ 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） @@ -236,6 +269,7 @@ def two_dimensional_fourier_transform_for_square_lattice_with_k1_k2(unit_cell, h 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） @@ -243,12 +277,15 @@ def three_dimensional_fourier_transform_for_cubic_lattice_with_k1_k2_k3(unit_cel 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） 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） @@ -263,6 +300,8 @@ 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） @@ -274,12 +313,16 @@ 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 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 @@ -294,6 +337,8 @@ 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 @@ -305,6 +350,8 @@ 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 @@ -363,6 +410,8 @@ 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） @@ -395,6 +444,8 @@ 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） @@ -443,6 +494,8 @@ 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 @@ -460,6 +513,8 @@ 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 @@ -473,6 +528,8 @@ 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） @@ -484,6 +541,7 @@ 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） @@ -504,6 +562,8 @@ 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 @@ -537,6 +597,8 @@ 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） @@ -560,6 +622,8 @@ 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） @@ -583,6 +647,8 @@ 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 @@ -638,12 +704,14 @@ def get_onsite_and_hopping_terms_of_half_bhz_model_for_spin_down(A=0.3645/5, B=- 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 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 @@ -660,13 +728,15 @@ def hamiltonian_of_square_lattice_in_quasi_one_dimension(k, N=10, period=0): h00[0, N-1] = 1 for i in range(N): h01[i, i] = 1 - hamiltonian = guan.one_dimensional_fourier_transform(k, unit_cell=h00, hopping=h01) + 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 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 @@ -676,6 +746,8 @@ def hamiltonian_of_ssh_model(k, v=0.6, w=1): 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 @@ -692,6 +764,8 @@ 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 @@ -707,6 +781,8 @@ 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 @@ -722,6 +798,8 @@ 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 @@ -750,7 +828,8 @@ def hamiltonian_of_graphene_with_zigzag_in_quasi_one_dimension(k, N=10, M=0, t=1 for i in range(N): 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) + 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 @@ -772,6 +851,8 @@ 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 @@ -826,6 +907,8 @@ 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 @@ -837,6 +920,8 @@ 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 @@ -855,6 +940,8 @@ 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） @@ -869,6 +956,8 @@ 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） @@ -883,6 +972,8 @@ 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 @@ -901,7 +992,9 @@ def hamiltonian_of_bbh_model(kx, ky, gamma_x=0.5, gamma_y=0.5, lambda_x=1, lambd hamiltonian[2, 0] = np.conj(hamiltonian[0, 2]) hamiltonian[3, 1] = np.conj(hamiltonian[1, 3]) hamiltonian[3, 0] = np.conj(hamiltonian[0, 3]) - hamiltonian[2, 1] = np.conj(hamiltonian[1, 2]) + 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 @@ -917,6 +1010,8 @@ 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 @@ -976,6 +1071,8 @@ def calculate_eigenvalue(hamiltonian): 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 @@ -999,6 +1096,8 @@ 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 @@ -1032,12 +1131,16 @@ 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 def calculate_eigenvector(hamiltonian): import numpy as np - eigenvalue, eigenvector = np.linalg.eigh(hamiltonian) + 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 @@ -1076,7 +1179,9 @@ def find_vector_with_the_same_gauge_with_binary_search(vector_target, vector_ref phase_2_pre = phase_2 vector_target = vector_target*cmath.exp(1j*phase) if show_phase==1: - print('Phase=', phase) + 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 @@ -1095,6 +1200,8 @@ 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） @@ -1108,6 +1215,7 @@ 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，效率不高） @@ -1137,6 +1245,8 @@ def rotation_of_degenerate_vectors(vector1, vector2, index1=None, index2=None, p break if np.abs(vector1_test[index2]) 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 @@ -2017,6 +2172,8 @@ 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 @@ -2026,6 +2183,8 @@ def calculate_scattering_matrix_and_get_information(fermi_energy, h00, h01, leng 