diff --git a/academic_codes/2021.01.07_Hofstadter_butterfly_in_square_lattice/Hofstadter_butterfly_in_square_lattice.py b/academic_codes/Landau_levels/2021.01.07_Hofstadter_butterfly_in_square_lattice/Hofstadter_butterfly_in_square_lattice.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2021.01.07_Hofstadter_butterfly_in_square_lattice/Hofstadter_butterfly_in_square_lattice.py
rename to academic_codes/Landau_levels/2021.01.07_Hofstadter_butterfly_in_square_lattice/Hofstadter_butterfly_in_square_lattice.py
index 8636517..1fba687
--- a/academic_codes/2021.01.07_Hofstadter_butterfly_in_square_lattice/Hofstadter_butterfly_in_square_lattice.py
+++ b/academic_codes/Landau_levels/2021.01.07_Hofstadter_butterfly_in_square_lattice/Hofstadter_butterfly_in_square_lattice.py
@@ -1,50 +1,50 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8491
-"""
-
-import numpy as np
-import cmath
-import matplotlib.pyplot as plt
-
-
-def main():
- for n in np.arange(1, 11):
- print('n=', n)
- width = n
- length = n
- B_array = np.arange(0, 1, 0.001)
- eigenvalue_all = np.zeros((B_array.shape[0], width*length))
- i0 = 0
- for B in B_array:
- # print(B)
- h = hamiltonian(width, length, B)
- eigenvalue, eigenvector = np.linalg.eig(h)
- eigenvalue_all[i0, :] = np.real(eigenvalue)
- i0 += 1
- plt.plot(B_array, eigenvalue_all, '.r', markersize=0.5)
- plt.title('width=length='+str(n))
- plt.xlabel('B*a^2/phi_0')
- plt.ylabel('E')
- plt.savefig('width=length='+str(n)+'.jpg', dpi=300)
- plt.close('all') # 关闭所有plt，防止循环画图时占用内存
- # plt.show()
-
-
-def hamiltonian(width, length, B): # 方格子哈密顿量
- h = np.zeros((width*length, width*length), dtype=complex)
- # y方向的跃迁
- for x in range(length):
- for y in range(width-1):
- h[x*width+y, x*width+y+1] = 1
- h[x*width+y+1, x*width+y] = 1
- # x方向的跃迁
- for x in range(length-1):
- for y in range(width):
- h[x*width+y, (x+1)*width+y] = 1*cmath.exp(-2*np.pi*1j*B*y)
- h[(x+1)*width+y, x*width+y] = 1*cmath.exp(2*np.pi*1j*B*y)
- return h
-
-
-if __name__ == "__main__":
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8491
+"""
+
+import numpy as np
+import cmath
+import matplotlib.pyplot as plt
+
+
+def main():
+ for n in np.arange(1, 11):
+ print('n=', n)
+ width = n
+ length = n
+ B_array = np.arange(0, 1, 0.001)
+ eigenvalue_all = np.zeros((B_array.shape[0], width*length))
+ i0 = 0
+ for B in B_array:
+ # print(B)
+ h = hamiltonian(width, length, B)
+ eigenvalue, eigenvector = np.linalg.eig(h)
+ eigenvalue_all[i0, :] = np.real(eigenvalue)
+ i0 += 1
+ plt.plot(B_array, eigenvalue_all, '.r', markersize=0.5)
+ plt.title('width=length='+str(n))
+ plt.xlabel('B*a^2/phi_0')
+ plt.ylabel('E')
+ plt.savefig('width=length='+str(n)+'.jpg', dpi=300)
+ plt.close('all') # 关闭所有plt，防止循环画图时占用内存
+ # plt.show()
+
+
+def hamiltonian(width, length, B): # 方格子哈密顿量
+ h = np.zeros((width*length, width*length), dtype=complex)
+ # y方向的跃迁
+ for x in range(length):
+ for y in range(width-1):
+ h[x*width+y, x*width+y+1] = 1
+ h[x*width+y+1, x*width+y] = 1
+ # x方向的跃迁
+ for x in range(length-1):
+ for y in range(width):
+ h[x*width+y, (x+1)*width+y] = 1*cmath.exp(-2*np.pi*1j*B*y)
+ h[(x+1)*width+y, x*width+y] = 1*cmath.exp(2*np.pi*1j*B*y)
+ return h
+
+
+if __name__ == "__main__":
+ main()
diff --git a/academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py b/academic_codes/Landau_levels/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py
similarity index 100%
rename from academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py
rename to academic_codes/Landau_levels/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py
diff --git a/academic_codes/2022.08.03_Landau_levels_of_honeycomb_lattice/Landau_levels_of_honeycomb_lattice.py b/academic_codes/Landau_levels/2022.08.03_Landau_levels_of_honeycomb_lattice/Landau_levels_of_honeycomb_lattice.py
similarity index 100%
rename from academic_codes/2022.08.03_Landau_levels_of_honeycomb_lattice/Landau_levels_of_honeycomb_lattice.py
rename to academic_codes/Landau_levels/2022.08.03_Landau_levels_of_honeycomb_lattice/Landau_levels_of_honeycomb_lattice.py
diff --git a/academic_codes/2019.11.02_numerically_verify_the_relation_between_eigenstates_and_DOS/numerically_verify_the_relation_between_eigenstates_and_DOS.py b/academic_codes/density_of_states/2019.11.02_numerically_verify_the_relation_between_eigenstates_and_DOS/numerically_verify_the_relation_between_eigenstates_and_DOS.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.02_numerically_verify_the_relation_between_eigenstates_and_DOS/numerically_verify_the_relation_between_eigenstates_and_DOS.py
rename to academic_codes/density_of_states/2019.11.02_numerically_verify_the_relation_between_eigenstates_and_DOS/numerically_verify_the_relation_between_eigenstates_and_DOS.py
index fc48384..1e13839
--- a/academic_codes/2019.11.02_numerically_verify_the_relation_between_eigenstates_and_DOS/numerically_verify_the_relation_between_eigenstates_and_DOS.py
+++ b/academic_codes/density_of_states/2019.11.02_numerically_verify_the_relation_between_eigenstates_and_DOS/numerically_verify_the_relation_between_eigenstates_and_DOS.py
@@ -1,43 +1,43 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/962
-"""
-
-import numpy as np
-
-
-def hamiltonian(width=2, length=4): # 有一定宽度和长度的方格子
- h00 = np.zeros((width*length, width*length))
- for i0 in range(length):
- for j0 in range(width-1):
- h00[i0*width+j0, i0*width+j0+1] = 1
- h00[i0*width+j0+1, i0*width+j0] = 1
- for i0 in range(length-1):
- for j0 in range(width):
- h00[i0*width+j0, (i0+1)*width+j0] = 1
- h00[(i0+1)*width+j0, i0*width+j0] = 1
- return h00
-
-
-def main():
- h0 = hamiltonian()
- dim = h0.shape[0]
- n = 4 # 选取第n个能级
- eigenvalue, eigenvector = np.linalg.eig(h0) # 本征值、本征矢
- # print(h0)
- # print('哈密顿量的维度：', dim) # 哈密顿量的维度
- # print('本征矢的维度：', eigenvector.shape) # 本征矢的维度
- # print('能级（未排序）：', eigenvalue) # 输出本征值。因为体系是受限的，所以是离散的能级
- # print('选取第', n, '个能级，为', eigenvalue[n-1]) # 从1开始算，查看第n个能级是什么（这里本征值未排序）
- # print('第', n, '个能级对应的波函数：', eigenvector[:, n-1]) # 查看第n个能级对应的波函数
- print('\n波函数模的平方：\n', np.square(np.abs(eigenvector[:, n-1]))) # 查看第n个能级对应的波函数模的平方
- green = np.linalg.inv((eigenvalue[n-1]+1e-15j)*np.eye(dim)-h0) # 第n个能级对应的格林函数
- total = np.trace(np.imag(green)) # 求该能级格林函数的迹，对应的是总态密度（忽略符号和系数）
- print('归一化后的态密度分布：')
- for i in range(dim):
- print(np.imag(green)[i, i]/total) # 第n个能级单位化后的态密度分布
- print('观察以上两个分布的数值情况，可以发现两者完全相同。')
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/962
+"""
+
+import numpy as np
+
+
+def hamiltonian(width=2, length=4): # 有一定宽度和长度的方格子
+ h00 = np.zeros((width*length, width*length))
+ for i0 in range(length):
+ for j0 in range(width-1):
+ h00[i0*width+j0, i0*width+j0+1] = 1
+ h00[i0*width+j0+1, i0*width+j0] = 1
+ for i0 in range(length-1):
+ for j0 in range(width):
+ h00[i0*width+j0, (i0+1)*width+j0] = 1
+ h00[(i0+1)*width+j0, i0*width+j0] = 1
+ return h00
+
+
+def main():
+ h0 = hamiltonian()
+ dim = h0.shape[0]
+ n = 4 # 选取第n个能级
+ eigenvalue, eigenvector = np.linalg.eig(h0) # 本征值、本征矢
+ # print(h0)
+ # print('哈密顿量的维度：', dim) # 哈密顿量的维度
+ # print('本征矢的维度：', eigenvector.shape) # 本征矢的维度
+ # print('能级（未排序）：', eigenvalue) # 输出本征值。因为体系是受限的，所以是离散的能级
+ # print('选取第', n, '个能级，为', eigenvalue[n-1]) # 从1开始算，查看第n个能级是什么（这里本征值未排序）
+ # print('第', n, '个能级对应的波函数：', eigenvector[:, n-1]) # 查看第n个能级对应的波函数
+ print('\n波函数模的平方：\n', np.square(np.abs(eigenvector[:, n-1]))) # 查看第n个能级对应的波函数模的平方
+ green = np.linalg.inv((eigenvalue[n-1]+1e-15j)*np.eye(dim)-h0) # 第n个能级对应的格林函数
+ total = np.trace(np.imag(green)) # 求该能级格林函数的迹，对应的是总态密度（忽略符号和系数）
+ print('归一化后的态密度分布：')
+ for i in range(dim):
+ print(np.imag(green)[i, i]/total) # 第n个能级单位化后的态密度分布
+ print('观察以上两个分布的数值情况，可以发现两者完全相同。')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2019.12.30_calculation_of_spectral_function_and_QPI/calculation of spectral function and QPI.f90 b/academic_codes/density_of_states/2019.12.30_calculation_of_spectral_function_and_QPI/calculation of spectral function and QPI.f90
similarity index 100%
rename from academic_codes/2019.12.30_calculation_of_spectral_function_and_QPI/calculation of spectral function and QPI.f90
rename to academic_codes/density_of_states/2019.12.30_calculation_of_spectral_function_and_QPI/calculation of spectral function and QPI.f90
diff --git a/academic_codes/2019.12.30_calculation_of_spectral_function_and_QPI/calculation of spectral function and QPI.py b/academic_codes/density_of_states/2019.12.30_calculation_of_spectral_function_and_QPI/calculation of spectral function and QPI.py
similarity index 100%
rename from academic_codes/2019.12.30_calculation_of_spectral_function_and_QPI/calculation of spectral function and QPI.py
rename to academic_codes/density_of_states/2019.12.30_calculation_of_spectral_function_and_QPI/calculation of spectral function and QPI.py
diff --git a/academic_codes/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_graphene.py b/academic_codes/density_of_states/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_graphene.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_graphene.py
rename to academic_codes/density_of_states/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_graphene.py
index 6686dcc..83f8627
--- a/academic_codes/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_graphene.py
+++ b/academic_codes/density_of_states/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_graphene.py
@@ -1,56 +1,56 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4622
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
-plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
-
-
-def hamiltonian(width=8, length=8): # 石墨烯格子的哈密顿量。这里width要求为4的倍数
- h = np.zeros((width*length, width*length))
- # y方向的跃迁
- for x in range(length):
- for y in range(width-1):
- h[x*width+y, x*width+y+1] = 1
- h[x*width+y+1, x*width+y] = 1
-
- # x方向的跃迁
- for x in range(length-1):
- for y in range(width):
- if np.mod(y, 4)==0:
- h[x*width+y+1, (x+1)*width+y] = 1
- h[(x+1)*width+y, x*width+y+1] = 1
-
- h[x*width+y+2, (x+1)*width+y+3] = 1
- h[(x+1)*width+y+3, x*width+y+2] = 1
-
- return h
-
-
-def main():
- plot_precision = 0.01 # 画图的精度
- Fermi_energy_array = np.arange(-5, 5, plot_precision) # 计算中取的费米能Fermi_energy组成的数组
- dim_energy = Fermi_energy_array.shape[0] # 需要计算的费米能的个数
- total_DOS_array = np.zeros((dim_energy)) # 计算结果（总态密度total_DOS）放入该数组中
- h = hamiltonian() # 体系的哈密顿量
- dim = h.shape[0] # 哈密顿量的维度
- i0 = 0
- for Fermi_energy in Fermi_energy_array:
- print(Fermi_energy) # 查看计算的进展情况
- green = np.linalg.inv((Fermi_energy+0.1j)*np.eye(dim)-h) # 体系的格林函数
- total_DOS = -np.trace(np.imag(green))/pi # 通过格林函数求得总态密度
- total_DOS_array[i0] = total_DOS # 记录每个Fermi_energy对应的总态密度
- i0 += 1
- sum_up = np.sum(total_DOS_array)*plot_precision # 用于图像归一化
- plt.plot(Fermi_energy_array, total_DOS_array/sum_up, '-') # 画DOS(E)图像
- plt.xlabel('费米能')
- plt.ylabel('总态密度')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4622
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
+plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
+
+
+def hamiltonian(width=8, length=8): # 石墨烯格子的哈密顿量。这里width要求为4的倍数
+ h = np.zeros((width*length, width*length))
+ # y方向的跃迁
+ for x in range(length):
+ for y in range(width-1):
+ h[x*width+y, x*width+y+1] = 1
+ h[x*width+y+1, x*width+y] = 1
+
+ # x方向的跃迁
+ for x in range(length-1):
+ for y in range(width):
+ if np.mod(y, 4)==0:
+ h[x*width+y+1, (x+1)*width+y] = 1
+ h[(x+1)*width+y, x*width+y+1] = 1
+
+ h[x*width+y+2, (x+1)*width+y+3] = 1
+ h[(x+1)*width+y+3, x*width+y+2] = 1
+
+ return h
+
+
+def main():
+ plot_precision = 0.01 # 画图的精度
+ Fermi_energy_array = np.arange(-5, 5, plot_precision) # 计算中取的费米能Fermi_energy组成的数组
+ dim_energy = Fermi_energy_array.shape[0] # 需要计算的费米能的个数
+ total_DOS_array = np.zeros((dim_energy)) # 计算结果（总态密度total_DOS）放入该数组中
+ h = hamiltonian() # 体系的哈密顿量
+ dim = h.shape[0] # 哈密顿量的维度
+ i0 = 0
+ for Fermi_energy in Fermi_energy_array:
+ print(Fermi_energy) # 查看计算的进展情况
+ green = np.linalg.inv((Fermi_energy+0.1j)*np.eye(dim)-h) # 体系的格林函数
+ total_DOS = -np.trace(np.imag(green))/pi # 通过格林函数求得总态密度
+ total_DOS_array[i0] = total_DOS # 记录每个Fermi_energy对应的总态密度
+ i0 += 1
+ sum_up = np.sum(total_DOS_array)*plot_precision # 用于图像归一化
+ plt.plot(Fermi_energy_array, total_DOS_array/sum_up, '-') # 画DOS(E)图像
+ plt.xlabel('费米能')
+ plt.ylabel('总态密度')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_square_lattice.py b/academic_codes/density_of_states/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_square_lattice.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_square_lattice.py
rename to academic_codes/density_of_states/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_square_lattice.py
index 911a50b..03a5517
--- a/academic_codes/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_square_lattice.py
+++ b/academic_codes/density_of_states/2020.06.16_total_DOS_as_a_function_of_Fermi_energy_in_square_lattice_and_graphene/total_DOS_as_a_function_of_Fermi_energy_in_square_lattice.py
@@ -1,52 +1,52 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4622
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
-plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
-
-
-def hamiltonian(width=10, length=10): # 方格子哈密顿量
- h = np.zeros((width*length, width*length))
- # y方向的跃迁
- for x in range(length):
- for y in range(width-1):
- h[x*width+y, x*width+y+1] = 1
- h[x*width+y+1, x*width+y] = 1
-
- # x方向的跃迁
- for x in range(length-1):
- for y in range(width):
- h[x*width+y, (x+1)*width+y] = 1
- h[(x+1)*width+y, x*width+y] = 1
-
- return h
-
-
-def main():
- plot_precision = 0.01 # 画图的精度
- Fermi_energy_array = np.arange(-5, 5, plot_precision) # 计算中取的费米能Fermi_energy组成的数组
- dim_energy = Fermi_energy_array.shape[0] # 需要计算的费米能的个数
- total_DOS_array = np.zeros((dim_energy)) # 计算结果（总态密度total_DOS）放入该数组中
- h = hamiltonian() # 体系的哈密顿量
- dim = h.shape[0] # 哈密顿量的维度
- i0 = 0
- for Fermi_energy in Fermi_energy_array:
- print(Fermi_energy) # 查看计算的进展情况
- green = np.linalg.inv((Fermi_energy+0.1j)*np.eye(dim)-h) # 体系的格林函数
- total_DOS = -np.trace(np.imag(green))/pi # 通过格林函数求得总态密度
- total_DOS_array[i0] = total_DOS # 记录每个Fermi_energy对应的总态密度
- i0 += 1
- sum_up = np.sum(total_DOS_array)*plot_precision # 用于图像归一化
- plt.plot(Fermi_energy_array, total_DOS_array/sum_up, '-') # 画DOS(E)图像
- plt.xlabel('费米能')
- plt.ylabel('总态密度')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4622
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签
+plt.rcParams['axes.unicode_minus']=False #用来正常显示负号
+
+
+def hamiltonian(width=10, length=10): # 方格子哈密顿量
+ h = np.zeros((width*length, width*length))
+ # y方向的跃迁
+ for x in range(length):
+ for y in range(width-1):
+ h[x*width+y, x*width+y+1] = 1
+ h[x*width+y+1, x*width+y] = 1
+
+ # x方向的跃迁
+ for x in range(length-1):
+ for y in range(width):
+ h[x*width+y, (x+1)*width+y] = 1
+ h[(x+1)*width+y, x*width+y] = 1
+
+ return h
+
+
+def main():
+ plot_precision = 0.01 # 画图的精度
+ Fermi_energy_array = np.arange(-5, 5, plot_precision) # 计算中取的费米能Fermi_energy组成的数组
+ dim_energy = Fermi_energy_array.shape[0] # 需要计算的费米能的个数
+ total_DOS_array = np.zeros((dim_energy)) # 计算结果（总态密度total_DOS）放入该数组中
+ h = hamiltonian() # 体系的哈密顿量
+ dim = h.shape[0] # 哈密顿量的维度
+ i0 = 0
+ for Fermi_energy in Fermi_energy_array:
+ print(Fermi_energy) # 查看计算的进展情况
+ green = np.linalg.inv((Fermi_energy+0.1j)*np.eye(dim)-h) # 体系的格林函数
+ total_DOS = -np.trace(np.imag(green))/pi # 通过格林函数求得总态密度
+ total_DOS_array[i0] = total_DOS # 记录每个Fermi_energy对应的总态密度
+ i0 += 1
+ sum_up = np.sum(total_DOS_array)*plot_precision # 用于图像归一化
+ plt.plot(Fermi_energy_array, total_DOS_array/sum_up, '-') # 画DOS(E)图像
+ plt.xlabel('费米能')
+ plt.ylabel('总态密度')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.06.25_numerically_verify_Green_functions_in_Dyson_equations/numerically_verify_Green_functions_in_Dyson_equations.py b/academic_codes/density_of_states/2020.06.25_numerically_verify_Green_functions_in_Dyson_equations/numerically_verify_Green_functions_in_Dyson_equations.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.06.25_numerically_verify_Green_functions_in_Dyson_equations/numerically_verify_Green_functions_in_Dyson_equations.py
rename to academic_codes/density_of_states/2020.06.25_numerically_verify_Green_functions_in_Dyson_equations/numerically_verify_Green_functions_in_Dyson_equations.py
index 4121853..78bdf7d
--- a/academic_codes/2020.06.25_numerically_verify_Green_functions_in_Dyson_equations/numerically_verify_Green_functions_in_Dyson_equations.py
+++ b/academic_codes/density_of_states/2020.06.25_numerically_verify_Green_functions_in_Dyson_equations/numerically_verify_Green_functions_in_Dyson_equations.py
@@ -1,84 +1,84 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4396
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-import copy
-import time
-
-
-def matrix_00(width): # 一个切片（slide)内的哈密顿量
- h00 = np.zeros((width, width))
- for width0 in range(width-1):
- h00[width0, width0+1] = 1
- h00[width0+1, width0] = 1
- return h00
-
-
-def matrix_01(width): # 切片之间的跃迁项（hopping）
- h01 = np.identity(width)
- return h01
-
-
-def matrix_whole(width, length): # 方格子整体的哈密顿量，宽度为width，长度为length
- hamiltonian = np.zeros((width*length, width*length))
- for x in range(length):
- for y in range(width-1):
- hamiltonian[x*width+y, x*width+y+1] = 1
- hamiltonian[x*width+y+1, x*width+y] = 1
- for x in range(length-1):
- for y in range(width):
- hamiltonian[x*width+y, (x+1)*width+y] = 1
- hamiltonian[(x+1)*width+y, x*width+y] = 1
- return hamiltonian
-
-
-def main():
- width =4 # 方格子的宽度
- length = 200 # 方格子的长度
- h00 = matrix_00(width) # 一个切片（slide)内的哈密顿量
- h01 = matrix_01(width) # 切片之间的跃迁项（hopping）
- hamiltonian = matrix_whole(width, length) # 方格子整体的哈密顿量，宽度为width，长度为length
- fermi_energy = 0.1 # 费米能取为0.1为例。按理来说计算格林函数时，这里需要加上一个无穷小的虚数，但Python中好像不加也不会有什么问题。
-
- start_1= time.perf_counter()
- green = General_way(fermi_energy, hamiltonian) # 利用通常直接求逆的方法得到整体的格林函数green
- end_1 = time.perf_counter()
- start_2= time.perf_counter()
- green_0n_n = Dyson_way(fermi_energy, h00, h01, length) # 利用Dyson方程得到的格林函数green_0n
- end_2 = time.perf_counter()
-
- # print(green)
- print('\n整体格林函数中的一个分块矩阵green_0n：\n', green[0:width, (length-1)*width+0:(length-1)*width+width]) # a:b代表 a <= x < b，左闭右开
- print('Dyson方程得到的格林函数green_0n：\n', green_0n_n)
- print('观察以上两个矩阵，可以直接看出两个矩阵完全相同。\n')
-
- print('General_way执行时间=', end_1-start_1)
- print('Dyson_way执行时间=', end_2-start_2)
-
-
-def General_way(fermi_energy, hamiltonian):
- dim_hamiltonian = hamiltonian.shape[0]
- green = np.linalg.inv((fermi_energy)*np.eye(dim_hamiltonian)-hamiltonian)
- return green
-
-
-def Dyson_way(fermi_energy, h00, h01, length):
- dim = h00.shape[0]
- for ix in range(length):
- if ix == 0:
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00) # 如果有左电极，还需要减去左电极的自能left_self_energy
- green_0n_n = copy.deepcopy(green_nn_n) # 如果直接用等于，两个变量会指向相同的id，改变一个值，另外一个值可能会发生改变，容易出错，所以要用上这个COPY
- elif ix != length-1:
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
- green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
- else: # 这里和（elif ix != length-1）中的内容完全一样，但如果有右电极，这里是还需要减去右电极的自能right_self_energy
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
- green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
- return green_0n_n
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4396
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import copy
+import time
+
+
+def matrix_00(width): # 一个切片（slide)内的哈密顿量
+ h00 = np.zeros((width, width))
+ for width0 in range(width-1):
+ h00[width0, width0+1] = 1
+ h00[width0+1, width0] = 1
+ return h00
+
+
+def matrix_01(width): # 切片之间的跃迁项（hopping）
+ h01 = np.identity(width)
+ return h01
+
+
+def matrix_whole(width, length): # 方格子整体的哈密顿量，宽度为width，长度为length
+ hamiltonian = np.zeros((width*length, width*length))
+ for x in range(length):
+ for y in range(width-1):
+ hamiltonian[x*width+y, x*width+y+1] = 1
+ hamiltonian[x*width+y+1, x*width+y] = 1
+ for x in range(length-1):
+ for y in range(width):
+ hamiltonian[x*width+y, (x+1)*width+y] = 1
+ hamiltonian[(x+1)*width+y, x*width+y] = 1
+ return hamiltonian
+
+
+def main():
+ width =4 # 方格子的宽度
+ length = 200 # 方格子的长度
+ h00 = matrix_00(width) # 一个切片（slide)内的哈密顿量
+ h01 = matrix_01(width) # 切片之间的跃迁项（hopping）
+ hamiltonian = matrix_whole(width, length) # 方格子整体的哈密顿量，宽度为width，长度为length
+ fermi_energy = 0.1 # 费米能取为0.1为例。按理来说计算格林函数时，这里需要加上一个无穷小的虚数，但Python中好像不加也不会有什么问题。
+
+ start_1= time.perf_counter()
+ green = General_way(fermi_energy, hamiltonian) # 利用通常直接求逆的方法得到整体的格林函数green
+ end_1 = time.perf_counter()
+ start_2= time.perf_counter()
+ green_0n_n = Dyson_way(fermi_energy, h00, h01, length) # 利用Dyson方程得到的格林函数green_0n
+ end_2 = time.perf_counter()
+
+ # print(green)
+ print('\n整体格林函数中的一个分块矩阵green_0n：\n', green[0:width, (length-1)*width+0:(length-1)*width+width]) # a:b代表 a <= x < b，左闭右开
+ print('Dyson方程得到的格林函数green_0n：\n', green_0n_n)
+ print('观察以上两个矩阵，可以直接看出两个矩阵完全相同。\n')
+
+ print('General_way执行时间=', end_1-start_1)
+ print('Dyson_way执行时间=', end_2-start_2)
+
+
+def General_way(fermi_energy, hamiltonian):
+ dim_hamiltonian = hamiltonian.shape[0]
+ green = np.linalg.inv((fermi_energy)*np.eye(dim_hamiltonian)-hamiltonian)
+ return green
+
+
+def Dyson_way(fermi_energy, h00, h01, length):
+ dim = h00.shape[0]
+ for ix in range(length):
+ if ix == 0:
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00) # 如果有左电极，还需要减去左电极的自能left_self_energy
+ green_0n_n = copy.deepcopy(green_nn_n) # 如果直接用等于，两个变量会指向相同的id，改变一个值，另外一个值可能会发生改变，容易出错，所以要用上这个COPY
+ elif ix != length-1:
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
+ green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
+ else: # 这里和（elif ix != length-1）中的内容完全一样，但如果有右电极，这里是还需要减去右电极的自能right_self_energy
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
+ green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
+ return green_0n_n
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice.py b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice.py
rename to academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice.py
index dfcdc87..6ea382e
--- a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice.py
+++ b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice.py
@@ -1,102 +1,102 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-
-
-def matrix_00(width, length):
- h00 = np.zeros((width*length, width*length))
- for x in range(length):
- for y in range(width-1):
- h00[x*width+y, x*width+y+1] = 1
- h00[x*width+y+1, x*width+y] = 1
- for x in range(length-1):
- for y in range(width):
- h00[x*width+y, (x+1)*width+y] = 1
- h00[(x+1)*width+y, x*width+y] = 1
- return h00
-
-
-def matrix_01(width, length):
- h01 = np.identity(width*length)
- return h01
-
-
-def main():
- height = 2 # z
- width = 3 # y
- length = 4 # x
- eta = 1e-2
- E = 0
- h00 = matrix_00(width, length)
- h01 = matrix_01(width, length)
- G_ii_n_array = G_ii_n_with_Dyson_equation(width, length, height, E, eta, h00, h01)
- for i0 in range(height):
- print('z=', i0+1, ':')
- for j0 in range(width):
- print(' y=', j0+1, ':')
- for k0 in range(length):
- print(' x=', k0+1, ' ', -np.imag(G_ii_n_array[i0, k0*width+j0, k0*width+j0])/pi) # 态密度
-
-
-def G_ii_n_with_Dyson_equation(width, length, height, E, eta, h00, h01):
- dim = length*width
- G_ii_n_array = np.zeros((height, dim, dim), dtype=complex)
- G_11_1 = np.linalg.inv((E+eta*1j)*np.identity(dim)-h00)
- for i in range(height): # i为格林函数的右下指标
- # 初始化开始
- G_nn_n_minus = G_11_1
- G_in_n_minus = G_11_1
- G_ni_n_minus = G_11_1
- G_ii_n_minus = G_11_1
- for i0 in range(i):
- G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
- G_nn_n_minus = G_nn_n
- if i!=0:
- G_in_n_minus = G_nn_n
- G_ni_n_minus = G_nn_n
- G_ii_n_minus = G_nn_n
- # 初始化结束
- for j0 in range(height-1-i): # j0为格林函数的右上指标，表示当前体系大小，即G^{(j0)}
- G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
- G_nn_n_minus = G_nn_n
-
- G_ii_n = Green_ii_n(G_ii_n_minus, G_in_n_minus, h01, G_nn_n, G_ni_n_minus) # 需要求的对角分块矩阵
- G_ii_n_minus = G_ii_n
-
- G_in_n = Green_in_n(G_in_n_minus, h01, G_nn_n)
- G_in_n_minus = G_in_n
-
- G_ni_n = Green_ni_n(G_nn_n, h01, G_ni_n_minus)
- G_ni_n_minus = G_ni_n
- G_ii_n_array[i, :, :] = G_ii_n_minus
- return G_ii_n_array
-
-
-def Green_nn_n(E, eta, H00, V, G_nn_n_minus): # n>=2
- dim = H00.shape[0]
- G_nn_n = np.linalg.inv((E+eta*1j)*np.identity(dim)-H00-np.dot(np.dot(V.transpose().conj(), G_nn_n_minus), V))
- return G_nn_n
-
-
-def Green_in_n(G_in_n_minus, V, G_nn_n): # n>=2
- G_in_n = np.dot(np.dot(G_in_n_minus, V), G_nn_n)
- return G_in_n
-
-
-def Green_ni_n(G_nn_n, V, G_ni_n_minus): # n>=2
- G_ni_n = np.dot(np.dot(G_nn_n, V.transpose().conj()), G_ni_n_minus)
- return G_ni_n
-
-
-def Green_ii_n(G_ii_n_minus, G_in_n_minus, V, G_nn_n, G_ni_n_minus): # n>=i
- G_ii_n = G_ii_n_minus+np.dot(np.dot(np.dot(np.dot(G_in_n_minus, V), G_nn_n), V.transpose().conj()),G_ni_n_minus)
- return G_ii_n
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+
+
+def matrix_00(width, length):
+ h00 = np.zeros((width*length, width*length))
+ for x in range(length):
+ for y in range(width-1):
+ h00[x*width+y, x*width+y+1] = 1
+ h00[x*width+y+1, x*width+y] = 1
+ for x in range(length-1):
+ for y in range(width):
+ h00[x*width+y, (x+1)*width+y] = 1
+ h00[(x+1)*width+y, x*width+y] = 1
+ return h00
+
+
+def matrix_01(width, length):
+ h01 = np.identity(width*length)
+ return h01
+
+
+def main():
+ height = 2 # z
+ width = 3 # y
+ length = 4 # x
+ eta = 1e-2
+ E = 0
+ h00 = matrix_00(width, length)
+ h01 = matrix_01(width, length)
+ G_ii_n_array = G_ii_n_with_Dyson_equation(width, length, height, E, eta, h00, h01)
+ for i0 in range(height):
+ print('z=', i0+1, ':')
+ for j0 in range(width):
+ print(' y=', j0+1, ':')
+ for k0 in range(length):
+ print(' x=', k0+1, ' ', -np.imag(G_ii_n_array[i0, k0*width+j0, k0*width+j0])/pi) # 态密度
+
+
+def G_ii_n_with_Dyson_equation(width, length, height, E, eta, h00, h01):
+ dim = length*width
+ G_ii_n_array = np.zeros((height, dim, dim), dtype=complex)
+ G_11_1 = np.linalg.inv((E+eta*1j)*np.identity(dim)-h00)
+ for i in range(height): # i为格林函数的右下指标
+ # 初始化开始
+ G_nn_n_minus = G_11_1
+ G_in_n_minus = G_11_1
+ G_ni_n_minus = G_11_1
+ G_ii_n_minus = G_11_1
+ for i0 in range(i):
+ G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
+ G_nn_n_minus = G_nn_n
+ if i!=0:
+ G_in_n_minus = G_nn_n
+ G_ni_n_minus = G_nn_n
+ G_ii_n_minus = G_nn_n
+ # 初始化结束
+ for j0 in range(height-1-i): # j0为格林函数的右上指标，表示当前体系大小，即G^{(j0)}
+ G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
+ G_nn_n_minus = G_nn_n
+
+ G_ii_n = Green_ii_n(G_ii_n_minus, G_in_n_minus, h01, G_nn_n, G_ni_n_minus) # 需要求的对角分块矩阵
+ G_ii_n_minus = G_ii_n
+
+ G_in_n = Green_in_n(G_in_n_minus, h01, G_nn_n)
+ G_in_n_minus = G_in_n
+
+ G_ni_n = Green_ni_n(G_nn_n, h01, G_ni_n_minus)
+ G_ni_n_minus = G_ni_n
+ G_ii_n_array[i, :, :] = G_ii_n_minus
+ return G_ii_n_array
+
+
+def Green_nn_n(E, eta, H00, V, G_nn_n_minus): # n>=2
+ dim = H00.shape[0]
+ G_nn_n = np.linalg.inv((E+eta*1j)*np.identity(dim)-H00-np.dot(np.dot(V.transpose().conj(), G_nn_n_minus), V))
+ return G_nn_n
+
+
+def Green_in_n(G_in_n_minus, V, G_nn_n): # n>=2
+ G_in_n = np.dot(np.dot(G_in_n_minus, V), G_nn_n)
+ return G_in_n
+
+
+def Green_ni_n(G_nn_n, V, G_ni_n_minus): # n>=2
+ G_ni_n = np.dot(np.dot(G_nn_n, V.transpose().conj()), G_ni_n_minus)
+ return G_ni_n
+
+
+def Green_ii_n(G_ii_n_minus, G_in_n_minus, V, G_nn_n, G_ni_n_minus): # n>=i
+ G_ii_n = G_ii_n_minus+np.dot(np.dot(np.dot(np.dot(G_in_n_minus, V), G_nn_n), V.transpose().conj()),G_ni_n_minus)
+ return G_ii_n
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice_version_II.py b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice_version_II.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice_version_II.py
rename to academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice_version_II.py
index 964cb75..dc451ef
--- a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice_version_II.py
+++ b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/DOS_calculation_using_Dyson_equations_in_cubic_lattice_version_II.py
@@ -1,99 +1,99 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-
-
-def matrix_00(width, length):
- h00 = np.zeros((width*length, width*length))
- for x in range(length):
- for y in range(width-1):
- h00[x*width+y, x*width+y+1] = 1
- h00[x*width+y+1, x*width+y] = 1
- for x in range(length-1):
- for y in range(width):
- h00[x*width+y, (x+1)*width+y] = 1
- h00[(x+1)*width+y, x*width+y] = 1
- return h00
-
-
-def matrix_01(width, length):
- h01 = np.identity(width*length)
- return h01
-
-
-def main():
- height = 2 # z
- width = 3 # y
- length = 4 # x
- eta = 1e-2
- E = 0
- h00 = matrix_00(width, length)
- h01 = matrix_01(width, length)
- G_ii_n_with_Dyson_equation_version_II(width, length, height, E, eta, h00, h01)
-
-
-def G_ii_n_with_Dyson_equation_version_II(width, length, height, E, eta, h00, h01):
- dim = length*width
- G_11_1 = np.linalg.inv((E+eta*1j)*np.identity(dim)-h00)
- for i in range(height): # i为格林函数的右下指标
- # 初始化开始
- G_nn_n_minus = G_11_1
- G_in_n_minus = G_11_1
- G_ni_n_minus = G_11_1
- G_ii_n_minus = G_11_1
- for i0 in range(i):
- G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
- G_nn_n_minus = G_nn_n
- if i!=0:
- G_in_n_minus = G_nn_n
- G_ni_n_minus = G_nn_n
- G_ii_n_minus = G_nn_n
- # 初始化结束
- for j0 in range(height-1-i): # j0为格林函数的右上指标，表示当前体系大小，即G^{(j0)}
- G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
- G_nn_n_minus = G_nn_n
-
- G_ii_n = Green_ii_n(G_ii_n_minus, G_in_n_minus, h01, G_nn_n, G_ni_n_minus) # 需要求的对角分块矩阵
- G_ii_n_minus = G_ii_n
-
- G_in_n = Green_in_n(G_in_n_minus, h01, G_nn_n)
- G_in_n_minus = G_in_n
-
- G_ni_n = Green_ni_n(G_nn_n, h01, G_ni_n_minus)
- G_ni_n_minus = G_ni_n
- # 输出
- print('z=', i+1, ':')
- for j0 in range(width):
- print(' y=', j0+1, ':')
- for k0 in range(length):
- print(' x=', k0+1, ' ', -np.imag(G_ii_n_minus[k0*width+j0, k0*width+j0])/pi) # 态密度
-
-
-def Green_nn_n(E, eta, H00, V, G_nn_n_minus): # n>=2
- dim = H00.shape[0]
- G_nn_n = np.linalg.inv((E+eta*1j)*np.identity(dim)-H00-np.dot(np.dot(V.transpose().conj(), G_nn_n_minus), V))
- return G_nn_n
-
-
-def Green_in_n(G_in_n_minus, V, G_nn_n): # n>=2
- G_in_n = np.dot(np.dot(G_in_n_minus, V), G_nn_n)
- return G_in_n
-
-
-def Green_ni_n(G_nn_n, V, G_ni_n_minus): # n>=2
- G_ni_n = np.dot(np.dot(G_nn_n, V.transpose().conj()), G_ni_n_minus)
- return G_ni_n
-
-
-def Green_ii_n(G_ii_n_minus, G_in_n_minus, V, G_nn_n, G_ni_n_minus): # n>=i
- G_ii_n = G_ii_n_minus+np.dot(np.dot(np.dot(np.dot(G_in_n_minus, V), G_nn_n), V.transpose().conj()),G_ni_n_minus)
- return G_ii_n
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+
+
+def matrix_00(width, length):
+ h00 = np.zeros((width*length, width*length))
+ for x in range(length):
+ for y in range(width-1):
+ h00[x*width+y, x*width+y+1] = 1
+ h00[x*width+y+1, x*width+y] = 1
+ for x in range(length-1):
+ for y in range(width):
+ h00[x*width+y, (x+1)*width+y] = 1
+ h00[(x+1)*width+y, x*width+y] = 1
+ return h00
+
+
+def matrix_01(width, length):
+ h01 = np.identity(width*length)
+ return h01
+
+
+def main():
+ height = 2 # z
+ width = 3 # y
+ length = 4 # x
+ eta = 1e-2
+ E = 0
+ h00 = matrix_00(width, length)
+ h01 = matrix_01(width, length)
+ G_ii_n_with_Dyson_equation_version_II(width, length, height, E, eta, h00, h01)
+
+
+def G_ii_n_with_Dyson_equation_version_II(width, length, height, E, eta, h00, h01):
+ dim = length*width
+ G_11_1 = np.linalg.inv((E+eta*1j)*np.identity(dim)-h00)
+ for i in range(height): # i为格林函数的右下指标
+ # 初始化开始
+ G_nn_n_minus = G_11_1
+ G_in_n_minus = G_11_1
+ G_ni_n_minus = G_11_1
+ G_ii_n_minus = G_11_1
+ for i0 in range(i):
+ G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
+ G_nn_n_minus = G_nn_n
+ if i!=0:
+ G_in_n_minus = G_nn_n
+ G_ni_n_minus = G_nn_n
+ G_ii_n_minus = G_nn_n
+ # 初始化结束
+ for j0 in range(height-1-i): # j0为格林函数的右上指标，表示当前体系大小，即G^{(j0)}
+ G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
+ G_nn_n_minus = G_nn_n
+
+ G_ii_n = Green_ii_n(G_ii_n_minus, G_in_n_minus, h01, G_nn_n, G_ni_n_minus) # 需要求的对角分块矩阵
+ G_ii_n_minus = G_ii_n
+
+ G_in_n = Green_in_n(G_in_n_minus, h01, G_nn_n)
+ G_in_n_minus = G_in_n
+
+ G_ni_n = Green_ni_n(G_nn_n, h01, G_ni_n_minus)
+ G_ni_n_minus = G_ni_n
+ # 输出
+ print('z=', i+1, ':')
+ for j0 in range(width):
+ print(' y=', j0+1, ':')
+ for k0 in range(length):
+ print(' x=', k0+1, ' ', -np.imag(G_ii_n_minus[k0*width+j0, k0*width+j0])/pi) # 态密度
+
+
+def Green_nn_n(E, eta, H00, V, G_nn_n_minus): # n>=2
+ dim = H00.shape[0]
+ G_nn_n = np.linalg.inv((E+eta*1j)*np.identity(dim)-H00-np.dot(np.dot(V.transpose().conj(), G_nn_n_minus), V))
+ return G_nn_n
+
+