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 @@ -2071,6 +2230,8 @@ 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 def print_or_write_scattering_matrix(fermi_energy, h00, h01, length=100, print_show=1, write_file=0, filename='a', file_format='.txt'): @@ -2081,6 +2242,8 @@ 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 def calculate_scattering_matrix_with_disorder(fermi_energy, h00, h01, length=100, disorder_intensity=2.0, disorder_concentration=1.0): import numpy as np @@ -2128,6 +2291,7 @@ 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 @@ -2144,6 +2308,7 @@ 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 @@ -2249,6 +2414,7 @@ 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） @@ -2318,6 +2484,8 @@ 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 @@ -2362,6 +2530,8 @@ 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） @@ -2425,6 +2595,8 @@ 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 @@ -2471,6 +2643,7 @@ 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） @@ -2549,6 +2722,8 @@ 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 @@ -2605,6 +2780,8 @@ 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） @@ -2676,6 +2853,8 @@ 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） @@ -2719,6 +2898,7 @@ 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 @@ -2746,6 +2926,7 @@ 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 @@ -2821,6 +3002,8 @@ 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开始画图 @@ -2838,6 +3021,8 @@ 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 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): @@ -2858,6 +3043,7 @@ 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 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): @@ -2883,6 +3069,7 @@ 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 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): @@ -2908,6 +3095,7 @@ 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 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): @@ -2936,6 +3124,7 @@ 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 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): @@ -2964,6 +3153,7 @@ 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 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): @@ -3005,6 +3195,8 @@ 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 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): @@ -3030,6 +3222,8 @@ 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 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): @@ -3055,6 +3249,8 @@ 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 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): @@ -3082,6 +3278,8 @@ 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 def combine_two_images(image_path_array, figsize=(16,8), show=0, save=1, filename='a', file_format='.jpg', dpi=300): @@ -3107,6 +3305,8 @@ 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 def combine_three_images(image_path_array, figsize=(16,5), show=0, save=1, filename='a', file_format='.jpg', dpi=300): @@ -3136,6 +3336,8 @@ 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 def combine_four_images(image_path_array, figsize=(16,16), show=0, save=1, filename='a', file_format='.jpg', dpi=300): @@ -3169,6 +3371,8 @@ 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() # 对于某个目录中的txt文件，批量读取和画图 def batch_reading_and_plotting(directory, xlabel='x', ylabel='y'): @@ -3181,6 +3385,7 @@ 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() # 制作GIF动画 def make_gif(image_path_array, filename='a', duration=0.1): @@ -3190,11 +3395,14 @@ 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() # 选取颜色 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 @@ -3259,7 +3467,6 @@ def color_matplotlib(): - # Module 11: read and write @@ -3269,12 +3476,16 @@ 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 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（每一行一组x和y） @@ -3298,6 +3509,8 @@ 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（每一行一组x和y）（支持复数形式） @@ -3321,6 +3534,8 @@ 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（第一行和列分别为横纵坐标） @@ -3350,6 +3565,8 @@ 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（第一行和列分别为横纵坐标）（支持复数形式） @@ -3379,12 +3596,16 @@ 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） 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 @@ -3393,6 +3614,8 @@ def open_file(filename='a', file_format='.txt'): f = open(filename+file_format, 'a', encoding='UTF-8') except: f = open(filename+file_format, 'w', encoding='UTF-8') + import guan + guan.statistics_of_guan_package() return f # 在文件中写入一维数据（每一行一组x和y） @@ -3400,6 +3623,8 @@ 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（每一行一组x和y）（需要输入文件） def write_one_dimensional_data_without_opening_file(x_array, y_array, f): @@ -3416,12 +3641,15 @@ 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（第一行和列分别为横纵坐标） 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（第一行和列分别为横纵坐标）（需要输入文件） def write_two_dimensional_data_without_opening_file(x_array, y_array, matrix, f): @@ -3442,12 +3670,15 @@ 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） 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）（需要输入文件） def write_two_dimensional_data_without_xy_array_and_without_opening_file(matrix, f): @@ -3455,6 +3686,8 @@ def write_two_dimensional_data_without_xy_array_and_without_opening_file(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 def print_array_with_index(array, show_index=1, index_type=0): @@ -3472,6 +3705,8 @@ 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() @@ -3558,17 +3793,23 @@ 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 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 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 def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_file='combined_file.pdf'): @@ -3584,6 +3825,8 @@ def combine_two_pdf_files(input_file_1='a.pdf', input_file_2='b.pdf', output_fil 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() # 将PDF文件转成文本 def pdf_to_text(pdf_path): @@ -3614,6 +3857,8 @@ 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 @@ -3622,6 +3867,8 @@ def get_pdf_page_number(pdf_path): 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 @@ -3635,6 +3882,8 @@ 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' @@ -3659,6 +3908,8 @@ def get_links_from_pdf(pdf_path, link_starting_form=''): links.append(u['/A']['/URI']) i0 += 1 old = u['/A']['/URI'] + import guan + guan.statistics_of_guan_package() return links # 通过Sci-Hub网站下载文献 @@ -3697,6 +3948,8 @@ 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() # 将文件目录结构写入Markdown文件 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): @@ -3800,6 +4053,8 @@ 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() # 查找文件名相同的文件 def find_repeated_file_with_same_filename(directory='./', ignored_directory_with_words=[], ignored_file_with_words=[], num=1000): @@ -3823,6 +4078,8 @@ 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 @@ -3849,6 +4106,8 @@ def count_file_in_sub_directory(directory='./', smaller_than_num=None): print(sub_dir) print(count_file) print() + import guan + guan.statistics_of_guan_package() # 产生必要的文件，例如readme.md def creat_necessary_file(directory, filename='readme', file_format='.md', content='', overwrite=None, ignored_directory_with_words=[]): @@ -3878,14 +4137,18 @@ 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 def delete_file_with_specific_name(directory, filename='readme', file_format='.md'): - import os - for root, dirs, files in os.walk(directory): + 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() # 所有文件移到根目录（慎用） def move_all_files_to_root_directory(directory): @@ -3900,6 +4163,8 @@ def move_all_files_to_root_directory(directory): os.rmdir(root) except: pass + import guan + guan.statistics_of_guan_package() # 改变当前的目录位置 def change_directory_by_replacement(current_key_word='code', new_key_word='data'): @@ -3910,12 +4175,16 @@ 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() # 生成二维码 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() # 将文本转成音频 def str_to_audio(str='hello world', filename='str', rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0): @@ -3939,6 +4208,7 @@ 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 def txt_to_audio(txt_path, rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0): @@ -3966,6 +4236,7 @@ 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 def pdf_to_audio(pdf_path, rate=125, voice=1, read=1, save=0, compress=0, bitrate='16k', print_text=0): @@ -3993,6 +4264,7 @@ 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 def compress_wav_to_mp3(wav_path, output_filename='a.mp3', bitrate='16k'): @@ -4000,6 +4272,8 @@ def compress_wav_to_mp3(wav_path, output_filename='a.mp3', bitrate='16k'): 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() @@ -4066,6 +4340,8 @@ 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 # 在一组数据中找到数值相近的数 @@ -4079,6 +4355,8 @@ 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 @@ -4093,6 +4371,8 @@ 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 # 选取一个种子生成固定的随机整数 @@ -4100,12 +4380,16 @@ 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分词 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 @@ -4143,18 +4427,22 @@ 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 # 统计运行的日期和时间，写进文件 def statistics_with_day_and_time(content='', filename='a', file_format='.txt'): - import datetime - datetime_today = str(datetime.date.today()) - datetime_time = datetime.datetime.now().strftime('%H:%M:%S') - with open(filename+file_format, 'a', encoding="utf-8") as f2: + import datetime + datetime_today = str(datetime.date.today()) + datetime_time = datetime.datetime.now().strftime('%H:%M:%S') + with open(filename+file_format, 'a', encoding="utf-8") as f2: if content == '': 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 def count_number_of_import_statements(filename, file_format='.py', num=1000): @@ -4167,12 +4455,16 @@ 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 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 # 从网页的标签中获取内容 @@ -4191,10 +4483,14 @@ 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 # 将RGB转成HEX def rgb_to_hex(rgb, pound=1): + import guan + guan.statistics_of_guan_package() if pound==0: return '%02x%02x%02x' % rgb else: @@ -4204,6 +4500,8 @@ def rgb_to_hex(rgb, pound=1): 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 @@ -4211,6 +4509,8 @@ 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 @@ -4218,12 +4518,16 @@ 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 # 获取CPU使用率 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 # 获取当前日期字符串 @@ -4232,12 +4536,16 @@ def get_date(bar=True): datetime_date = str(datetime.date.today()) if bar==False: datetime_date = datetime_date.replace('-', '') + import guan + guan.statistics_of_guan_package() return datetime_date # 获取当前时间字符串 def get_time(): import datetime datetime_time = datetime.datetime.now().strftime('%H:%M:%S') + import guan + guan.statistics_of_guan_package() return datetime_time # 获取本月的所有日期 @@ -4257,6 +4565,8 @@ 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 @@ -4269,6 +4579,8 @@ 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 @@ -4286,6 +4598,8 @@ 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 @@ -4307,6 +4621,7 @@ 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 @@ -4328,6 +4643,7 @@ 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 @@ -4337,6 +4653,8 @@ def all_stocks(): 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 @@ -4344,6 +4662,7 @@ 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 @@ -4353,6 +4672,7 @@ def find_stock_name_from_symbol(symbol='000002'): 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 @@ -4365,6 +4685,8 @@ 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 # 播放学术单词 @@ -4426,6 +4748,8 @@ 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 def play_selected_academic_words(reverse=0, random_on=0, bre_or_ame='ame', show_link=1, rest_time=3): @@ -4482,6 +4806,8 @@ 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 def play_element_words(random_on=0, show_translation=1, show_link=1, translation_time=2, rest_time=1): @@ -4537,12 +4863,32 @@ 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() +# 获取Guan软件包当前模块的所有函数名 +def get_all_function_names_in_current_module(): + import inspect + current_module = inspect.getmodule(inspect.currentframe()) + function_names = [name for name, obj in inspect.getmembers(current_module) if inspect.isfunction(obj)] + import guan + guan.statistics_of_guan_package() + return function_names + +# 统计Guan软件包中的函数数量 +def count_functions_in_current_module(): + import guan + function_names = guan.get_all_function_names_in_current_module() + num_functions = len(function_names) + guan.statistics_of_guan_package() + return num_functions # 获取当前函数名 def get_current_function_name(): import inspect current_function_name = inspect.currentframe().f_code.co_name + import guan + guan.statistics_of_guan_package() return current_function_name # 获取调用本函数的函数名 @@ -4552,20 +4898,6 @@ def get_calling_function_name(layer=1): calling_function_name = caller.function return calling_function_name -# 获取Guan软件包当前模块的所有函数名 -def get_all_function_names_in_current_module(): - import inspect - current_module = inspect.getmodule(inspect.currentframe()) - function_names = [name for name, obj in inspect.getmembers(current_module) if inspect.isfunction(obj)] - return function_names - -# 统计Guan软件包中的函数数量 -def count_functions_in_current_module(): - import guan - function_names = guan.get_all_function_names_in_current_module() - num_functions = len(function_names) - return num_functions - # Guan软件包的使用统计（不涉及到用户的个人数据） def statistics_of_guan_package(): try: @@ -4580,23 +4912,4 @@ def statistics_of_guan_package(): client_socket.send(send_message.encode()) client_socket.close() except: - pass - -# 装饰器函数，用于在每个函数调用前调用 guan.statistics_of_guan_package() -def call_common_function(func): - def wrapper(*args, **kwargs): - import guan - guan.statistics_of_guan_package() - return func(*args, **kwargs) - return wrapper - -# 动态应用装饰器到每个函数，用于统计 -try: - function_names = get_all_function_names_in_current_module() - for func_name in function_names: - func = locals().get(func_name) - if callable(func): - decorated_func = call_common_function(func) - locals()[func_name] = decorated_func -except: - pass \ No newline at end of file + pass \ No newline at end of file