+def Green_in_n(G_in_n_minus, V, G_nn_n): # n>=2
+ G_in_n = np.dot(np.dot(G_in_n_minus, V), G_nn_n)
+ return G_in_n
+
+
+def Green_ni_n(G_nn_n, V, G_ni_n_minus): # n>=2
+ G_ni_n = np.dot(np.dot(G_nn_n, V.transpose().conj()), G_ni_n_minus)
+ return G_ni_n
+
+
+def Green_ii_n(G_ii_n_minus, G_in_n_minus, V, G_nn_n, G_ni_n_minus): # n>=i
+ G_ii_n = G_ii_n_minus+np.dot(np.dot(np.dot(np.dot(G_in_n_minus, V), G_nn_n), V.transpose().conj()),G_ni_n_minus)
+ return G_ii_n
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_cubic_lattice.py b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_cubic_lattice.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_cubic_lattice.py
rename to academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_cubic_lattice.py
index 25acba5..652ee6e
--- a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_cubic_lattice.py
+++ b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/cubic_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_cubic_lattice.py
@@ -1,49 +1,49 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-
-
-def hamiltonian(width, length, height):
- h = np.zeros((width*length*height, width*length*height))
- for i0 in range(length):
- for j0 in range(width):
- for k0 in range(height-1):
- h[k0*width*length+i0*width+j0, (k0+1)*width*length+i0*width+j0] = 1
- h[(k0+1)*width*length+i0*width+j0, k0*width*length+i0*width+j0] = 1
- for i0 in range(length):
- for j0 in range(width-1):
- for k0 in range(height):
- h[k0*width*length+i0*width+j0, k0*width*length+i0*width+j0+1] = 1
- h[k0*width*length+i0*width+j0+1, k0*width*length+i0*width+j0] = 1
- for i0 in range(length-1):
- for j0 in range(width):
- for k0 in range(height):
- h[k0*width*length+i0*width+j0, k0*width*length+(i0+1)*width+j0] = 1
- h[k0*width*length+(i0+1)*width+j0, k0*width*length+i0*width+j0] = 1
- return h
-
-
-def main():
- height = 2 # z
- width = 3 # y
- length = 4 # x
- h = hamiltonian(width, length, height)
- E = 0
- green = np.linalg.inv((E+1e-2j)*np.eye(width*length*height)-h)
- for k0 in range(height):
- print('z=', k0+1, ':')
- for j0 in range(width):
- print(' y=', j0+1, ':')
- for i0 in range(length):
- print(' x=', i0+1, ' ', -np.imag(green[k0*width*length+i0*width+j0, k0*width*length+i0*width+j0])/pi) # 态密度
-
-
-if __name__ == "__main__":
- main()
-
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+
+
+def hamiltonian(width, length, height):
+ h = np.zeros((width*length*height, width*length*height))
+ for i0 in range(length):
+ for j0 in range(width):
+ for k0 in range(height-1):
+ h[k0*width*length+i0*width+j0, (k0+1)*width*length+i0*width+j0] = 1
+ h[(k0+1)*width*length+i0*width+j0, k0*width*length+i0*width+j0] = 1
+ for i0 in range(length):
+ for j0 in range(width-1):
+ for k0 in range(height):
+ h[k0*width*length+i0*width+j0, k0*width*length+i0*width+j0+1] = 1
+ h[k0*width*length+i0*width+j0+1, k0*width*length+i0*width+j0] = 1
+ for i0 in range(length-1):
+ for j0 in range(width):
+ for k0 in range(height):
+ h[k0*width*length+i0*width+j0, k0*width*length+(i0+1)*width+j0] = 1
+ h[k0*width*length+(i0+1)*width+j0, k0*width*length+i0*width+j0] = 1
+ return h
+
+
+def main():
+ height = 2 # z
+ width = 3 # y
+ length = 4 # x
+ h = hamiltonian(width, length, height)
+ E = 0
+ green = np.linalg.inv((E+1e-2j)*np.eye(width*length*height)-h)
+ for k0 in range(height):
+ print('z=', k0+1, ':')
+ for j0 in range(width):
+ print(' y=', j0+1, ':')
+ for i0 in range(length):
+ print(' x=', i0+1, ' ', -np.imag(green[k0*width*length+i0*width+j0, k0*width*length+i0*width+j0])/pi) # 态密度
+
+
+if __name__ == "__main__":
+ main()
+
\ No newline at end of file
diff --git a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/DOS_calculation_using_Dyson_equations_in_square_lattice.py b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/DOS_calculation_using_Dyson_equations_in_square_lattice.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/DOS_calculation_using_Dyson_equations_in_square_lattice.py
rename to academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/DOS_calculation_using_Dyson_equations_in_square_lattice.py
index 9eeee35..03dbc45
--- a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/DOS_calculation_using_Dyson_equations_in_square_lattice.py
+++ b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/DOS_calculation_using_Dyson_equations_in_square_lattice.py
@@ -1,95 +1,95 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-
-
-def matrix_00(width):
- h00 = np.zeros((width, width))
- for width0 in range(width-1):
- h00[width0, width0+1] = 1
- h00[width0+1, width0] = 1
- return h00
-
-
-def matrix_01(width):
- h01 = np.identity(width)
- return h01
-
-
-def main():
- width = 2
- length = 3
- eta = 1e-2
- E = 0
- h00 = matrix_00(width)
- h01 = matrix_01(width)
- G_ii_n_array = G_ii_n_with_Dyson_equation(width, length, E, eta, h00, h01)
- for i0 in range(length):
- # print('G_{'+str(i0+1)+','+str(i0+1)+'}^{('+str(length)+')}:')
- # print(G_ii_n_array[i0, :, :],'\n')
- print('x=', i0+1, ':')
- for j0 in range(width):
- print(' y=', j0+1, ' ', -np.imag(G_ii_n_array[i0, j0, j0])/pi) # 态密度
-
-
-def G_ii_n_with_Dyson_equation(width, length, E, eta, h00, h01):
- G_ii_n_array = np.zeros((length, width, width), complex)
- G_11_1 = np.linalg.inv((E+eta*1j)*np.identity(width)-h00)
- for i in range(length): # i为格林函数的右下指标
- # 初始化开始
- G_nn_n_minus = G_11_1
- G_in_n_minus = G_11_1
- G_ni_n_minus = G_11_1
- G_ii_n_minus = G_11_1
- for i0 in range(i):
- G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
- G_nn_n_minus = G_nn_n
- if i!=0:
- G_in_n_minus = G_nn_n
- G_ni_n_minus = G_nn_n
- G_ii_n_minus = G_nn_n
- # 初始化结束
- for j0 in range(length-1-i): # j0为格林函数的右上指标，表示当前体系大小，即G^{(j0)}
- G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
- G_nn_n_minus = G_nn_n
-
- G_ii_n = Green_ii_n(G_ii_n_minus, G_in_n_minus, h01, G_nn_n, G_ni_n_minus) # 需要求的对角分块矩阵
- G_ii_n_minus = G_ii_n
-
- G_in_n = Green_in_n(G_in_n_minus, h01, G_nn_n)
- G_in_n_minus = G_in_n
-
- G_ni_n = Green_ni_n(G_nn_n, h01, G_ni_n_minus)
- G_ni_n_minus = G_ni_n
- G_ii_n_array[i, :, :] = G_ii_n_minus
- return G_ii_n_array
-
-
-def Green_nn_n(E, eta, H00, V, G_nn_n_minus): # n>=2
- dim = H00.shape[0]
- G_nn_n = np.linalg.inv((E+eta*1j)*np.identity(dim)-H00-np.dot(np.dot(V.transpose().conj(), G_nn_n_minus), V))
- return G_nn_n
-
-
-def Green_in_n(G_in_n_minus, V, G_nn_n): # n>=2
- G_in_n = np.dot(np.dot(G_in_n_minus, V), G_nn_n)
- return G_in_n
-
-
-def Green_ni_n(G_nn_n, V, G_ni_n_minus): # n>=2
- G_ni_n = np.dot(np.dot(G_nn_n, V.transpose().conj()), G_ni_n_minus)
- return G_ni_n
-
-
-def Green_ii_n(G_ii_n_minus, G_in_n_minus, V, G_nn_n, G_ni_n_minus): # n>=i
- G_ii_n = G_ii_n_minus+np.dot(np.dot(np.dot(np.dot(G_in_n_minus, V), G_nn_n), V.transpose().conj()),G_ni_n_minus)
- return G_ii_n
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+
+
+def matrix_00(width):
+ h00 = np.zeros((width, width))
+ for width0 in range(width-1):
+ h00[width0, width0+1] = 1
+ h00[width0+1, width0] = 1
+ return h00
+
+
+def matrix_01(width):
+ h01 = np.identity(width)
+ return h01
+
+
+def main():
+ width = 2
+ length = 3
+ eta = 1e-2
+ E = 0
+ h00 = matrix_00(width)
+ h01 = matrix_01(width)
+ G_ii_n_array = G_ii_n_with_Dyson_equation(width, length, E, eta, h00, h01)
+ for i0 in range(length):
+ # print('G_{'+str(i0+1)+','+str(i0+1)+'}^{('+str(length)+')}:')
+ # print(G_ii_n_array[i0, :, :],'\n')
+ print('x=', i0+1, ':')
+ for j0 in range(width):
+ print(' y=', j0+1, ' ', -np.imag(G_ii_n_array[i0, j0, j0])/pi) # 态密度
+
+
+def G_ii_n_with_Dyson_equation(width, length, E, eta, h00, h01):
+ G_ii_n_array = np.zeros((length, width, width), complex)
+ G_11_1 = np.linalg.inv((E+eta*1j)*np.identity(width)-h00)
+ for i in range(length): # i为格林函数的右下指标
+ # 初始化开始
+ G_nn_n_minus = G_11_1
+ G_in_n_minus = G_11_1
+ G_ni_n_minus = G_11_1
+ G_ii_n_minus = G_11_1
+ for i0 in range(i):
+ G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
+ G_nn_n_minus = G_nn_n
+ if i!=0:
+ G_in_n_minus = G_nn_n
+ G_ni_n_minus = G_nn_n
+ G_ii_n_minus = G_nn_n
+ # 初始化结束
+ for j0 in range(length-1-i): # j0为格林函数的右上指标，表示当前体系大小，即G^{(j0)}
+ G_nn_n = Green_nn_n(E, eta, h00, h01, G_nn_n_minus)
+ G_nn_n_minus = G_nn_n
+
+ G_ii_n = Green_ii_n(G_ii_n_minus, G_in_n_minus, h01, G_nn_n, G_ni_n_minus) # 需要求的对角分块矩阵
+ G_ii_n_minus = G_ii_n
+
+ G_in_n = Green_in_n(G_in_n_minus, h01, G_nn_n)
+ G_in_n_minus = G_in_n
+
+ G_ni_n = Green_ni_n(G_nn_n, h01, G_ni_n_minus)
+ G_ni_n_minus = G_ni_n
+ G_ii_n_array[i, :, :] = G_ii_n_minus
+ return G_ii_n_array
+
+
+def Green_nn_n(E, eta, H00, V, G_nn_n_minus): # n>=2
+ dim = H00.shape[0]
+ G_nn_n = np.linalg.inv((E+eta*1j)*np.identity(dim)-H00-np.dot(np.dot(V.transpose().conj(), G_nn_n_minus), V))
+ return G_nn_n
+
+
+def Green_in_n(G_in_n_minus, V, G_nn_n): # n>=2
+ G_in_n = np.dot(np.dot(G_in_n_minus, V), G_nn_n)
+ return G_in_n
+
+
+def Green_ni_n(G_nn_n, V, G_ni_n_minus): # n>=2
+ G_ni_n = np.dot(np.dot(G_nn_n, V.transpose().conj()), G_ni_n_minus)
+ return G_ni_n
+
+
+def Green_ii_n(G_ii_n_minus, G_in_n_minus, V, G_nn_n, G_ni_n_minus): # n>=i
+ G_ii_n = G_ii_n_minus+np.dot(np.dot(np.dot(np.dot(G_in_n_minus, V), G_nn_n), V.transpose().conj()),G_ni_n_minus)
+ return G_ii_n
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_square_lattice.py b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_square_lattice.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_square_lattice.py
rename to academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_square_lattice.py
index 185bc4a..b14e74c
--- a/academic_codes/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_square_lattice.py
+++ b/academic_codes/density_of_states/2020.12.31_DOS_calculation_using_Dyson_equations/square_lattice/get_DOS_by_direct_inverseion_of_Hamiltonian_of_square_lattice.py
@@ -1,41 +1,41 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-
-
-def hamiltonian(width, length):
- h = np.zeros((width*length, width*length))
- for i0 in range(length):
- for j0 in range(width-1):
- h[i0*width+j0, i0*width+j0+1] = 1
- h[i0*width+j0+1, i0*width+j0] = 1
- for i0 in range(length-1):
- for j0 in range(width):
- h[i0*width+j0, (i0+1)*width+j0] = 1
- h[(i0+1)*width+j0, i0*width+j0] = 1
- return h
-
-
-def main():
- width = 2
- length = 3
- h = hamiltonian(width, length)
- E = 0
- green = np.linalg.inv((E+1e-2j)*np.eye(width*length)-h)
- for i0 in range(length):
- # print('G_{'+str(i0+1)+','+str(i0+1)+'}^{('+str(length)+')}:')
- # print(green[i0*width+0: i0*width+width, i0*width+0: i0*width+width], '\n')
- print('x=', i0+1, ':')
- for j0 in range(width):
- print(' y=', j0+1, ' ', -np.imag(green[i0*width+j0, i0*width+j0])/pi)
-
-
-if __name__ == "__main__":
- main()
-
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/7650
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+
+
+def hamiltonian(width, length):
+ h = np.zeros((width*length, width*length))
+ for i0 in range(length):
+ for j0 in range(width-1):
+ h[i0*width+j0, i0*width+j0+1] = 1
+ h[i0*width+j0+1, i0*width+j0] = 1
+ for i0 in range(length-1):
+ for j0 in range(width):
+ h[i0*width+j0, (i0+1)*width+j0] = 1
+ h[(i0+1)*width+j0, i0*width+j0] = 1
+ return h
+
+
+def main():
+ width = 2
+ length = 3
+ h = hamiltonian(width, length)
+ E = 0
+ green = np.linalg.inv((E+1e-2j)*np.eye(width*length)-h)
+ for i0 in range(length):
+ # print('G_{'+str(i0+1)+','+str(i0+1)+'}^{('+str(length)+')}:')
+ # print(green[i0*width+0: i0*width+width, i0*width+0: i0*width+width], '\n')
+ print('x=', i0+1, ':')
+ for j0 in range(width):
+ print(' y=', j0+1, ' ', -np.imag(green[i0*width+j0, i0*width+j0])/pi)
+
+
+if __name__ == "__main__":
+ main()
+
\ No newline at end of file
diff --git a/academic_codes/2019.10.23_Hamiltonian_and_bands_of_graphene/1D_graphene.py b/academic_codes/models_and_bands/2019.10.23_Hamiltonian_and_bands_of_graphene/1D_graphene.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2019.10.23_Hamiltonian_and_bands_of_graphene/1D_graphene.py
rename to academic_codes/models_and_bands/2019.10.23_Hamiltonian_and_bands_of_graphene/1D_graphene.py
index 2faafb8..31f2e52
--- a/academic_codes/2019.10.23_Hamiltonian_and_bands_of_graphene/1D_graphene.py
+++ b/academic_codes/models_and_bands/2019.10.23_Hamiltonian_and_bands_of_graphene/1D_graphene.py
@@ -1,69 +1,69 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/408
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import functools
-
-
-def hamiltonian(k, N, M, t1): # graphene哈密顿量（N是条带的宽度参数）
- # 初始化为零矩阵
- h00 = np.zeros((4*N, 4*N), dtype=complex)
- h01 = np.zeros((4*N, 4*N), dtype=complex)
-
- # 原胞内的跃迁h00
- for i in range(N):
- h00[i*4+0, i*4+0] = M
- h00[i*4+1, i*4+1] = -M
- h00[i*4+2, i*4+2] = M
- h00[i*4+3, i*4+3] = -M
-
- # 最近邻
- h00[i*4+0, i*4+1] = t1
- h00[i*4+1, i*4+0] = t1
- h00[i*4+1, i*4+2] = t1
- h00[i*4+2, i*4+1] = t1
- h00[i*4+2, i*4+3] = t1
- h00[i*4+3, i*4+2] = t1
- for i in range(N-1):
- # 最近邻
- h00[i*4+3, (i+1)*4+0] = t1
- h00[(i+1)*4+0, i*4+3] = t1
-
- # 原胞间的跃迁h01
- for i in range(N):
- # 最近邻
- h01[i*4+1, i*4+0] = t1
- h01[i*4+2, i*4+3] = t1
-
- matrix = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
- return matrix
-
-
-def main():
- hamiltonian0 = functools.partial(hamiltonian, N=40, M=0, t1=1)
- k = np.linspace(-pi, pi, 300)
- plot_bands_one_dimension(k, hamiltonian0)
-
-
-def plot_bands_one_dimension(k, hamiltonian):
- dim = hamiltonian(0).shape[0]
- dim_k = k.shape[0]
- eigenvalue_k = np.zeros((dim_k, dim))
- i0 = 0
- for k0 in k:
- matrix0 = hamiltonian(k0)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
- i0 += 1
- for dim0 in range(dim):
- plt.plot(k, eigenvalue_k[:, dim0], '-k')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/408
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import functools
+
+
+def hamiltonian(k, N, M, t1): # graphene哈密顿量（N是条带的宽度参数）
+ # 初始化为零矩阵
+ h00 = np.zeros((4*N, 4*N), dtype=complex)
+ h01 = np.zeros((4*N, 4*N), dtype=complex)
+
+ # 原胞内的跃迁h00
+ for i in range(N):
+ h00[i*4+0, i*4+0] = M
+ h00[i*4+1, i*4+1] = -M
+ h00[i*4+2, i*4+2] = M
+ h00[i*4+3, i*4+3] = -M
+
+ # 最近邻
+ h00[i*4+0, i*4+1] = t1
+ h00[i*4+1, i*4+0] = t1
+ h00[i*4+1, i*4+2] = t1
+ h00[i*4+2, i*4+1] = t1
+ h00[i*4+2, i*4+3] = t1
+ h00[i*4+3, i*4+2] = t1
+ for i in range(N-1):
+ # 最近邻
+ h00[i*4+3, (i+1)*4+0] = t1
+ h00[(i+1)*4+0, i*4+3] = t1
+
+ # 原胞间的跃迁h01
+ for i in range(N):
+ # 最近邻
+ h01[i*4+1, i*4+0] = t1
+ h01[i*4+2, i*4+3] = t1
+
+ matrix = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
+ return matrix
+
+
+def main():
+ hamiltonian0 = functools.partial(hamiltonian, N=40, M=0, t1=1)
+ k = np.linspace(-pi, pi, 300)
+ plot_bands_one_dimension(k, hamiltonian0)
+
+
+def plot_bands_one_dimension(k, hamiltonian):
+ dim = hamiltonian(0).shape[0]
+ dim_k = k.shape[0]
+ eigenvalue_k = np.zeros((dim_k, dim))
+ i0 = 0
+ for k0 in k:
+ matrix0 = hamiltonian(k0)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
+ i0 += 1
+ for dim0 in range(dim):
+ plt.plot(k, eigenvalue_k[:, dim0], '-k')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2019.10.23_Hamiltonian_and_bands_of_graphene/2D_graphene.py b/academic_codes/models_and_bands/2019.10.23_Hamiltonian_and_bands_of_graphene/2D_graphene.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2019.10.23_Hamiltonian_and_bands_of_graphene/2D_graphene.py
rename to academic_codes/models_and_bands/2019.10.23_Hamiltonian_and_bands_of_graphene/2D_graphene.py
index a68736a..570d7fc
--- a/academic_codes/2019.10.23_Hamiltonian_and_bands_of_graphene/2D_graphene.py
+++ b/academic_codes/models_and_bands/2019.10.23_Hamiltonian_and_bands_of_graphene/2D_graphene.py
@@ -1,65 +1,65 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/408
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import functools
-
-
-def hamiltonian(k1, k2, M, t1, a=1/sqrt(3)): # graphene哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
- # 初始化为零矩阵
- h0 = np.zeros((2, 2), dtype=complex)
- h1 = np.zeros((2, 2), dtype=complex)
-
- # 质量项(mass term)，用于打开带隙
- h0[0, 0] = M
- h0[1, 1] = -M
-
- # 最近邻项
- h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
- h1[0, 1] = h1[1, 0].conj()
-
- # # 最近邻项也可写成这种形式
- # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
- # h1[0, 1] = h1[1, 0].conj()
-
- matrix = h0 + h1
- return matrix
-
-
-def main():
- hamiltonian0 = functools.partial(hamiltonian, M=0, t1=1, a=1/sqrt(3)) # 使用偏函数，固定一些参数
- k1 = np.linspace(-2*pi, 2*pi, 500)
- k2 = np.linspace(-2*pi, 2*pi, 500)
- plot_bands_two_dimension(k1, k2, hamiltonian0)
-
-
-def plot_bands_two_dimension(k1, k2, hamiltonian):
- from matplotlib import cm
- dim = hamiltonian(0, 0).shape[0]
- dim1 = k1.shape[0]
- dim2 = k2.shape[0]
- eigenvalue_k = np.zeros((dim2, dim1, dim))
- i0 = 0
- for k10 in k1:
- j0 = 0
- for k20 in k2:
- matrix0 = hamiltonian(k10, k20)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[j0, i0, :] = np.sort(np.real(eigenvalue[:]))
- j0 += 1
- i0 += 1
- fig = plt.figure()
- ax = fig.gca(projection='3d')
- k1, k2 = np.meshgrid(k1, k2)
- for dim0 in range(dim):
- ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], rcount=200, ccount=200, cmap=cm.coolwarm, linewidth=0, antialiased=False)
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/408
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import functools
+
+
+def hamiltonian(k1, k2, M, t1, a=1/sqrt(3)): # graphene哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
+ # 初始化为零矩阵
+ h0 = np.zeros((2, 2), dtype=complex)
+ h1 = np.zeros((2, 2), dtype=complex)
+
+ # 质量项(mass term)，用于打开带隙
+ h0[0, 0] = M
+ h0[1, 1] = -M
+
+ # 最近邻项
+ h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
+ h1[0, 1] = h1[1, 0].conj()
+
+ # # 最近邻项也可写成这种形式
+ # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
+ # h1[0, 1] = h1[1, 0].conj()
+
+ matrix = h0 + h1
+ return matrix
+
+
+def main():
+ hamiltonian0 = functools.partial(hamiltonian, M=0, t1=1, a=1/sqrt(3)) # 使用偏函数，固定一些参数
+ k1 = np.linspace(-2*pi, 2*pi, 500)
+ k2 = np.linspace(-2*pi, 2*pi, 500)
+ plot_bands_two_dimension(k1, k2, hamiltonian0)
+
+
+def plot_bands_two_dimension(k1, k2, hamiltonian):
+ from matplotlib import cm
+ dim = hamiltonian(0, 0).shape[0]
+ dim1 = k1.shape[0]
+ dim2 = k2.shape[0]
+ eigenvalue_k = np.zeros((dim2, dim1, dim))
+ i0 = 0
+ for k10 in k1:
+ j0 = 0
+ for k20 in k2:
+ matrix0 = hamiltonian(k10, k20)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[j0, i0, :] = np.sort(np.real(eigenvalue[:]))
+ j0 += 1
+ i0 += 1
+ fig = plt.figure()
+ ax = fig.gca(projection='3d')
+ k1, k2 = np.meshgrid(k1, k2)
+ for dim0 in range(dim):
+ ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], rcount=200, ccount=200, cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/1D_Haldane_model.py b/academic_codes/models_and_bands/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/1D_Haldane_model.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/1D_Haldane_model.py
rename to academic_codes/models_and_bands/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/1D_Haldane_model.py
index a75a020..2e8cf3e
--- a/academic_codes/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/1D_Haldane_model.py
+++ b/academic_codes/models_and_bands/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/1D_Haldane_model.py
@@ -1,94 +1,94 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/410
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import functools
-
-
-def hamiltonian(k, N, M, t1, t2, phi): # Haldane哈密顿量（N是条带的宽度参数）
- # 初始化为零矩阵
- h00 = np.zeros((4*N, 4*N), dtype=complex)
- h01 = np.zeros((4*N, 4*N), dtype=complex)
-
- # 原胞内的跃迁h00
- for i in range(N):
- h00[i*4+0, i*4+0] = M
- h00[i*4+1, i*4+1] = -M
- h00[i*4+2, i*4+2] = M
- h00[i*4+3, i*4+3] = -M
-
- # 最近邻
- h00[i*4+0, i*4+1] = t1
- h00[i*4+1, i*4+0] = t1
- h00[i*4+1, i*4+2] = t1
- h00[i*4+2, i*4+1] = t1
- h00[i*4+2, i*4+3] = t1
- h00[i*4+3, i*4+2] = t1
-
- # 次近邻
- h00[i*4+0, i*4+2] = t2*cmath.exp(-1j*phi)
- h00[i*4+2, i*4+0] = h00[i*4+0, i*4+2].conj()
- h00[i*4+1, i*4+3] = t2*cmath.exp(-1j*phi)
- h00[i*4+3, i*4+1] = h00[i*4+1, i*4+3].conj()
- for i in range(N-1):
- # 最近邻
- h00[i*4+3, (i+1)*4+0] = t1
- h00[(i+1)*4+0, i*4+3] = t1
-
- # 次近邻
- h00[i*4+2, (i+1)*4+0] = t2*cmath.exp(1j*phi)
- h00[(i+1)*4+0, i*4+2] = h00[i*4+2, (i+1)*4+0].conj()
- h00[i*4+3, (i+1)*4+1] = t2*cmath.exp(1j*phi)
- h00[(i+1)*4+1, i*4+3] = h00[i*4+3, (i+1)*4+1].conj()
-
- # 原胞间的跃迁h01
- for i in range(N):
- # 最近邻
- h01[i*4+1, i*4+0] = t1
- h01[i*4+2, i*4+3] = t1
-
- # 次近邻
- h01[i*4+0, i*4+0] = t2*cmath.exp(1j*phi)
- h01[i*4+1, i*4+1] = t2*cmath.exp(-1j*phi)
- h01[i*4+2, i*4+2] = t2*cmath.exp(1j*phi)
- h01[i*4+3, i*4+3] = t2*cmath.exp(-1j*phi)
-
- h01[i*4+1, i*4+3] = t2*cmath.exp(1j*phi)
- h01[i*4+2, i*4+0] = t2*cmath.exp(-1j*phi)
- if i != 0:
- h01[i*4+1, (i-1)*4+3] = t2*cmath.exp(1j*phi)
- for i in range(N-1):
- h01[i*4+2, (i+1)*4+0] = t2*cmath.exp(-1j*phi)
-
- matrix = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
- return matrix
-
-
-def main():
- hamiltonian0 = functools.partial(hamiltonian, N=40, M=2/3, t1=1, t2=1/3, phi=pi/4)
- k = np.linspace(-pi, pi, 300)
- plot_bands_one_dimension(k, hamiltonian0)
-
-
-def plot_bands_one_dimension(k, hamiltonian):
- dim = hamiltonian(0).shape[0]
- dim_k = k.shape[0]
- eigenvalue_k = np.zeros((dim_k, dim))
- i0 = 0
- for k0 in k:
- matrix0 = hamiltonian(k0)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
- i0 += 1
- for dim0 in range(dim):
- plt.plot(k, eigenvalue_k[:, dim0], '-k')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/410
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import functools
+
+
+def hamiltonian(k, N, M, t1, t2, phi): # Haldane哈密顿量（N是条带的宽度参数）
+ # 初始化为零矩阵
+ h00 = np.zeros((4*N, 4*N), dtype=complex)
+ h01 = np.zeros((4*N, 4*N), dtype=complex)
+
+ # 原胞内的跃迁h00
+ for i in range(N):
+ h00[i*4+0, i*4+0] = M
+ h00[i*4+1, i*4+1] = -M
+ h00[i*4+2, i*4+2] = M
+ h00[i*4+3, i*4+3] = -M
+
+ # 最近邻
+ h00[i*4+0, i*4+1] = t1
+ h00[i*4+1, i*4+0] = t1
+ h00[i*4+1, i*4+2] = t1
+ h00[i*4+2, i*4+1] = t1
+ h00[i*4+2, i*4+3] = t1
+ h00[i*4+3, i*4+2] = t1
+
+ # 次近邻
+ h00[i*4+0, i*4+2] = t2*cmath.exp(-1j*phi)
+ h00[i*4+2, i*4+0] = h00[i*4+0, i*4+2].conj()
+ h00[i*4+1, i*4+3] = t2*cmath.exp(-1j*phi)
+ h00[i*4+3, i*4+1] = h00[i*4+1, i*4+3].conj()
+ for i in range(N-1):
+ # 最近邻
+ h00[i*4+3, (i+1)*4+0] = t1
+ h00[(i+1)*4+0, i*4+3] = t1
+
+ # 次近邻
+ h00[i*4+2, (i+1)*4+0] = t2*cmath.exp(1j*phi)
+ h00[(i+1)*4+0, i*4+2] = h00[i*4+2, (i+1)*4+0].conj()
+ h00[i*4+3, (i+1)*4+1] = t2*cmath.exp(1j*phi)
+ h00[(i+1)*4+1, i*4+3] = h00[i*4+3, (i+1)*4+1].conj()
+
+ # 原胞间的跃迁h01
+ for i in range(N):
+ # 最近邻
+ h01[i*4+1, i*4+0] = t1
+ h01[i*4+2, i*4+3] = t1
+
+ # 次近邻
+ h01[i*4+0, i*4+0] = t2*cmath.exp(1j*phi)
+ h01[i*4+1, i*4+1] = t2*cmath.exp(-1j*phi)
+ h01[i*4+2, i*4+2] = t2*cmath.exp(1j*phi)
+ h01[i*4+3, i*4+3] = t2*cmath.exp(-1j*phi)
+
+ h01[i*4+1, i*4+3] = t2*cmath.exp(1j*phi)
+ h01[i*4+2, i*4+0] = t2*cmath.exp(-1j*phi)
+ if i != 0:
+ h01[i*4+1, (i-1)*4+3] = t2*cmath.exp(1j*phi)
+ for i in range(N-1):
+ h01[i*4+2, (i+1)*4+0] = t2*cmath.exp(-1j*phi)
+
+ matrix = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
+ return matrix
+
+
+def main():
+ hamiltonian0 = functools.partial(hamiltonian, N=40, M=2/3, t1=1, t2=1/3, phi=pi/4)
+ k = np.linspace(-pi, pi, 300)
+ plot_bands_one_dimension(k, hamiltonian0)
+
+
+def plot_bands_one_dimension(k, hamiltonian):
+ dim = hamiltonian(0).shape[0]
+ dim_k = k.shape[0]
+ eigenvalue_k = np.zeros((dim_k, dim))
+ i0 = 0
+ for k0 in k:
+ matrix0 = hamiltonian(k0)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
+ i0 += 1
+ for dim0 in range(dim):
+ plt.plot(k, eigenvalue_k[:, dim0], '-k')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/2D_Haldane_model.py b/academic_codes/models_and_bands/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/2D_Haldane_model.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/2D_Haldane_model.py
rename to academic_codes/models_and_bands/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/2D_Haldane_model.py
index 632278c..755ffa2
--- a/academic_codes/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/2D_Haldane_model.py
+++ b/academic_codes/models_and_bands/2019.10.24_Halmiltonian_and_bands_of_Haldane_model/2D_Haldane_model.py
@@ -1,70 +1,70 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/410
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import functools
-
-
-def hamiltonian(k1, k2, M, t1, t2, phi, a=1/sqrt(3)): # Haldane哈密顿量(a为原子间距，不赋值的话默认为1/sqrt(3)）
- # 初始化为零矩阵
- h0 = np.zeros((2, 2), dtype=complex)
- h1 = np.zeros((2, 2), dtype=complex)
- h2 = np.zeros((2, 2), dtype=complex)
-
- # 质量项(mass term)，用于打开带隙
- h0[0, 0] = M
- h0[1, 1] = -M
-
- # 最近邻项
- h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
- h1[0, 1] = h1[1, 0].conj()
-
- # 最近邻项也可写成这种形式
- # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
- # h1[0, 1] = h1[1, 0].conj()
-
- # 次近邻项
- h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
- h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
-
- matrix = h0 + h1 + h2 + h2.transpose().conj()
- return matrix
-
-
-def main():
- hamiltonian0 = functools.partial(hamiltonian, M=2/3, t1=1, t2=1/3, phi=pi/4, a=1/sqrt(3))
- k1 = np.linspace(-2*pi, 2*pi, 500)
- k2 = np.linspace(-2*pi, 2*pi, 500)
- plot_bands_two_dimension(k1, k2, hamiltonian0)
-
-
-def plot_bands_two_dimension(k1, k2, hamiltonian):
- from matplotlib import cm
- dim = hamiltonian(0, 0).shape[0]
- dim1 = k1.shape[0]
- dim2 = k2.shape[0]
- eigenvalue_k = np.zeros((dim2, dim1, dim))
- i0 = 0
- for k10 in k1:
- j0 = 0
- for k20 in k2:
- matrix0 = hamiltonian(k10, k20)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[j0, i0, :] = np.sort(np.real(eigenvalue[:]))
- j0 += 1
- i0 += 1
- fig = plt.figure()
- ax = fig.gca(projection='3d')
- k1, k2 = np.meshgrid(k1, k2)
- for dim0 in range(dim):
- ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/410
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import functools
+
+
+def hamiltonian(k1, k2, M, t1, t2, phi, a=1/sqrt(3)): # Haldane哈密顿量(a为原子间距，不赋值的话默认为1/sqrt(3)）
+ # 初始化为零矩阵
+ h0 = np.zeros((2, 2), dtype=complex)
+ h1 = np.zeros((2, 2), dtype=complex)
+ h2 = np.zeros((2, 2), dtype=complex)
+
+ # 质量项(mass term)，用于打开带隙
+ h0[0, 0] = M
+ h0[1, 1] = -M
+
+ # 最近邻项
+ h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
+ h1[0, 1] = h1[1, 0].conj()
+
+ # 最近邻项也可写成这种形式
+ # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
+ # h1[0, 1] = h1[1, 0].conj()
+
+ # 次近邻项
+ h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+ h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+
+ matrix = h0 + h1 + h2 + h2.transpose().conj()
+ return matrix
+
+
+def main():
+ hamiltonian0 = functools.partial(hamiltonian, M=2/3, t1=1, t2=1/3, phi=pi/4, a=1/sqrt(3))
+ k1 = np.linspace(-2*pi, 2*pi, 500)
+ k2 = np.linspace(-2*pi, 2*pi, 500)
+ plot_bands_two_dimension(k1, k2, hamiltonian0)
+
+
+def plot_bands_two_dimension(k1, k2, hamiltonian):
+ from matplotlib import cm
+ dim = hamiltonian(0, 0).shape[0]
+ dim1 = k1.shape[0]
+ dim2 = k2.shape[0]
+ eigenvalue_k = np.zeros((dim2, dim1, dim))
+ i0 = 0
+ for k10 in k1:
+ j0 = 0
+ for k20 in k2:
+ matrix0 = hamiltonian(k10, k20)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[j0, i0, :] = np.sort(np.real(eigenvalue[:]))
+ j0 += 1
+ i0 += 1
+ fig = plt.figure()
+ ax = fig.gca(projection='3d')
+ k1, k2 = np.meshgrid(k1, k2)
+ for dim0 in range(dim):
+ ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2019.11.27_Hamiltonian_of_BHZ_model_and_bands_in_quasi_1D_systems/bands_in_quasi_1D_BHZ_systems.py b/academic_codes/models_and_bands/2019.11.27_Hamiltonian_of_BHZ_model_and_bands_in_quasi_1D_systems/bands_in_quasi_1D_BHZ_systems.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.27_Hamiltonian_of_BHZ_model_and_bands_in_quasi_1D_systems/bands_in_quasi_1D_BHZ_systems.py
rename to academic_codes/models_and_bands/2019.11.27_Hamiltonian_of_BHZ_model_and_bands_in_quasi_1D_systems/bands_in_quasi_1D_BHZ_systems.py
index 720b51d..9cae6bf
--- a/academic_codes/2019.11.27_Hamiltonian_of_BHZ_model_and_bands_in_quasi_1D_systems/bands_in_quasi_1D_BHZ_systems.py
+++ b/academic_codes/models_and_bands/2019.11.27_Hamiltonian_of_BHZ_model_and_bands_in_quasi_1D_systems/bands_in_quasi_1D_BHZ_systems.py
@@ -1,84 +1,84 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/2327
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import * # 引入sqrt(), pi, exp等
-import cmath # 要处理复数情况，用到cmath.exp()
-import functools # 使用偏函数functools.partial()
-
-
-def get_terms(A, B, C, D, M, a):
- E_s = C+M-4*(D+B)/(a**2)
- E_p = C-M-4*(D-B)/(a**2)
- V_ss = (D+B)/(a**2)
- V_pp = (D-B)/(a**2)
- V_sp = -1j*A/(2*a)
- H0 = np.zeros((4, 4), dtype=complex) # 在位能 (on-site energy)
- H1 = np.zeros((4, 4), dtype=complex) # x方向的跃迁 (hopping)
- H2 = np.zeros((4, 4), dtype=complex) # y方向的跃迁 (hopping)
- H0[0, 0] = E_s
- H0[1, 1] = E_p
- H0[2, 2] = E_s
- H0[3, 3] = E_p
-
- H1[0, 0] = V_ss
- H1[1, 1] = V_pp
- H1[2, 2] = V_ss
- H1[3, 3] = V_pp
- H1[0, 1] = V_sp
- H1[1, 0] = -np.conj(V_sp)
- H1[2, 3] = np.conj(V_sp)
- H1[3, 2] = -V_sp
-
- H2[0, 0] = V_ss
- H2[1, 1] = V_pp
- H2[2, 2] = V_ss
- H2[3, 3] = V_pp
- H2[0, 1] = 1j*V_sp
- H2[1, 0] = 1j*np.conj(V_sp)
- H2[2, 3] = -1j*np.conj(V_sp)
- H2[3, 2] = -1j*V_sp
- return H0, H1, H2
-
-
-def BHZ_model(k, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01, a=1, N=100): # 这边数值是不赋值时的默认参数
- H0, H1, H2 = get_terms(A, B, C, D, M, a)
- H00 = np.zeros((4*N, 4*N), dtype=complex) # 元胞内，条带宽度为N
- H01 = np.zeros((4*N, 4*N), dtype=complex) # 条带方向元胞间的跃迁
- for i in range(N):
- H00[i*4+0:i*4+4, i*4+0:i*4+4] = H0 # a:b代表 a <= x < b
- H01[i*4+0:i*4+4, i*4+0:i*4+4] = H1
- for i in range(N-1):
- H00[i*4+0:i*4+4, (i+1)*4+0:(i+1)*4+4] = H2
- H00[(i+1)*4+0:(i+1)*4+4, i*4+0:i*4+4] = np.conj(np.transpose(H2))
- H = H00 + H01 * cmath.exp(-1j * k) + H01.transpose().conj() * cmath.exp(1j * k)
- return H
-
-
-def main():
- hamiltonian0 = functools.partial(BHZ_model, N=50) # 使用偏函数，固定一些参数
- k = np.linspace(-pi, pi, 300) # 300
- plot_bands_one_dimension(k, hamiltonian0)
-
-
-def plot_bands_one_dimension(k, hamiltonian, filename='bands_1D'):
- dim = hamiltonian(0).shape[0]
- dim_k = k.shape[0]
- eigenvalue_k = np.zeros((dim_k, dim)) # np.zeros()里要用tuple
- i0 = 0
- for k0 in k:
- matrix0 = hamiltonian(k0)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
- i0 += 1
- for dim0 in range(dim):
- plt.plot(k, eigenvalue_k[:, dim0], '-k') # -.
- # plt.savefig(filename + '.jpg') # plt.savefig(filename+'.eps')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/2327
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import * # 引入sqrt(), pi, exp等
+import cmath # 要处理复数情况，用到cmath.exp()
+import functools # 使用偏函数functools.partial()
+
+
+def get_terms(A, B, C, D, M, a):
+ E_s = C+M-4*(D+B)/(a**2)
+ E_p = C-M-4*(D-B)/(a**2)
+ V_ss = (D+B)/(a**2)
+ V_pp = (D-B)/(a**2)
+ V_sp = -1j*A/(2*a)
+ H0 = np.zeros((4, 4), dtype=complex) # 在位能 (on-site energy)
+ H1 = np.zeros((4, 4), dtype=complex) # x方向的跃迁 (hopping)
+ H2 = np.zeros((4, 4), dtype=complex) # y方向的跃迁 (hopping)
+ H0[0, 0] = E_s
+ H0[1, 1] = E_p
+ H0[2, 2] = E_s
+ H0[3, 3] = E_p
+
+ H1[0, 0] = V_ss
+ H1[1, 1] = V_pp
+ H1[2, 2] = V_ss
+ H1[3, 3] = V_pp
+ H1[0, 1] = V_sp
+ H1[1, 0] = -np.conj(V_sp)
+ H1[2, 3] = np.conj(V_sp)
+ H1[3, 2] = -V_sp
+
+ H2[0, 0] = V_ss
+ H2[1, 1] = V_pp
+ H2[2, 2] = V_ss
+ H2[3, 3] = V_pp
+ H2[0, 1] = 1j*V_sp
+ H2[1, 0] = 1j*np.conj(V_sp)
+ H2[2, 3] = -1j*np.conj(V_sp)
+ H2[3, 2] = -1j*V_sp
+ return H0, H1, H2
+
+
+def BHZ_model(k, A=0.3645/5, B=-0.686/25, C=0, D=-0.512/25, M=-0.01, a=1, N=100): # 这边数值是不赋值时的默认参数
+ H0, H1, H2 = get_terms(A, B, C, D, M, a)
+ H00 = np.zeros((4*N, 4*N), dtype=complex) # 元胞内，条带宽度为N
+ H01 = np.zeros((4*N, 4*N), dtype=complex) # 条带方向元胞间的跃迁
+ for i in range(N):
+ H00[i*4+0:i*4+4, i*4+0:i*4+4] = H0 # a:b代表 a <= x < b
+ H01[i*4+0:i*4+4, i*4+0:i*4+4] = H1
+ for i in range(N-1):
+ H00[i*4+0:i*4+4, (i+1)*4+0:(i+1)*4+4] = H2
+ H00[(i+1)*4+0:(i+1)*4+4, i*4+0:i*4+4] = np.conj(np.transpose(H2))
+ H = H00 + H01 * cmath.exp(-1j * k) + H01.transpose().conj() * cmath.exp(1j * k)
+ return H
+
+
+def main():
+ hamiltonian0 = functools.partial(BHZ_model, N=50) # 使用偏函数，固定一些参数
+ k = np.linspace(-pi, pi, 300) # 300
+ plot_bands_one_dimension(k, hamiltonian0)
+
+
+def plot_bands_one_dimension(k, hamiltonian, filename='bands_1D'):
+ dim = hamiltonian(0).shape[0]
+ dim_k = k.shape[0]
+ eigenvalue_k = np.zeros((dim_k, dim)) # np.zeros()里要用tuple
+ i0 = 0
+ for k0 in k:
+ matrix0 = hamiltonian(k0)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
+ i0 += 1
+ for dim0 in range(dim):
+ plt.plot(k, eigenvalue_k[:, dim0], '-k') # -.
+ # plt.savefig(filename + '.jpg') # plt.savefig(filename+'.eps')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.01.03_bands_of_qusi_1D_square_lattice/bands_of_qusi_1D_square_lattice.py b/academic_codes/models_and_bands/2020.01.03_bands_of_qusi_1D_square_lattice/bands_of_qusi_1D_square_lattice.py
similarity index 100%
rename from academic_codes/2020.01.03_bands_of_qusi_1D_square_lattice/bands_of_qusi_1D_square_lattice.py
rename to academic_codes/models_and_bands/2020.01.03_bands_of_qusi_1D_square_lattice/bands_of_qusi_1D_square_lattice.py
diff --git a/academic_codes/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_1.py b/academic_codes/models_and_bands/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_1.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_1.py
rename to academic_codes/models_and_bands/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_1.py
index a655c58..a10c630
--- a/academic_codes/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_1.py
+++ b/academic_codes/models_and_bands/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_1.py
@@ -1,69 +1,69 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4322
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import functools
-
-
-def hamiltonian(k, N):
- # 初始化为零矩阵
- h = np.zeros((4*N, 4*N), dtype=complex)
-
- t=1
- a=1
- t0=0.2 # 层间跃迁
- V=0.2 # 层间的势能差为2V
-
- for i in range(N):
- h[i*4+0, i*4+0] = V
- h[i*4+1, i*4+1] = V
- h[i*4+2, i*4+2] = -V
- h[i*4+3, i*4+3] = -V
-
- h[i*4+0, i*4+1] = -t*(1+cmath.exp(1j*k*a))
- h[i*4+1, i*4+0] = -t*(1+cmath.exp(-1j*k*a))
- h[i*4+2, i*4+3] = -t*(1+cmath.exp(1j*k*a))
- h[i*4+3, i*4+2] = -t*(1+cmath.exp(-1j*k*a))
-
- h[i*4+0, i*4+3] = -t0
- h[i*4+3, i*4+0] = -t0
-
- for i in range(N-1):
- # 最近邻
- h[i*4+1, (i+1)*4+0] = -t
- h[(i+1)*4+0, i*4+1] = -t
-
- h[i*4+3, (i+1)*4+2] = -t
- h[(i+1)*4+2, i*4+3] = -t
- return h
-
-
-def main():
- hamiltonian0 = functools.partial(hamiltonian, N=100)
- k = np.linspace(-pi, pi, 400)
- plot_bands_one_dimension(k, hamiltonian0)
-
-
-def plot_bands_one_dimension(k, hamiltonian):
- dim = hamiltonian(0).shape[0]
- dim_k = k.shape[0]
- eigenvalue_k = np.zeros((dim_k, dim))
- i0 = 0
- for k0 in k:
- matrix0 = hamiltonian(k0)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
- i0 += 1
- print(k0)
- for dim0 in range(dim):
- plt.plot(k, eigenvalue_k[:, dim0], '-k')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4322
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import functools
+
+
+def hamiltonian(k, N):
+ # 初始化为零矩阵
+ h = np.zeros((4*N, 4*N), dtype=complex)
+
+ t=1
+ a=1
+ t0=0.2 # 层间跃迁
+ V=0.2 # 层间的势能差为2V
+
+ for i in range(N):
+ h[i*4+0, i*4+0] = V
+ h[i*4+1, i*4+1] = V
+ h[i*4+2, i*4+2] = -V
+ h[i*4+3, i*4+3] = -V
+
+ h[i*4+0, i*4+1] = -t*(1+cmath.exp(1j*k*a))
+ h[i*4+1, i*4+0] = -t*(1+cmath.exp(-1j*k*a))
+ h[i*4+2, i*4+3] = -t*(1+cmath.exp(1j*k*a))
+ h[i*4+3, i*4+2] = -t*(1+cmath.exp(-1j*k*a))
+
+ h[i*4+0, i*4+3] = -t0
+ h[i*4+3, i*4+0] = -t0
+
+ for i in range(N-1):
+ # 最近邻
+ h[i*4+1, (i+1)*4+0] = -t
+ h[(i+1)*4+0, i*4+1] = -t
+
+ h[i*4+3, (i+1)*4+2] = -t
+ h[(i+1)*4+2, i*4+3] = -t
+ return h
+
+
+def main():
+ hamiltonian0 = functools.partial(hamiltonian, N=100)
+ k = np.linspace(-pi, pi, 400)
+ plot_bands_one_dimension(k, hamiltonian0)
+
+
+def plot_bands_one_dimension(k, hamiltonian):
+ dim = hamiltonian(0).shape[0]
+ dim_k = k.shape[0]
+ eigenvalue_k = np.zeros((dim_k, dim))
+ i0 = 0
+ for k0 in k:
+ matrix0 = hamiltonian(k0)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
+ i0 += 1
+ print(k0)
+ for dim0 in range(dim):
+ plt.plot(k, eigenvalue_k[:, dim0], '-k')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_2.py b/academic_codes/models_and_bands/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_2.py
old mode 100755
new mode 100644
similarity index 95%
rename from academic_codes/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_2.py
rename to academic_codes/models_and_bands/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_2.py
index c9f1b71..f0ff69e
--- a/academic_codes/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_2.py
+++ b/academic_codes/models_and_bands/2020.03.25_Hamiltonian_and_bands_of_bilayer_graphene/bands_of_bilayer_graphene_with_label_sequence_2.py
@@ -1,83 +1,83 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4322
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import functools
-
-
-def hamiltonian(k, N):
- # 初始化为零矩阵
- h = np.zeros((4*N, 4*N), dtype=complex)
- h11 = np.zeros((4*N, 4*N), dtype=complex) # 元胞内
- h12 = np.zeros((4*N, 4*N), dtype=complex) # 元胞间
-
- t=1
- a=1
- t0=0.2 # 层间跃迁
- V=0.2 # 层间的势能差为2V
-
- for i in range(N):
- h11[i*2+0, i*2+0] = V
- h11[i*2+1, i*2+1] = V
-
-
- h11[N*2+i*2+0, N*2+i*2+0] = -V
- h11[N*2+i*2+1, N*2+i*2+1] = -V
-
-
- h11[i*2+0, i*2+1] = -t
- h11[i*2+1, i*2+0] = -t
-
-
- h11[N*2+i*2+0, N*2+i*2+1] = -t
- h11[N*2+i*2+1, N*2+i*2+0] = -t
-
- h11[i*2+0, N*2+i*2+1] = -t0
- h11[N*2+i*2+1, i*2+0] = -t0
-
-
- for i in range(N-1):
- h11[i*2+1, (i+1)*2+0] = -t
- h11[(i+1)*2+0, i*2+1] = -t
-
- h11[N*2+i*2+1, N*2+(i+1)*2+0] = -t
- h11[N*2+(i+1)*2+0, N*2+i*2+1] = -t
-
-
- for i in range(N):
- h12[i*2+0, i*2+1] = -t
- h12[N*2+i*2+0, N*2+i*2+1] = -t
-
- h= h11 + h12*cmath.exp(-1j*k*a) + h12.transpose().conj()*cmath.exp(1j*k*a)
- return h
-
-
-def main():
- hamiltonian0 = functools.partial(hamiltonian, N=100)
- k = np.linspace(-pi, pi, 400)
- plot_bands_one_dimension(k, hamiltonian0)
-
-
-def plot_bands_one_dimension(k, hamiltonian):
- dim = hamiltonian(0).shape[0]
- dim_k = k.shape[0]
- eigenvalue_k = np.zeros((dim_k, dim))
- i0 = 0
- for k0 in k:
- matrix0 = hamiltonian(k0)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
- i0 += 1
- print(k0)
- for dim0 in range(dim):
- plt.plot(k, eigenvalue_k[:, dim0], '-k')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4322
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import functools
+
+
+def hamiltonian(k, N):
+ # 初始化为零矩阵
+ h = np.zeros((4*N, 4*N), dtype=complex)
+ h11 = np.zeros((4*N, 4*N), dtype=complex) # 元胞内
+ h12 = np.zeros((4*N, 4*N), dtype=complex) # 元胞间
+
+ t=1
+ a=1
+ t0=0.2 # 层间跃迁
+ V=0.2 # 层间的势能差为2V
+
+ for i in range(N):
+ h11[i*2+0, i*2+0] = V
+ h11[i*2+1, i*2+1] = V
+
+
+ h11[N*2+i*2+0, N*2+i*2+0] = -V
+ h11[N*2+i*2+1, N*2+i*2+1] = -V
+
+
+ h11[i*2+0, i*2+1] = -t
+ h11[i*2+1, i*2+0] = -t
+
+
+ h11[N*2+i*2+0, N*2+i*2+1] = -t
+ h11[N*2+i*2+1, N*2+i*2+0] = -t
+
+ h11[i*2+0, N*2+i*2+1] = -t0
+ h11[N*2+i*2+1, i*2+0] = -t0
+
+
+ for i in range(N-1):
+ h11[i*2+1, (i+1)*2+0] = -t
+ h11[(i+1)*2+0, i*2+1] = -t
+
+ h11[N*2+i*2+1, N*2+(i+1)*2+0] = -t
+ h11[N*2+(i+1)*2+0, N*2+i*2+1] = -t
+
+
+ for i in range(N):
+ h12[i*2+0, i*2+1] = -t
+ h12[N*2+i*2+0, N*2+i*2+1] = -t
+
+ h= h11 + h12*cmath.exp(-1j*k*a) + h12.transpose().conj()*cmath.exp(1j*k*a)
+ return h
+
+
+def main():
+ hamiltonian0 = functools.partial(hamiltonian, N=100)
+ k = np.linspace(-pi, pi, 400)
+ plot_bands_one_dimension(k, hamiltonian0)
+
+
+def plot_bands_one_dimension(k, hamiltonian):
+ dim = hamiltonian(0).shape[0]
+ dim_k = k.shape[0]
+ eigenvalue_k = np.zeros((dim_k, dim))
+ i0 = 0
+ for k0 in k:
+ matrix0 = hamiltonian(k0)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
+ i0 += 1
+ print(k0)
+ for dim0 in range(dim):
+ plt.plot(k, eigenvalue_k[:, dim0], '-k')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/Kane_Mele_model.m b/academic_codes/models_and_bands/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/Kane_Mele_model.m
similarity index 100%
rename from academic_codes/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/Kane_Mele_model.m
rename to academic_codes/models_and_bands/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/Kane_Mele_model.m
diff --git a/academic_codes/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/bands_of_quasi_1D_Kane_Mele_model_without considering_Rashba_SOC.py b/academic_codes/models_and_bands/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/bands_of_quasi_1D_Kane_Mele_model_without considering_Rashba_SOC.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/bands_of_quasi_1D_Kane_Mele_model_without considering_Rashba_SOC.py
rename to academic_codes/models_and_bands/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/bands_of_quasi_1D_Kane_Mele_model_without considering_Rashba_SOC.py
index 062e399..1db3edd
--- a/academic_codes/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/bands_of_quasi_1D_Kane_Mele_model_without considering_Rashba_SOC.py
+++ b/academic_codes/models_and_bands/2020.06.26_Hamiltonian_and_bands_of_Kane_Mele_model_without considering_Rashba_SOC/bands_of_quasi_1D_Kane_Mele_model_without considering_Rashba_SOC.py
@@ -1,104 +1,104 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4829
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import functools
-
-
-def hamiltonian(k, N, M, t1, t2, phi): # Kane-Mele model
- # 初始化为零矩阵
- h00 = np.zeros((2*4*N, 2*4*N), dtype=complex) # 因为自旋有上有下，所以整个维度要乘2。这里4是元胞内部重复单位的大小，规定元胞大小以4来倍增。
- h01 = np.zeros((2*4*N, 2*4*N), dtype=complex)
-
- for spin in range(2):
- # 原胞内的跃迁h00
- for i in range(N):
- # 最近邻
- h00[i*4*2+0*2+spin, i*4*2+1*2+spin] = t1
- h00[i*4*2+1*2+spin, i*4*2+0*2+spin] = t1
-
- h00[i*4*2+1*2+spin, i*4*2+2*2+spin] = t1
- h00[i*4*2+2*2+spin, i*4*2+1*2+spin] = t1
-
- h00[i*4*2+2*2+spin, i*4*2+3*2+spin] = t1
- h00[i*4*2+3*2+spin, i*4*2+2*2+spin] = t1
-
- # 次近邻
- h00[i*4*2+0*2+spin, i*4*2+2*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
- h00[i*4*2+2*2+spin, i*4*2+0*2+spin] = h00[i*4*2+0*2+spin, i*4*2+2*2+spin].conj()
- h00[i*4*2+1*2+spin, i*4*2+3*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
- h00[i*4*2+3*2+spin, i*4*2+1*2+spin] = h00[i*4*2+1*2+spin, i*4*2+3*2+spin].conj()
-
- for i in range(N-1):
- # 最近邻
- h00[i*4*2+3*2+spin, (i+1)*4*2+0*2+spin] = t1
- h00[(i+1)*4*2+0*2+spin, i*4*2+3*2+spin] = t1
-
- # 次近邻
- h00[i*4*2+2*2+spin, (i+1)*4*2+0*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
- h00[(i+1)*4*2+0*2+spin, i*4*2+2*2+spin] = h00[i*4*2+2*2+spin, (i+1)*4*2+0*2+spin].conj()
- h00[i*4*2+3*2+spin, (i+1)*4*2+1*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
- h00[(i+1)*4*2+1*2+spin, i*4*2+3*2+spin] = h00[i*4*2+3*2+spin, (i+1)*4*2+1*2+spin].conj()
-
- # 原胞间的跃迁h01
- for i in range(N):
- # 最近邻
- h01[i*4*2+1*2+spin, i*4*2+0*2+spin] = t1
- h01[i*4*2+2*2+spin, i*4*2+3*2+spin] = t1
-
- # 次近邻
- h01[i*4*2+0*2+spin, i*4*2+0*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
- h01[i*4*2+1*2+spin, i*4*2+1*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
- h01[i*4*2+2*2+spin, i*4*2+2*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
- h01[i*4*2+3*2+spin, i*4*2+3*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
-
- h01[i*4*2+1*2+spin, i*4*2+3*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
- h01[i*4*2+2*2+spin, i*4*2+0*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
-
- if i != 0:
- h01[i*4*2+1*2+spin, (i-1)*4*2+3*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
-
- for i in range(N-1):
- h01[i*4*2+2*2+spin, (i+1)*4*2+0*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
-
- matrix = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
- return matrix
-
-
-def sign_spin(spin):
- if spin==0:
- sign=1
- else:
- sign=-1
- return sign
-
-
-def main():
- hamiltonian0 = functools.partial(hamiltonian, N=20, M=0, t1=1, t2=0.03, phi=pi/2)
- k = np.linspace(0, 2*pi, 300)
- plot_bands_one_dimension(k, hamiltonian0)
-
-
-def plot_bands_one_dimension(k, hamiltonian):
- dim = hamiltonian(0).shape[0]
- dim_k = k.shape[0]
- eigenvalue_k = np.zeros((dim_k, dim))
- i0 = 0
- for k0 in k:
- matrix0 = hamiltonian(k0)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
- i0 += 1
- for dim0 in range(dim):
- plt.plot(k, eigenvalue_k[:, dim0], '-k')
- plt.ylim(-1, 1)
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4829
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import functools
+
+
+def hamiltonian(k, N, M, t1, t2, phi): # Kane-Mele model
+ # 初始化为零矩阵
+ h00 = np.zeros((2*4*N, 2*4*N), dtype=complex) # 因为自旋有上有下，所以整个维度要乘2。这里4是元胞内部重复单位的大小，规定元胞大小以4来倍增。
+ h01 = np.zeros((2*4*N, 2*4*N), dtype=complex)
+
+ for spin in range(2):
+ # 原胞内的跃迁h00
+ for i in range(N):
+ # 最近邻
+ h00[i*4*2+0*2+spin, i*4*2+1*2+spin] = t1
+ h00[i*4*2+1*2+spin, i*4*2+0*2+spin] = t1
+
+ h00[i*4*2+1*2+spin, i*4*2+2*2+spin] = t1
+ h00[i*4*2+2*2+spin, i*4*2+1*2+spin] = t1
+
+ h00[i*4*2+2*2+spin, i*4*2+3*2+spin] = t1
+ h00[i*4*2+3*2+spin, i*4*2+2*2+spin] = t1
+
+ # 次近邻
+ h00[i*4*2+0*2+spin, i*4*2+2*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
+ h00[i*4*2+2*2+spin, i*4*2+0*2+spin] = h00[i*4*2+0*2+spin, i*4*2+2*2+spin].conj()
+ h00[i*4*2+1*2+spin, i*4*2+3*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
+ h00[i*4*2+3*2+spin, i*4*2+1*2+spin] = h00[i*4*2+1*2+spin, i*4*2+3*2+spin].conj()
+
+ for i in range(N-1):
+ # 最近邻
+ h00[i*4*2+3*2+spin, (i+1)*4*2+0*2+spin] = t1
+ h00[(i+1)*4*2+0*2+spin, i*4*2+3*2+spin] = t1
+
+ # 次近邻
+ h00[i*4*2+2*2+spin, (i+1)*4*2+0*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
+ h00[(i+1)*4*2+0*2+spin, i*4*2+2*2+spin] = h00[i*4*2+2*2+spin, (i+1)*4*2+0*2+spin].conj()
+ h00[i*4*2+3*2+spin, (i+1)*4*2+1*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
+ h00[(i+1)*4*2+1*2+spin, i*4*2+3*2+spin] = h00[i*4*2+3*2+spin, (i+1)*4*2+1*2+spin].conj()
+
+ # 原胞间的跃迁h01
+ for i in range(N):
+ # 最近邻
+ h01[i*4*2+1*2+spin, i*4*2+0*2+spin] = t1
+ h01[i*4*2+2*2+spin, i*4*2+3*2+spin] = t1
+
+ # 次近邻
+ h01[i*4*2+0*2+spin, i*4*2+0*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
+ h01[i*4*2+1*2+spin, i*4*2+1*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
+ h01[i*4*2+2*2+spin, i*4*2+2*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
+ h01[i*4*2+3*2+spin, i*4*2+3*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
+
+ h01[i*4*2+1*2+spin, i*4*2+3*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
+ h01[i*4*2+2*2+spin, i*4*2+0*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
+
+ if i != 0:
+ h01[i*4*2+1*2+spin, (i-1)*4*2+3*2+spin] = t2*cmath.exp(1j*phi)*sign_spin(spin)
+
+ for i in range(N-1):
+ h01[i*4*2+2*2+spin, (i+1)*4*2+0*2+spin] = t2*cmath.exp(-1j*phi)*sign_spin(spin)
+
+ matrix = h00 + h01*cmath.exp(1j*k) + h01.transpose().conj()*cmath.exp(-1j*k)
+ return matrix
+
+
+def sign_spin(spin):
+ if spin==0:
+ sign=1
+ else:
+ sign=-1
+ return sign
+
+
+def main():
+ hamiltonian0 = functools.partial(hamiltonian, N=20, M=0, t1=1, t2=0.03, phi=pi/2)
+ k = np.linspace(0, 2*pi, 300)
+ plot_bands_one_dimension(k, hamiltonian0)
+
+
+def plot_bands_one_dimension(k, hamiltonian):
+ dim = hamiltonian(0).shape[0]
+ dim_k = k.shape[0]
+ eigenvalue_k = np.zeros((dim_k, dim))
+ i0 = 0
+ for k0 in k:
+ matrix0 = hamiltonian(k0)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
+ i0 += 1
+ for dim0 in range(dim):
+ plt.plot(k, eigenvalue_k[:, dim0], '-k')
+ plt.ylim(-1, 1)
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_direct_assigning.py b/academic_codes/models_and_bands/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_direct_assigning.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_direct_assigning.py
rename to academic_codes/models_and_bands/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_direct_assigning.py
index 88ce253..8b5b3e2
--- a/academic_codes/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_direct_assigning.py
+++ b/academic_codes/models_and_bands/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_direct_assigning.py
@@ -1,23 +1,23 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5375
-"""
-
-import numpy as np
-
-def hamiltonian(width=3, length=3): # 方格子哈密顿量
- h = np.zeros((width*length, width*length))
- # y方向的跃迁
- for x in range(length):
- for y in range(width-1):
- h[x*width+y, x*width+y+1] = 1
- h[x*width+y+1, x*width+y] = 1
- # x方向的跃迁
- for x in range(length-1):
- for y in range(width):
- h[x*width+y, (x+1)*width+y] = 1
- h[(x+1)*width+y, x*width+y] = 1
- return h
-
-h = hamiltonian()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5375
+"""
+
+import numpy as np
+
+def hamiltonian(width=3, length=3): # 方格子哈密顿量
+ h = np.zeros((width*length, width*length))
+ # y方向的跃迁
+ for x in range(length):
+ for y in range(width-1):
+ h[x*width+y, x*width+y+1] = 1
+ h[x*width+y+1, x*width+y] = 1
+ # x方向的跃迁
+ for x in range(length-1):
+ for y in range(width):
+ h[x*width+y, (x+1)*width+y] = 1
+ h[(x+1)*width+y, x*width+y] = 1
+ return h
+
+h = hamiltonian()
print(h)
\ No newline at end of file
diff --git a/academic_codes/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_tensor_product.py b/academic_codes/models_and_bands/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_tensor_product.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_tensor_product.py
rename to academic_codes/models_and_bands/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_tensor_product.py
index 43b72cd..5780f4a
--- a/academic_codes/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_tensor_product.py
+++ b/academic_codes/models_and_bands/2020.08.10_Hamiltonian_form_of_square_lattice_in_real_space/form_of_tensor_product.py
@@ -1,21 +1,21 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5375
-"""
-
-import numpy as np
-
-def hamiltonian(width=3, length=3): # 方格子哈密顿量
- hopping_x = np.zeros((length, length))
- hopping_y = np.zeros((width, width))
- for i in range(length-1):
- hopping_x[i, i+1] = 1
- hopping_x[i+1, i] = 1
- for i in range(width-1):
- hopping_y[i, i+1] = 1
- hopping_y[i+1, i] = 1
- h = np.kron(hopping_x, np.eye(width))+np.kron(np.eye(length), hopping_y)
- return h
-
-h = hamiltonian()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5375
+"""
+
+import numpy as np
+
+def hamiltonian(width=3, length=3): # 方格子哈密顿量
+ hopping_x = np.zeros((length, length))
+ hopping_y = np.zeros((width, width))
+ for i in range(length-1):
+ hopping_x[i, i+1] = 1
+ hopping_x[i+1, i] = 1
+ for i in range(width-1):
+ hopping_y[i, i+1] = 1
+ hopping_y[i+1, i] = 1
+ h = np.kron(hopping_x, np.eye(width))+np.kron(np.eye(length), hopping_y)
+ return h
+
+h = hamiltonian()
print(h)
\ No newline at end of file
diff --git a/academic_codes/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/Fermi_arc_in_Weyl_semimetals.py b/academic_codes/models_and_bands/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/Fermi_arc_in_Weyl_semimetals.py
old mode 100755
new mode 100644
similarity index 95%
rename from academic_codes/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/Fermi_arc_in_Weyl_semimetals.py
rename to academic_codes/models_and_bands/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/Fermi_arc_in_Weyl_semimetals.py
index 8ee5b9e..08327a3
--- a/academic_codes/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/Fermi_arc_in_Weyl_semimetals.py
+++ b/academic_codes/models_and_bands/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/Fermi_arc_in_Weyl_semimetals.py
@@ -1,64 +1,64 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6077
-"""
-
-import numpy as np
-from math import *
-import matplotlib.pyplot as plt
-
-
-def main():
- n = 0.5
- k1 = np.arange(-n*pi, n*pi, n/50)
- k2 = np.arange(-n*pi, n*pi, n/50)
- plot_bands_two_dimension_direct(k1, k2, hamiltonian)
-
-
-def hamiltonian(kx,kz,ky=0): # surface states of Weyl semimetal
- A = 1
- H = A*kx
- return H
-
-
-def sigma_x():
- return np.array([[0, 1],[1, 0]])
-
-
-def sigma_y():
- return np.array([[0, -1j],[1j, 0]])
-
-
-def sigma_z():
- return np.array([[1, 0],[0, -1]])
-
-
-def plot_bands_two_dimension_direct(k1, k2, hamiltonian):
- from mpl_toolkits.mplot3d import Axes3D
- from matplotlib import cm
- from matplotlib.ticker import LinearLocator, FormatStrFormatter
- fig = plt.figure()
- ax = fig.gca(projection='3d')
- dim1 = k1.shape[0]
- dim2 = k2.shape[0]
- eigenvalue_k = np.zeros((dim2, dim1))
- i0 = 0
- for k10 in k1:
- j0 = 0
- for k20 in k2:
- if (k10**2+k20**2 <= 1):
- eigenvalue_k[j0, i0] = hamiltonian(k10, k20)
- else:
- eigenvalue_k[j0, i0] = 'nan'
- j0 += 1
- i0 += 1
- k1, k2 = np.meshgrid(k1, k2)
- ax.scatter(k1, k2, eigenvalue_k)
- plt.xlabel('kx')
- plt.ylabel('kz')
- ax.set_zlabel('E')
- plt.show()
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6077
+"""
+
+import numpy as np
+from math import *
+import matplotlib.pyplot as plt
+
+
+def main():
+ n = 0.5
+ k1 = np.arange(-n*pi, n*pi, n/50)
+ k2 = np.arange(-n*pi, n*pi, n/50)
+ plot_bands_two_dimension_direct(k1, k2, hamiltonian)
+
+
+def hamiltonian(kx,kz,ky=0): # surface states of Weyl semimetal
+ A = 1
+ H = A*kx
+ return H
+
+
+def sigma_x():
+ return np.array([[0, 1],[1, 0]])
+
+
+def sigma_y():
+ return np.array([[0, -1j],[1j, 0]])
+
+
+def sigma_z():
+ return np.array([[1, 0],[0, -1]])
+
+
+def plot_bands_two_dimension_direct(k1, k2, hamiltonian):
+ from mpl_toolkits.mplot3d import Axes3D
+ from matplotlib import cm
+ from matplotlib.ticker import LinearLocator, FormatStrFormatter
+ fig = plt.figure()
+ ax = fig.gca(projection='3d')
+ dim1 = k1.shape[0]
+ dim2 = k2.shape[0]
+ eigenvalue_k = np.zeros((dim2, dim1))
+ i0 = 0
+ for k10 in k1:
+ j0 = 0
+ for k20 in k2:
+ if (k10**2+k20**2 <= 1):
+ eigenvalue_k[j0, i0] = hamiltonian(k10, k20)
+ else:
+ eigenvalue_k[j0, i0] = 'nan'
+ j0 += 1
+ i0 += 1
+ k1, k2 = np.meshgrid(k1, k2)
+ ax.scatter(k1, k2, eigenvalue_k)
+ plt.xlabel('kx')
+ plt.ylabel('kz')
+ ax.set_zlabel('E')
+ plt.show()
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/bands_of_Weyl_semimetals.py b/academic_codes/models_and_bands/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/bands_of_Weyl_semimetals.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/bands_of_Weyl_semimetals.py
rename to academic_codes/models_and_bands/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/bands_of_Weyl_semimetals.py
index 8ac9553..a30a4f7
--- a/academic_codes/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/bands_of_Weyl_semimetals.py
+++ b/academic_codes/models_and_bands/2020.09.29_Halmiltonian_and_Fermi_arc_in_Weyl_semimetals/bands_of_Weyl_semimetals.py
@@ -1,67 +1,67 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6077
-"""
-
-import numpy as np
-from math import *
-import matplotlib.pyplot as plt
-
-
-def main():
- n = 0.5
- k1 = np.arange(-n*pi, n*pi, n/20)
- k2 = np.arange(-n*pi, n*pi, n/20)
- plot_bands_two_dimension(k1, k2, hamiltonian)
-
-
-def hamiltonian(kx,kz,ky=0): # Weyl semimetal
- A = 1
- M0 = 1
- M1 = 1
- H = A*(kx*sigma_x()+ky*sigma_y())+(M0-M1*(kx**2+ky**2+kz**2))*sigma_z()
- return H
-
-
-def sigma_x():
- return np.array([[0, 1],[1, 0]])
-
-
-def sigma_y():
- return np.array([[0, -1j],[1j, 0]])
-
-
-def sigma_z():
- return np.array([[1, 0],[0, -1]])
-
-
-def plot_bands_two_dimension(k1, k2, hamiltonian):
- from mpl_toolkits.mplot3d import Axes3D
- from matplotlib import cm
- from matplotlib.ticker import LinearLocator, FormatStrFormatter
- dim = hamiltonian(0, 0).shape[0]
- dim1 = k1.shape[0]
- dim2 = k2.shape[0]
- eigenvalue_k = np.zeros((dim2, dim1, dim))
- i0 = 0
- for k10 in k1:
- j0 = 0
- for k20 in k2:
- matrix0 = hamiltonian(k10, k20)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[j0, i0, :] = np.sort(np.real(eigenvalue[:]))
- j0 += 1
- i0 += 1
- fig = plt.figure()
- ax = fig.gca(projection='3d')
- k1, k2 = np.meshgrid(k1, k2)
- for dim0 in range(dim):
- ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
- plt.xlabel('kx')
- plt.ylabel('kz')
- ax.set_zlabel('E')
- plt.show()
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6077
+"""
+
+import numpy as np
+from math import *
+import matplotlib.pyplot as plt
+
+
+def main():
+ n = 0.5
+ k1 = np.arange(-n*pi, n*pi, n/20)
+ k2 = np.arange(-n*pi, n*pi, n/20)
+ plot_bands_two_dimension(k1, k2, hamiltonian)
+
+
+def hamiltonian(kx,kz,ky=0): # Weyl semimetal
+ A = 1
+ M0 = 1
+ M1 = 1
+ H = A*(kx*sigma_x()+ky*sigma_y())+(M0-M1*(kx**2+ky**2+kz**2))*sigma_z()
+ return H
+
+
+def sigma_x():
+ return np.array([[0, 1],[1, 0]])
+
+
+def sigma_y():
+ return np.array([[0, -1j],[1j, 0]])
+
+
+def sigma_z():
+ return np.array([[1, 0],[0, -1]])
+
+
+def plot_bands_two_dimension(k1, k2, hamiltonian):
+ from mpl_toolkits.mplot3d import Axes3D
+ from matplotlib import cm
+ from matplotlib.ticker import LinearLocator, FormatStrFormatter
+ dim = hamiltonian(0, 0).shape[0]
+ dim1 = k1.shape[0]
+ dim2 = k2.shape[0]
+ eigenvalue_k = np.zeros((dim2, dim1, dim))
+ i0 = 0
+ for k10 in k1:
+ j0 = 0
+ for k20 in k2:
+ matrix0 = hamiltonian(k10, k20)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[j0, i0, :] = np.sort(np.real(eigenvalue[:]))
+ j0 += 1
+ i0 += 1
+ fig = plt.figure()
+ ax = fig.gca(projection='3d')
+ k1, k2 = np.meshgrid(k1, k2)
+ for dim0 in range(dim):
+ ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ plt.xlabel('kx')
+ plt.ylabel('kz')
+ ax.set_zlabel('E')
+ plt.show()
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.09.30_bandstructure_of_graphene_on_high_symmetric_axes/bandstructure_of_graphene_on_high_symmetric_axes.py b/academic_codes/models_and_bands/2020.09.30_bandstructure_of_graphene_on_high_symmetric_axes/bandstructure_of_graphene_on_high_symmetric_axes.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.09.30_bandstructure_of_graphene_on_high_symmetric_axes/bandstructure_of_graphene_on_high_symmetric_axes.py
rename to academic_codes/models_and_bands/2020.09.30_bandstructure_of_graphene_on_high_symmetric_axes/bandstructure_of_graphene_on_high_symmetric_axes.py
index ee20732..a7c20c4
--- a/academic_codes/2020.09.30_bandstructure_of_graphene_on_high_symmetric_axes/bandstructure_of_graphene_on_high_symmetric_axes.py
+++ b/academic_codes/models_and_bands/2020.09.30_bandstructure_of_graphene_on_high_symmetric_axes/bandstructure_of_graphene_on_high_symmetric_axes.py
@@ -1,77 +1,77 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6260
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import * # 引入sqrt(), pi, exp等
-import cmath # 要处理复数情况，用到cmath.exp()
-
-
-def hamiltonian(k1, k2, M=0, t1=1, a=1/sqrt(3)): # Haldane哈密顿量(a为原子间距，不赋值的话默认为1/sqrt(3)）
- h0 = np.zeros((2, 2), dtype=complex)
- h1 = np.zeros((2, 2), dtype=complex)
- h2 = np.zeros((2, 2), dtype=complex)
-
- # 质量项(mass term), 用于打开带隙
- h0[0, 0] = M
- h0[1, 1] = -M
-
- # 最近邻项
- h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
- h1[0, 1] = h1[1, 0].conj()
-
- # # 最近邻项也可写成这种形式
- # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
- # h1[0, 1] = h1[1, 0].conj()
-
- matrix = h0 + h1
- return matrix
-
-
-def main():
- a = 1/sqrt(3)
- Gamma0 = np.array([0, 0])
- M0 = np.array([0, 2*pi/3/a])
- K0 = np.array([2*np.sqrt(3)*pi/9/a, 2*pi/3/a])
-
- kn = 100 # 每个区域的取点数
- n = 3 # n个区域（K-Gamma，Gamma-M, M-K）
- k1_array = np.zeros(kn*n)
- k2_array = np.zeros(kn*n)
-
- # K-Gamma轴
- k1_array[0:kn] = np.linspace(0, K0[0], kn)[::-1] # [::-1]表示反转数组
- k2_array[0:kn] = np.linspace(0, K0[1], kn)[::-1]
-
- # Gamma-M轴
- k1_array[kn:2*kn] = np.zeros(kn)
- k2_array[kn:2*kn] = np.linspace(0, M0[1], kn)
-
- # M-K轴
- k1_array[2*kn:3*kn] = np.linspace(0, K0[0], kn)
- k2_array[2*kn:3*kn] = np.ones(kn)*M0[1]
-
- i0 = 0
- dim = hamiltonian(0, 0).shape[0]
- eigenvalue_k = np.zeros((kn*n, dim))
- fig, ax = plt.subplots()
- for kn0 in range(kn*n):
- k1 = k1_array[kn0]
- k2 = k2_array[kn0]
- eigenvalue, eigenvector = np.linalg.eig(hamiltonian(k1, k2))
- eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
- i0 += 1
- for dim0 in range(dim):
- plt.plot(range(kn*n), eigenvalue_k[:, dim0], '-k')
- plt.ylabel('E')
- ax.set_xticks([0, kn, 2*kn, 3*kn])
- ax.set_xticklabels(['K', 'Gamma', 'M', 'K'])
- plt.xlim(0, n*kn)
- plt.grid(axis='x',c='r',linestyle='--')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6260
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import * # 引入sqrt(), pi, exp等
+import cmath # 要处理复数情况，用到cmath.exp()
+
+
+def hamiltonian(k1, k2, M=0, t1=1, a=1/sqrt(3)): # Haldane哈密顿量(a为原子间距，不赋值的话默认为1/sqrt(3)）
+ h0 = np.zeros((2, 2), dtype=complex)
+ h1 = np.zeros((2, 2), dtype=complex)
+ h2 = np.zeros((2, 2), dtype=complex)
+
+ # 质量项(mass term), 用于打开带隙
+ h0[0, 0] = M
+ h0[1, 1] = -M
+
+ # 最近邻项
+ h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
+ h1[0, 1] = h1[1, 0].conj()
+
+ # # 最近邻项也可写成这种形式
+ # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
+ # h1[0, 1] = h1[1, 0].conj()
+
+ matrix = h0 + h1
+ return matrix
+
+
+def main():
+ a = 1/sqrt(3)
+ Gamma0 = np.array([0, 0])
+ M0 = np.array([0, 2*pi/3/a])
+ K0 = np.array([2*np.sqrt(3)*pi/9/a, 2*pi/3/a])
+
+ kn = 100 # 每个区域的取点数
+ n = 3 # n个区域（K-Gamma，Gamma-M, M-K）
+ k1_array = np.zeros(kn*n)
+ k2_array = np.zeros(kn*n)
+
+ # K-Gamma轴
+ k1_array[0:kn] = np.linspace(0, K0[0], kn)[::-1] # [::-1]表示反转数组
+ k2_array[0:kn] = np.linspace(0, K0[1], kn)[::-1]
+
+ # Gamma-M轴
+ k1_array[kn:2*kn] = np.zeros(kn)
+ k2_array[kn:2*kn] = np.linspace(0, M0[1], kn)
+
+ # M-K轴
+ k1_array[2*kn:3*kn] = np.linspace(0, K0[0], kn)
+ k2_array[2*kn:3*kn] = np.ones(kn)*M0[1]
+
+ i0 = 0
+ dim = hamiltonian(0, 0).shape[0]
+ eigenvalue_k = np.zeros((kn*n, dim))
+ fig, ax = plt.subplots()
+ for kn0 in range(kn*n):
+ k1 = k1_array[kn0]
+ k2 = k2_array[kn0]
+ eigenvalue, eigenvector = np.linalg.eig(hamiltonian(k1, k2))
+ eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
+ i0 += 1
+ for dim0 in range(dim):
+ plt.plot(range(kn*n), eigenvalue_k[:, dim0], '-k')
+ plt.ylabel('E')
+ ax.set_xticks([0, kn, 2*kn, 3*kn])
+ ax.set_xticklabels(['K', 'Gamma', 'M', 'K'])
+ plt.xlim(0, n*kn)
+ plt.grid(axis='x',c='r',linestyle='--')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator.py b/academic_codes/models_and_bands/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator.py
rename to academic_codes/models_and_bands/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator.py
index daa5742..c048b7c
--- a/academic_codes/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator.py
+++ b/academic_codes/models_and_bands/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator.py
@@ -1,100 +1,100 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8557
-"""
-
-import numpy as np
-from math import *
-
-
-def hamiltonian(Nx, Ny):
- delta = 1e-3
- gamma = 1e-3
- lambda0 = 1
- h = np.zeros((4*Nx*Ny, 4*Nx*Ny))
- # 元胞内部跃迁
- for x in range(Nx):
- for y in range(Ny):
- h[x*Ny*4+y*4+0, x*Ny*4+y*4+0] = delta
- h[x*Ny*4+y*4+1, x*Ny*4+y*4+1] = delta
- h[x*Ny*4+y*4+2, x*Ny*4+y*4+2] = -delta
- h[x*Ny*4+y*4+3, x*Ny*4+y*4+3] = -delta
-
- h[x*Ny*4+y*4+0, x*Ny*4+y*4+2] = gamma
- h[x*Ny*4+y*4+0, x*Ny*4+y*4+3] = gamma
- h[x*Ny*4+y*4+1, x*Ny*4+y*4+2] = -gamma
- h[x*Ny*4+y*4+1, x*Ny*4+y*4+3] = gamma
- h[x*Ny*4+y*4+2, x*Ny*4+y*4+0] = gamma
- h[x*Ny*4+y*4+2, x*Ny*4+y*4+1] = -gamma
- h[x*Ny*4+y*4+3, x*Ny*4+y*4+0] = gamma
- h[x*Ny*4+y*4+3, x*Ny*4+y*4+1] = gamma
-
- # y方向上的元胞间跃迁
- for x in range(Nx):
- for y in range(Ny-1):
- h[x*Ny*4+y*4+0, x*Ny*4+(y+1)*4+3] = lambda0
- h[x*Ny*4+(y+1)*4+1, x*Ny*4+y*4+2] = -lambda0
- h[x*Ny*4+y*4+2, x*Ny*4+(y+1)*4+1] = -lambda0
- h[x*Ny*4+(y+1)*4+3, x*Ny*4+y*4+0] = lambda0
-
- # x方向上的元胞间跃迁
- for x in range(Nx-1):
- for y in range(Ny):
- h[x*Ny*4+y*4+0, (x+1)*Ny*4+y*4+2] = lambda0
- h[(x+1)*Ny*4+y*4+1, x*Ny*4+y*4+3] = lambda0
- h[(x+1)*Ny*4+y*4+2, x*Ny*4+y*4+0] = lambda0
- h[x*Ny*4+y*4+3, (x+1)*Ny*4+y*4+1] = lambda0
- return h
-
-
-def main():
- Nx = 10
- Ny = 10
- fermi_energy = 0
- h = hamiltonian(Nx, Ny)
- green = np.linalg.inv((fermi_energy+1e-6j)*np.eye(h.shape[0])-h)
-
- x_array = []
- y_array = []
- DOS = []
- for x in range(Nx):
- for y in range(Ny):
- x_array.append(x*2+2)
- y_array.append(y*2+2)
- DOS.append(-np.imag(green[x*Ny*4+y*4+0, x*Ny*4+y*4+0])/pi)
-
- x_array.append(x*2+1)
- y_array.append(y*2+1)
- DOS.append(-np.imag(green[x*Ny*4+y*4+1, x*Ny*4+y*4+1])/pi)
-
- x_array.append(x*2+1)
- y_array.append(y*2+2)
- DOS.append(-np.imag(green[x*Ny*4+y*4+2, x*Ny*4+y*4+2])/pi)
-
- x_array.append(x*2+2)
- y_array.append(y*2+1)
- DOS.append(-np.imag(green[x*Ny*4+y*4+3, x*Ny*4+y*4+3])/pi)
- DOS = DOS/np.sum(DOS)
- Plot_2D_Scatter(x_array, y_array, DOS, xlabel='x', ylabel='y', title='BBH Model', filename='BBH Model')
-
-
-def Plot_2D_Scatter(x, y, value, xlabel='x', ylabel='y', title='title', filename='a'):
- import matplotlib.pyplot as plt
- fig = plt.figure()
- ax = fig.add_subplot(111)
- plt.subplots_adjust(bottom=0.2, right=0.8, left=0.2)
- for i in range(np.array(x).shape[0]):
- ax.scatter(x[i], y[i], marker='o', s=1000*value[i], c=[(1,0,0)])
- ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
- ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
- ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
- ax.tick_params(labelsize=15) # 设置刻度值字体大小
- labels = ax.get_xticklabels() + ax.get_yticklabels()
- [label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
- # plt.savefig(filename+'.jpg', dpi=300)
- plt.show()
- plt.close('all')
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8557
+"""
+
+import numpy as np
+from math import *
+
+
+def hamiltonian(Nx, Ny):
+ delta = 1e-3
+ gamma = 1e-3
+ lambda0 = 1
+ h = np.zeros((4*Nx*Ny, 4*Nx*Ny))
+ # 元胞内部跃迁
+ for x in range(Nx):
+ for y in range(Ny):
+ h[x*Ny*4+y*4+0, x*Ny*4+y*4+0] = delta
+ h[x*Ny*4+y*4+1, x*Ny*4+y*4+1] = delta
+ h[x*Ny*4+y*4+2, x*Ny*4+y*4+2] = -delta
+ h[x*Ny*4+y*4+3, x*Ny*4+y*4+3] = -delta
+
+ h[x*Ny*4+y*4+0, x*Ny*4+y*4+2] = gamma
+ h[x*Ny*4+y*4+0, x*Ny*4+y*4+3] = gamma
+ h[x*Ny*4+y*4+1, x*Ny*4+y*4+2] = -gamma
+ h[x*Ny*4+y*4+1, x*Ny*4+y*4+3] = gamma
+ h[x*Ny*4+y*4+2, x*Ny*4+y*4+0] = gamma
+ h[x*Ny*4+y*4+2, x*Ny*4+y*4+1] = -gamma
+ h[x*Ny*4+y*4+3, x*Ny*4+y*4+0] = gamma
+ h[x*Ny*4+y*4+3, x*Ny*4+y*4+1] = gamma
+
+ # y方向上的元胞间跃迁
+ for x in range(Nx):
+ for y in range(Ny-1):
+ h[x*Ny*4+y*4+0, x*Ny*4+(y+1)*4+3] = lambda0
+ h[x*Ny*4+(y+1)*4+1, x*Ny*4+y*4+2] = -lambda0
+ h[x*Ny*4+y*4+2, x*Ny*4+(y+1)*4+1] = -lambda0
+ h[x*Ny*4+(y+1)*4+3, x*Ny*4+y*4+0] = lambda0
+
+ # x方向上的元胞间跃迁
+ for x in range(Nx-1):
+ for y in range(Ny):
+ h[x*Ny*4+y*4+0, (x+1)*Ny*4+y*4+2] = lambda0
+ h[(x+1)*Ny*4+y*4+1, x*Ny*4+y*4+3] = lambda0
+ h[(x+1)*Ny*4+y*4+2, x*Ny*4+y*4+0] = lambda0
+ h[x*Ny*4+y*4+3, (x+1)*Ny*4+y*4+1] = lambda0
+ return h
+
+
+def main():
+ Nx = 10
+ Ny = 10
+ fermi_energy = 0
+ h = hamiltonian(Nx, Ny)
+ green = np.linalg.inv((fermi_energy+1e-6j)*np.eye(h.shape[0])-h)
+
+ x_array = []
+ y_array = []
+ DOS = []
+ for x in range(Nx):
+ for y in range(Ny):
+ x_array.append(x*2+2)
+ y_array.append(y*2+2)
+ DOS.append(-np.imag(green[x*Ny*4+y*4+0, x*Ny*4+y*4+0])/pi)
+
+ x_array.append(x*2+1)
+ y_array.append(y*2+1)
+ DOS.append(-np.imag(green[x*Ny*4+y*4+1, x*Ny*4+y*4+1])/pi)
+
+ x_array.append(x*2+1)
+ y_array.append(y*2+2)
+ DOS.append(-np.imag(green[x*Ny*4+y*4+2, x*Ny*4+y*4+2])/pi)
+
+ x_array.append(x*2+2)
+ y_array.append(y*2+1)
+ DOS.append(-np.imag(green[x*Ny*4+y*4+3, x*Ny*4+y*4+3])/pi)
+ DOS = DOS/np.sum(DOS)
+ Plot_2D_Scatter(x_array, y_array, DOS, xlabel='x', ylabel='y', title='BBH Model', filename='BBH Model')
+
+
+def Plot_2D_Scatter(x, y, value, xlabel='x', ylabel='y', title='title', filename='a'):
+ import matplotlib.pyplot as plt
+ fig = plt.figure()
+ ax = fig.add_subplot(111)
+ plt.subplots_adjust(bottom=0.2, right=0.8, left=0.2)
+ for i in range(np.array(x).shape[0]):
+ ax.scatter(x[i], y[i], marker='o', s=1000*value[i], c=[(1,0,0)])
+ ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
+ ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
+ ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
+ ax.tick_params(labelsize=15) # 设置刻度值字体大小
+ labels = ax.get_xticklabels() + ax.get_yticklabels()
+ [label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
+ # plt.savefig(filename+'.jpg', dpi=300)
+ plt.show()
+ plt.close('all')
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator_plotting_3D_surface.py b/academic_codes/models_and_bands/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator_plotting_3D_surface.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator_plotting_3D_surface.py
rename to academic_codes/models_and_bands/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator_plotting_3D_surface.py
index f49b51a..b7f378c
--- a/academic_codes/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator_plotting_3D_surface.py
+++ b/academic_codes/models_and_bands/2021.01.23_BBH_model_of_high_order_topological_insulator/BBH_model_of_high_order_topological_insulator_plotting_3D_surface.py
@@ -1,100 +1,100 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8557
-"""
-
-import numpy as np
-from math import *
-
-
-def hamiltonian(Nx, Ny):
- delta = 1e-3
- gamma = 1e-3
- lambda0 = 1
- h = np.zeros((4*Nx*Ny, 4*Nx*Ny))
- # 元胞内部跃迁
- for x in range(Nx):
- for y in range(Ny):
- h[x*Ny*4+y*4+0, x*Ny*4+y*4+0] = delta
- h[x*Ny*4+y*4+1, x*Ny*4+y*4+1] = delta
- h[x*Ny*4+y*4+2, x*Ny*4+y*4+2] = -delta
- h[x*Ny*4+y*4+3, x*Ny*4+y*4+3] = -delta
-
- h[x*Ny*4+y*4+0, x*Ny*4+y*4+2] = gamma
- h[x*Ny*4+y*4+0, x*Ny*4+y*4+3] = gamma
- h[x*Ny*4+y*4+1, x*Ny*4+y*4+2] = -gamma
- h[x*Ny*4+y*4+1, x*Ny*4+y*4+3] = gamma
- h[x*Ny*4+y*4+2, x*Ny*4+y*4+0] = gamma
- h[x*Ny*4+y*4+2, x*Ny*4+y*4+1] = -gamma
- h[x*Ny*4+y*4+3, x*Ny*4+y*4+0] = gamma
- h[x*Ny*4+y*4+3, x*Ny*4+y*4+1] = gamma
-
- # y方向上的元胞间跃迁
- for x in range(Nx):
- for y in range(Ny-1):
- h[x*Ny*4+y*4+0, x*Ny*4+(y+1)*4+3] = lambda0
- h[x*Ny*4+(y+1)*4+1, x*Ny*4+y*4+2] = -lambda0
- h[x*Ny*4+y*4+2, x*Ny*4+(y+1)*4+1] = -lambda0
- h[x*Ny*4+(y+1)*4+3, x*Ny*4+y*4+0] = lambda0
-
- # x方向上的元胞间跃迁
- for x in range(Nx-1):
- for y in range(Ny):
- h[x*Ny*4+y*4+0, (x+1)*Ny*4+y*4+2] = lambda0
- h[(x+1)*Ny*4+y*4+1, x*Ny*4+y*4+3] = lambda0
- h[(x+1)*Ny*4+y*4+2, x*Ny*4+y*4+0] = lambda0
- h[x*Ny*4+y*4+3, (x+1)*Ny*4+y*4+1] = lambda0
- return h
-
-
-def main():
- Nx = 10
- Ny = 10
- fermi_energy = 0
- h = hamiltonian(Nx, Ny)
- green = np.linalg.inv((fermi_energy+1e-6j)*np.eye(h.shape[0])-h)
- DOS = np.zeros((Ny*2, Nx*2))
- for x in range(Nx):
- for y in range(Ny):
- DOS[y*2+1, x*2+1] = -np.imag(green[x*Ny*4+y*4+0, x*Ny*4+y*4+0])/pi
- DOS[y*2+0, x*2+0] = -np.imag(green[x*Ny*4+y*4+1, x*Ny*4+y*4+1])/pi
- DOS[y*2+1, x*2+0] = -np.imag(green[x*Ny*4+y*4+2, x*Ny*4+y*4+2])/pi
- DOS[y*2+0, x*2+1] = -np.imag(green[x*Ny*4+y*4+3, x*Ny*4+y*4+3])/pi
- DOS = DOS/np.sum(DOS)
- Plot_3D_Surface(np.arange(1, 2*Nx+0.001), np.arange(1, 2*Ny+0.001), DOS, xlabel='x', ylabel='y', zlabel='DOS', title='BBH Model', filename='BBH Model')
-
-
-def Plot_3D_Surface(x, y, matrix, xlabel='x', ylabel='y', zlabel='z', title='title', filename='a'):
- import matplotlib.pyplot as plt
- from matplotlib import cm
- from matplotlib.ticker import LinearLocator
- fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
- plt.subplots_adjust(bottom=0.1, right=0.65)
- x, y = np.meshgrid(x, y)
- if len(matrix.shape) == 2:
- surf = ax.plot_surface(x, y, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
- elif len(matrix.shape) == 3:
- for i0 in range(matrix.shape[2]):
- surf = ax.plot_surface(x, y, matrix[:,:,i0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
- ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
- ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
- ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
- ax.set_zlabel(zlabel, fontsize=20, fontfamily='Times New Roman')
- # ax.set_zlim(-1, 1) # 设置z轴的范围
- ax.zaxis.set_major_locator(LinearLocator(2)) # 设置z轴主刻度的个数
- ax.zaxis.set_major_formatter('{x:.2f}') # 设置z轴主刻度的格式
- ax.tick_params(labelsize=15) # 设置刻度值字体大小
- labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
- [label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
- cax = plt.axes([0.80, 0.15, 0.05, 0.75]) # color bar的位置 [左，下，宽度， 高度]
- cbar = fig.colorbar(surf, cax=cax) # color bar
- cbar.ax.tick_params(labelsize=15) # 设置color bar刻度的字体大小
- for l in cbar.ax.yaxis.get_ticklabels():
- l.set_family('Times New Roman')
- # plt.savefig(filename+'.jpg', dpi=300)
- plt.show()
- plt.close('all')
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8557
+"""
+
+import numpy as np
+from math import *
+
+
+def hamiltonian(Nx, Ny):
+ delta = 1e-3
+ gamma = 1e-3
+ lambda0 = 1
+ h = np.zeros((4*Nx*Ny, 4*Nx*Ny))
+ # 元胞内部跃迁
+ for x in range(Nx):
+ for y in range(Ny):
+ h[x*Ny*4+y*4+0, x*Ny*4+y*4+0] = delta
+ h[x*Ny*4+y*4+1, x*Ny*4+y*4+1] = delta
+ h[x*Ny*4+y*4+2, x*Ny*4+y*4+2] = -delta
+ h[x*Ny*4+y*4+3, x*Ny*4+y*4+3] = -delta
+
+ h[x*Ny*4+y*4+0, x*Ny*4+y*4+2] = gamma
+ h[x*Ny*4+y*4+0, x*Ny*4+y*4+3] = gamma
+ h[x*Ny*4+y*4+1, x*Ny*4+y*4+2] = -gamma
+ h[x*Ny*4+y*4+1, x*Ny*4+y*4+3] = gamma
+ h[x*Ny*4+y*4+2, x*Ny*4+y*4+0] = gamma
+ h[x*Ny*4+y*4+2, x*Ny*4+y*4+1] = -gamma
+ h[x*Ny*4+y*4+3, x*Ny*4+y*4+0] = gamma
+ h[x*Ny*4+y*4+3, x*Ny*4+y*4+1] = gamma
+
+ # y方向上的元胞间跃迁
+ for x in range(Nx):
+ for y in range(Ny-1):
+ h[x*Ny*4+y*4+0, x*Ny*4+(y+1)*4+3] = lambda0
+ h[x*Ny*4+(y+1)*4+1, x*Ny*4+y*4+2] = -lambda0
+ h[x*Ny*4+y*4+2, x*Ny*4+(y+1)*4+1] = -lambda0
+ h[x*Ny*4+(y+1)*4+3, x*Ny*4+y*4+0] = lambda0
+
+ # x方向上的元胞间跃迁
+ for x in range(Nx-1):
+ for y in range(Ny):
+ h[x*Ny*4+y*4+0, (x+1)*Ny*4+y*4+2] = lambda0
+ h[(x+1)*Ny*4+y*4+1, x*Ny*4+y*4+3] = lambda0
+ h[(x+1)*Ny*4+y*4+2, x*Ny*4+y*4+0] = lambda0
+ h[x*Ny*4+y*4+3, (x+1)*Ny*4+y*4+1] = lambda0
+ return h
+
+
+def main():
+ Nx = 10
+ Ny = 10
+ fermi_energy = 0
+ h = hamiltonian(Nx, Ny)
+ green = np.linalg.inv((fermi_energy+1e-6j)*np.eye(h.shape[0])-h)
+ DOS = np.zeros((Ny*2, Nx*2))
+ for x in range(Nx):
+ for y in range(Ny):
+ DOS[y*2+1, x*2+1] = -np.imag(green[x*Ny*4+y*4+0, x*Ny*4+y*4+0])/pi
+ DOS[y*2+0, x*2+0] = -np.imag(green[x*Ny*4+y*4+1, x*Ny*4+y*4+1])/pi
+ DOS[y*2+1, x*2+0] = -np.imag(green[x*Ny*4+y*4+2, x*Ny*4+y*4+2])/pi
+ DOS[y*2+0, x*2+1] = -np.imag(green[x*Ny*4+y*4+3, x*Ny*4+y*4+3])/pi
+ DOS = DOS/np.sum(DOS)
+ Plot_3D_Surface(np.arange(1, 2*Nx+0.001), np.arange(1, 2*Ny+0.001), DOS, xlabel='x', ylabel='y', zlabel='DOS', title='BBH Model', filename='BBH Model')
+
+
+def Plot_3D_Surface(x, y, matrix, xlabel='x', ylabel='y', zlabel='z', title='title', filename='a'):
+ import matplotlib.pyplot as plt
+ from matplotlib import cm
+ from matplotlib.ticker import LinearLocator
+ fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
+ plt.subplots_adjust(bottom=0.1, right=0.65)
+ x, y = np.meshgrid(x, y)
+ if len(matrix.shape) == 2:
+ surf = ax.plot_surface(x, y, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ elif len(matrix.shape) == 3:
+ for i0 in range(matrix.shape[2]):
+ surf = ax.plot_surface(x, y, matrix[:,:,i0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
+ ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
+ ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
+ ax.set_zlabel(zlabel, fontsize=20, fontfamily='Times New Roman')
+ # ax.set_zlim(-1, 1) # 设置z轴的范围
+ ax.zaxis.set_major_locator(LinearLocator(2)) # 设置z轴主刻度的个数
+ ax.zaxis.set_major_formatter('{x:.2f}') # 设置z轴主刻度的格式
+ ax.tick_params(labelsize=15) # 设置刻度值字体大小
+ labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
+ [label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
+ cax = plt.axes([0.80, 0.15, 0.05, 0.75]) # color bar的位置 [左，下，宽度， 高度]
+ cbar = fig.colorbar(surf, cax=cax) # color bar
+ cbar.ax.tick_params(labelsize=15) # 设置color bar刻度的字体大小
+ for l in cbar.ax.yaxis.get_ticklabels():
+ l.set_family('Times New Roman')
+ # plt.savefig(filename+'.jpg', dpi=300)
+ plt.show()
+ plt.close('all')
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.03.08_eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices/eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices.nb b/academic_codes/models_and_bands/2021.03.08_eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices/eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices.nb
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2021.03.08_eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices/eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices.nb
rename to academic_codes/models_and_bands/2021.03.08_eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices/eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices.nb
index 05acd1b..fc18a1d
--- a/academic_codes/2021.03.08_eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices/eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices.nb
+++ b/academic_codes/models_and_bands/2021.03.08_eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices/eigenvalues_of_the_Hamiltonian_composed_of_Pauli_matrices.nb
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diff --git a/academic_codes/2021.03.10_bands_of_type_II_Weyl_semimetals/bands_of_type_II_Weyl_semimetals.py b/academic_codes/models_and_bands/2021.03.10_bands_of_type_II_Weyl_semimetals/bands_of_type_II_Weyl_semimetals.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.10_bands_of_type_II_Weyl_semimetals/bands_of_type_II_Weyl_semimetals.py
rename to academic_codes/models_and_bands/2021.03.10_bands_of_type_II_Weyl_semimetals/bands_of_type_II_Weyl_semimetals.py
index a114181..1c4e115
--- a/academic_codes/2021.03.10_bands_of_type_II_Weyl_semimetals/bands_of_type_II_Weyl_semimetals.py
+++ b/academic_codes/models_and_bands/2021.03.10_bands_of_type_II_Weyl_semimetals/bands_of_type_II_Weyl_semimetals.py
@@ -1,89 +1,89 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10385
-"""
-
-import numpy as np
-from math import *
-
-
-def main():
- k1 = np.linspace(-pi, pi, 100)
- k2 = np.linspace(-pi, pi, 100)
-
- eigenvalue_array = Calculate_Eigenvalue_with_Two_Parameters(k1, k2, hamiltonian)
- Plot_3D_Surface(k1, k2, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', title='', filename='a')
-
-
-def hamiltonian(kx,ky,kz=0):
- w0x = 2
- w0y = 0
- w0z = 0
- vx = 1
- vy = 1
- vz = 1
- H = (w0x*kx+w0y*ky+w0z*kz)*sigma_0() + vx*kx*sigma_x()+vy*ky*sigma_y()+vz*kz*sigma_z()
- return H
-
-
-def sigma_0():
- return np.eye(2)
-
-def sigma_x():
- return np.array([[0, 1],[1, 0]])
-
-
-def sigma_y():
- return np.array([[0, -1j],[1j, 0]])
-
-
-def sigma_z():
- return np.array([[1, 0],[0, -1]])
-
-
-def Calculate_Eigenvalue_with_Two_Parameters(x, y, matrix):
- dim = np.array(matrix(0, 0)).shape[0]
- dim_x = np.array(x).shape[0]
- dim_y = np.array(y).shape[0]
- eigenvalue_array = np.zeros((dim_y, dim_x, dim))
- i0 = 0
- for y0 in y:
- j0 = 0
- for x0 in x:
- matrix0 = matrix(x0, y0)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_array[i0, j0, :] = np.sort(np.real(eigenvalue[:]))
- j0 += 1
- i0 += 1
- return eigenvalue_array
-
-
-def Plot_3D_Surface(x, y, matrix, xlabel='x', ylabel='y', zlabel='z', title='', filename='a'):
- import matplotlib.pyplot as plt
- from matplotlib import cm
- from matplotlib.ticker import LinearLocator
- fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
- plt.subplots_adjust(bottom=0.1, right=0.8)
- x, y = np.meshgrid(x, y)
- if len(matrix.shape) == 2:
- surf = ax.plot_surface(x, y, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
- elif len(matrix.shape) == 3:
- for i0 in range(matrix.shape[2]):
- surf = ax.plot_surface(x, y, matrix[:,:,i0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
- ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
- ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
- ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
- ax.set_zlabel(zlabel, fontsize=20, fontfamily='Times New Roman')
- # ax.set_zlim(-1, 1) # 设置z轴的范围
- ax.zaxis.set_major_locator(LinearLocator(5)) # 设置z轴主刻度的个数
- ax.zaxis.set_major_formatter('{x:.2f}') # 设置z轴主刻度的格式
- ax.tick_params(labelsize=15) # 设置刻度值字体大小
- labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
- [label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
- # plt.savefig(filename+'.jpg', dpi=300)
- plt.show()
- plt.close('all')
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10385
+"""
+
+import numpy as np
+from math import *
+
+
+def main():
+ k1 = np.linspace(-pi, pi, 100)
+ k2 = np.linspace(-pi, pi, 100)
+
+ eigenvalue_array = Calculate_Eigenvalue_with_Two_Parameters(k1, k2, hamiltonian)
+ Plot_3D_Surface(k1, k2, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', title='', filename='a')
+
+
+def hamiltonian(kx,ky,kz=0):
+ w0x = 2
+ w0y = 0
+ w0z = 0
+ vx = 1
+ vy = 1
+ vz = 1
+ H = (w0x*kx+w0y*ky+w0z*kz)*sigma_0() + vx*kx*sigma_x()+vy*ky*sigma_y()+vz*kz*sigma_z()
+ return H
+
+
+def sigma_0():
+ return np.eye(2)
+
+def sigma_x():
+ return np.array([[0, 1],[1, 0]])
+
+
+def sigma_y():
+ return np.array([[0, -1j],[1j, 0]])
+
+
+def sigma_z():
+ return np.array([[1, 0],[0, -1]])
+
+
+def Calculate_Eigenvalue_with_Two_Parameters(x, y, matrix):
+ dim = np.array(matrix(0, 0)).shape[0]
+ dim_x = np.array(x).shape[0]
+ dim_y = np.array(y).shape[0]
+ eigenvalue_array = np.zeros((dim_y, dim_x, dim))
+ i0 = 0
+ for y0 in y:
+ j0 = 0
+ for x0 in x:
+ matrix0 = matrix(x0, y0)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_array[i0, j0, :] = np.sort(np.real(eigenvalue[:]))
+ j0 += 1
+ i0 += 1
+ return eigenvalue_array
+
+
+def Plot_3D_Surface(x, y, matrix, xlabel='x', ylabel='y', zlabel='z', title='', filename='a'):
+ import matplotlib.pyplot as plt
+ from matplotlib import cm
+ from matplotlib.ticker import LinearLocator
+ fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
+ plt.subplots_adjust(bottom=0.1, right=0.8)
+ x, y = np.meshgrid(x, y)
+ if len(matrix.shape) == 2:
+ surf = ax.plot_surface(x, y, matrix, cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ elif len(matrix.shape) == 3:
+ for i0 in range(matrix.shape[2]):
+ surf = ax.plot_surface(x, y, matrix[:,:,i0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
+ ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
+ ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
+ ax.set_zlabel(zlabel, fontsize=20, fontfamily='Times New Roman')
+ # ax.set_zlim(-1, 1) # 设置z轴的范围
+ ax.zaxis.set_major_locator(LinearLocator(5)) # 设置z轴主刻度的个数
+ ax.zaxis.set_major_formatter('{x:.2f}') # 设置z轴主刻度的格式
+ ax.tick_params(labelsize=15) # 设置刻度值字体大小
+ labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
+ [label.set_fontname('Times New Roman') for label in labels] # 设置刻度值字体
+ # plt.savefig(filename+'.jpg', dpi=300)
+ plt.show()
+ plt.close('all')
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.03.19_plot_twist_graphene_with_python/graphene.py b/academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/graphene.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.19_plot_twist_graphene_with_python/graphene.py
rename to academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/graphene.py
index f6b6b3e..18a62e3
--- a/academic_codes/2021.03.19_plot_twist_graphene_with_python/graphene.py
+++ b/academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/graphene.py
@@ -1,40 +1,40 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10909
-"""
-
-import numpy as np
-
-
-def main():
- x_array = np.arange(-5, 5.1)
- y_array = np.arange(-5, 5.1)
- coordinates = []
- for x in x_array:
- for y in y_array:
- coordinates.append([0+x*3, 0+y*np.sqrt(3)])
- coordinates.append([1+x*3, 0+y*np.sqrt(3)])
- coordinates.append([-1/2+x*3, np.sqrt(3)/2+y*np.sqrt(3)])
- coordinates.append([-3/2+x*3, np.sqrt(3)/2+y*np.sqrt(3)])
- plot_dots(coordinates)
-
-
-def plot_dots(coordinates):
- import matplotlib.pyplot as plt
- x_range = max(np.array(coordinates)[:, 0])-min(np.array(coordinates)[:, 0])
- y_range = max(np.array(coordinates)[:, 1])-min(np.array(coordinates)[:, 1])
- fig, ax = plt.subplots(figsize=(9*x_range/y_range,9))
- plt.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
- plt.axis('off')
- for i1 in range(len(coordinates)):
- for i2 in range(len(coordinates)):
- if np.sqrt((coordinates[i1][0] - coordinates[i2][0])**2+(coordinates[i1][1] - coordinates[i2][1])**2) < 1.1:
- ax.plot([coordinates[i1][0], coordinates[i2][0]], [coordinates[i1][1], coordinates[i2][1]], '-k', linewidth=1)
- for i in range(len(coordinates)):
- ax.plot(coordinates[i][0], coordinates[i][1], 'ro', markersize=8)
- # plt.savefig('graphene.eps')
- plt.show()
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10909
+"""
+
+import numpy as np
+
+
+def main():
+ x_array = np.arange(-5, 5.1)
+ y_array = np.arange(-5, 5.1)
+ coordinates = []
+ for x in x_array:
+ for y in y_array:
+ coordinates.append([0+x*3, 0+y*np.sqrt(3)])
+ coordinates.append([1+x*3, 0+y*np.sqrt(3)])
+ coordinates.append([-1/2+x*3, np.sqrt(3)/2+y*np.sqrt(3)])
+ coordinates.append([-3/2+x*3, np.sqrt(3)/2+y*np.sqrt(3)])
+ plot_dots(coordinates)
+
+
+def plot_dots(coordinates):
+ import matplotlib.pyplot as plt
+ x_range = max(np.array(coordinates)[:, 0])-min(np.array(coordinates)[:, 0])
+ y_range = max(np.array(coordinates)[:, 1])-min(np.array(coordinates)[:, 1])
+ fig, ax = plt.subplots(figsize=(9*x_range/y_range,9))
+ plt.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
+ plt.axis('off')
+ for i1 in range(len(coordinates)):
+ for i2 in range(len(coordinates)):
+ if np.sqrt((coordinates[i1][0] - coordinates[i2][0])**2+(coordinates[i1][1] - coordinates[i2][1])**2) < 1.1:
+ ax.plot([coordinates[i1][0], coordinates[i2][0]], [coordinates[i1][1], coordinates[i2][1]], '-k', linewidth=1)
+ for i in range(len(coordinates)):
+ ax.plot(coordinates[i][0], coordinates[i][1], 'ro', markersize=8)
+ # plt.savefig('graphene.eps')
+ plt.show()
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.03.19_plot_twist_graphene_with_python/square.py b/academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/square.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.19_plot_twist_graphene_with_python/square.py
rename to academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/square.py
index 828c6e9..d781e8b
--- a/academic_codes/2021.03.19_plot_twist_graphene_with_python/square.py
+++ b/academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/square.py
@@ -1,35 +1,35 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10909
-"""
-
-import numpy as np
-
-
-def main():
- x_array = np.arange(-5, 5.1)
- y_array = np.arange(-5, 5.1)
- coordinates = []
- for x in x_array:
- for y in y_array:
- coordinates.append([x, y])
- plot_dots(coordinates)
-
-
-def plot_dots(coordinates):
- import matplotlib.pyplot as plt
- fig, ax = plt.subplots(figsize=(9,9))
- plt.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
- plt.axis('off')
- for i1 in range(len(coordinates)):
- for i2 in range(len(coordinates)):
- if np.sqrt((coordinates[i1][0] - coordinates[i2][0])**2+(coordinates[i1][1] - coordinates[i2][1])**2) < 1.1:
- ax.plot([coordinates[i1][0], coordinates[i2][0]], [coordinates[i1][1], coordinates[i2][1]], '-k', linewidth=1)
- for i in range(len(coordinates)):
- ax.plot(coordinates[i][0], coordinates[i][1], 'ro', markersize=10)
- # plt.savefig('square.eps')
- plt.show()
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10909
+"""
+
+import numpy as np
+
+
+def main():
+ x_array = np.arange(-5, 5.1)
+ y_array = np.arange(-5, 5.1)
+ coordinates = []
+ for x in x_array:
+ for y in y_array:
+ coordinates.append([x, y])
+ plot_dots(coordinates)
+
+
+def plot_dots(coordinates):
+ import matplotlib.pyplot as plt
+ fig, ax = plt.subplots(figsize=(9,9))
+ plt.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
+ plt.axis('off')
+ for i1 in range(len(coordinates)):
+ for i2 in range(len(coordinates)):
+ if np.sqrt((coordinates[i1][0] - coordinates[i2][0])**2+(coordinates[i1][1] - coordinates[i2][1])**2) < 1.1:
+ ax.plot([coordinates[i1][0], coordinates[i2][0]], [coordinates[i1][1], coordinates[i2][1]], '-k', linewidth=1)
+ for i in range(len(coordinates)):
+ ax.plot(coordinates[i][0], coordinates[i][1], 'ro', markersize=10)
+ # plt.savefig('square.eps')
+ plt.show()
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.03.19_plot_twist_graphene_with_python/twist_graphene.py b/academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/twist_graphene.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.19_plot_twist_graphene_with_python/twist_graphene.py
rename to academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/twist_graphene.py
index 318f4f6..664d630
--- a/academic_codes/2021.03.19_plot_twist_graphene_with_python/twist_graphene.py
+++ b/academic_codes/models_and_bands/2021.03.19_plot_twist_graphene_with_python/twist_graphene.py
@@ -1,72 +1,72 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10909
-"""
-
-import numpy as np
-import copy
-import matplotlib.pyplot as plt
-from math import *
-
-def main():
- x_array = np.arange(-20, 20.1)
- y_array = np.arange(-20, 20.1)
- coordinates = []
- for x in x_array:
- for y in y_array:
- coordinates.append([0+x*3, 0+y*np.sqrt(3)])
- coordinates.append([1+x*3, 0+y*np.sqrt(3)])
- coordinates.append([-1/2+x*3, np.sqrt(3)/2+y*np.sqrt(3)])
- coordinates.append([-3/2+x*3, np.sqrt(3)/2+y*np.sqrt(3)])
- x_range1 = max(np.array(coordinates)[:, 0])-min(np.array(coordinates)[:, 0])
- y_range1 = max(np.array(coordinates)[:, 1])-min(np.array(coordinates)[:, 1])
-
- theta = -1.1/180*pi
- rotation_matrix = np.zeros((2, 2))
- rotation_matrix[0, 0] = np.cos(theta)
- rotation_matrix[1, 1] = np.cos(theta)
- rotation_matrix[0, 1] = -np.sin(theta)
- rotation_matrix[1, 0] = np.sin(theta)
- coordinates2 = copy.deepcopy(coordinates)
- for i in range(len(coordinates)):
- coordinates2[i] = np.dot(rotation_matrix, coordinates[i])
- x_range2 = max(np.array(coordinates2)[:, 0])-min(np.array(coordinates2)[:, 0])
- y_range2 = max(np.array(coordinates2)[:, 1])-min(np.array(coordinates2)[:, 1])
-
- x_range = max([x_range1, x_range2])
- y_range = max([y_range1, y_range2])
- fig, ax = plt.subplots(figsize=(9*x_range/y_range,9))
- plt.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
- plt.axis('off')
- plot_dots_1(ax, coordinates)
- plot_dots_2(ax, coordinates2)
- plot_dots_0(ax, [[0, 0]])
- plt.savefig('twist_graphene.eps')
- plt.show()
-
-
-def plot_dots_0(ax, coordinates):
- for i in range(len(coordinates)):
- ax.plot(coordinates[i][0], coordinates[i][1], 'ko', markersize=0.5)
-
-
-def plot_dots_1(ax, coordinates):
- for i1 in range(len(coordinates)):
- for i2 in range(len(coordinates)):
- if np.sqrt((coordinates[i1][0] - coordinates[i2][0])**2+(coordinates[i1][1] - coordinates[i2][1])**2) < 1.1:
- ax.plot([coordinates[i1][0], coordinates[i2][0]], [coordinates[i1][1], coordinates[i2][1]], '-k', linewidth=0.2)
- for i in range(len(coordinates)):
- ax.plot(coordinates[i][0], coordinates[i][1], 'ro', markersize=0.5)
-
-
-def plot_dots_2(ax, coordinates):
- for i1 in range(len(coordinates)):
- for i2 in range(len(coordinates)):
- if np.sqrt((coordinates[i1][0] - coordinates[i2][0])**2+(coordinates[i1][1] - coordinates[i2][1])**2) < 1.1:
- ax.plot([coordinates[i1][0], coordinates[i2][0]], [coordinates[i1][1], coordinates[i2][1]], '--k', linewidth=0.2)
- for i in range(len(coordinates)):
- ax.plot(coordinates[i][0], coordinates[i][1], 'bo', markersize=0.5)
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10909
+"""
+
+import numpy as np
+import copy
+import matplotlib.pyplot as plt
+from math import *
+
+def main():
+ x_array = np.arange(-20, 20.1)
+ y_array = np.arange(-20, 20.1)
+ coordinates = []
+ for x in x_array:
+ for y in y_array:
+ coordinates.append([0+x*3, 0+y*np.sqrt(3)])
+ coordinates.append([1+x*3, 0+y*np.sqrt(3)])
+ coordinates.append([-1/2+x*3, np.sqrt(3)/2+y*np.sqrt(3)])
+ coordinates.append([-3/2+x*3, np.sqrt(3)/2+y*np.sqrt(3)])
+ x_range1 = max(np.array(coordinates)[:, 0])-min(np.array(coordinates)[:, 0])
+ y_range1 = max(np.array(coordinates)[:, 1])-min(np.array(coordinates)[:, 1])
+
+ theta = -1.1/180*pi
+ rotation_matrix = np.zeros((2, 2))
+ rotation_matrix[0, 0] = np.cos(theta)
+ rotation_matrix[1, 1] = np.cos(theta)
+ rotation_matrix[0, 1] = -np.sin(theta)
+ rotation_matrix[1, 0] = np.sin(theta)
+ coordinates2 = copy.deepcopy(coordinates)
+ for i in range(len(coordinates)):
+ coordinates2[i] = np.dot(rotation_matrix, coordinates[i])
+ x_range2 = max(np.array(coordinates2)[:, 0])-min(np.array(coordinates2)[:, 0])
+ y_range2 = max(np.array(coordinates2)[:, 1])-min(np.array(coordinates2)[:, 1])
+
+ x_range = max([x_range1, x_range2])
+ y_range = max([y_range1, y_range2])
+ fig, ax = plt.subplots(figsize=(9*x_range/y_range,9))
+ plt.subplots_adjust(left=0.05, bottom=0.05, right=0.95, top=0.95)
+ plt.axis('off')
+ plot_dots_1(ax, coordinates)
+ plot_dots_2(ax, coordinates2)
+ plot_dots_0(ax, [[0, 0]])
+ plt.savefig('twist_graphene.eps')
+ plt.show()
+
+
+def plot_dots_0(ax, coordinates):
+ for i in range(len(coordinates)):
+ ax.plot(coordinates[i][0], coordinates[i][1], 'ko', markersize=0.5)
+
+
+def plot_dots_1(ax, coordinates):
+ for i1 in range(len(coordinates)):
+ for i2 in range(len(coordinates)):
+ if np.sqrt((coordinates[i1][0] - coordinates[i2][0])**2+(coordinates[i1][1] - coordinates[i2][1])**2) < 1.1:
+ ax.plot([coordinates[i1][0], coordinates[i2][0]], [coordinates[i1][1], coordinates[i2][1]], '-k', linewidth=0.2)
+ for i in range(len(coordinates)):
+ ax.plot(coordinates[i][0], coordinates[i][1], 'ro', markersize=0.5)
+
+
+def plot_dots_2(ax, coordinates):
+ for i1 in range(len(coordinates)):
+ for i2 in range(len(coordinates)):
+ if np.sqrt((coordinates[i1][0] - coordinates[i2][0])**2+(coordinates[i1][1] - coordinates[i2][1])**2) < 1.1:
+ ax.plot([coordinates[i1][0], coordinates[i2][0]], [coordinates[i1][1], coordinates[i2][1]], '--k', linewidth=0.2)
+ for i in range(len(coordinates)):
+ ax.plot(coordinates[i][0], coordinates[i][1], 'bo', markersize=0.5)
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/eigenvalue_of_Kronecker_product_of_Pauli_matrices.py b/academic_codes/models_and_bands/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
rename to academic_codes/models_and_bands/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
index a62150e..600a184
--- a/academic_codes/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
+++ b/academic_codes/models_and_bands/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
@@ -1,53 +1,53 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/12731
-"""
-
-import numpy as np
-import guan
-
-a_00 = np.random.uniform(-1, 1)
-a_0x = np.random.uniform(-1, 1)
-a_0y = np.random.uniform(-1, 1)
-a_0z = np.random.uniform(-1, 1)
-
-a_x0 = np.random.uniform(-1, 1)
-a_xx = np.random.uniform(-1, 1)
-a_xy = np.random.uniform(-1, 1)
-a_xz = np.random.uniform(-1, 1)
-
-a_y0 = np.random.uniform(-1, 1)
-a_yx = np.random.uniform(-1, 1)
-a_yy = np.random.uniform(-1, 1)
-a_yz = np.random.uniform(-1, 1)
-
-a_z0 = np.random.uniform(-1, 1)
-a_zx = np.random.uniform(-1, 1)
-a_zy = np.random.uniform(-1, 1)
-a_zz = np.random.uniform(-1, 1)
-
-hamiltonian_1 = \
- a_00*guan.sigma_00()+a_0x*guan.sigma_0x()+a_0y*guan.sigma_0y()+a_0z*guan.sigma_0z()+ \
- a_x0*guan.sigma_x0()+a_xx*guan.sigma_xx()+a_xy*guan.sigma_xy()+a_xz*guan.sigma_xz()+ \
- a_y0*guan.sigma_y0()+a_yx*guan.sigma_yx()+a_yy*guan.sigma_yy()+a_yz*guan.sigma_yz()+ \
- a_z0*guan.sigma_z0()+a_zx*guan.sigma_zx()+a_zy*guan.sigma_zy()+a_zz*guan.sigma_zz()
-eigenvalue_1 = guan.calculate_eigenvalue(hamiltonian_1)
-
-hamiltonian_2 = \
- a_00*guan.sigma_00()+a_0x*guan.sigma_x0()+a_0y*guan.sigma_y0()+a_0z*guan.sigma_z0()+ \
- a_x0*guan.sigma_0x()+a_xx*guan.sigma_xx()+a_xy*guan.sigma_yx()+a_xz*guan.sigma_zx()+ \
- a_y0*guan.sigma_0y()+a_yx*guan.sigma_xy()+a_yy*guan.sigma_yy()+a_yz*guan.sigma_zy()+ \
- a_z0*guan.sigma_0z()+a_zx*guan.sigma_xz()+a_zy*guan.sigma_yz()+a_zz*guan.sigma_zz()
-eigenvalue_2 = guan.calculate_eigenvalue(hamiltonian_2)
-
-
-print()
-print('Eigenvalue:')
-print(eigenvalue_1)
-print(eigenvalue_2)
-print()
-print('Difference of matrices:')
-print(hamiltonian_1-hamiltonian_2)
-print()
-# print(hamiltonian_1)
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/12731
+"""
+
+import numpy as np
+import guan
+
+a_00 = np.random.uniform(-1, 1)
+a_0x = np.random.uniform(-1, 1)
+a_0y = np.random.uniform(-1, 1)
+a_0z = np.random.uniform(-1, 1)
+
+a_x0 = np.random.uniform(-1, 1)
+a_xx = np.random.uniform(-1, 1)
+a_xy = np.random.uniform(-1, 1)
+a_xz = np.random.uniform(-1, 1)
+
+a_y0 = np.random.uniform(-1, 1)
+a_yx = np.random.uniform(-1, 1)
+a_yy = np.random.uniform(-1, 1)
+a_yz = np.random.uniform(-1, 1)
+
+a_z0 = np.random.uniform(-1, 1)
+a_zx = np.random.uniform(-1, 1)
+a_zy = np.random.uniform(-1, 1)
+a_zz = np.random.uniform(-1, 1)
+
+hamiltonian_1 = \
+ a_00*guan.sigma_00()+a_0x*guan.sigma_0x()+a_0y*guan.sigma_0y()+a_0z*guan.sigma_0z()+ \
+ a_x0*guan.sigma_x0()+a_xx*guan.sigma_xx()+a_xy*guan.sigma_xy()+a_xz*guan.sigma_xz()+ \
+ a_y0*guan.sigma_y0()+a_yx*guan.sigma_yx()+a_yy*guan.sigma_yy()+a_yz*guan.sigma_yz()+ \
+ a_z0*guan.sigma_z0()+a_zx*guan.sigma_zx()+a_zy*guan.sigma_zy()+a_zz*guan.sigma_zz()
+eigenvalue_1 = guan.calculate_eigenvalue(hamiltonian_1)
+
+hamiltonian_2 = \
+ a_00*guan.sigma_00()+a_0x*guan.sigma_x0()+a_0y*guan.sigma_y0()+a_0z*guan.sigma_z0()+ \
+ a_x0*guan.sigma_0x()+a_xx*guan.sigma_xx()+a_xy*guan.sigma_yx()+a_xz*guan.sigma_zx()+ \
+ a_y0*guan.sigma_0y()+a_yx*guan.sigma_xy()+a_yy*guan.sigma_yy()+a_yz*guan.sigma_zy()+ \
+ a_z0*guan.sigma_0z()+a_zx*guan.sigma_xz()+a_zy*guan.sigma_yz()+a_zz*guan.sigma_zz()
+eigenvalue_2 = guan.calculate_eigenvalue(hamiltonian_2)
+
+
+print()
+print('Eigenvalue:')
+print(eigenvalue_1)
+print(eigenvalue_2)
+print()
+print('Difference of matrices:')
+print(hamiltonian_1-hamiltonian_2)
+print()
+# print(hamiltonian_1)
# print(hamiltonian_2)
\ No newline at end of file
diff --git a/academic_codes/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/test2_eigenvalue_of_Kronecker_product_of_Pauli_matrices.py b/academic_codes/models_and_bands/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/test2_eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/test2_eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
rename to academic_codes/models_and_bands/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/test2_eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
index 7e882f4..e7111eb
--- a/academic_codes/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/test2_eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
+++ b/academic_codes/models_and_bands/2021.05.21_eigenvalue_of_Kronecker_product_of_Pauli_matrices/test2_eigenvalue_of_Kronecker_product_of_Pauli_matrices.py
@@ -1,49 +1,49 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/12731
-"""
-
-import numpy as np
-import guan
-
-a_00 = np.random.uniform(-1, 1)
-a_0x = np.random.uniform(-1, 1)
-a_0y = np.random.uniform(-1, 1)
-a_0z = np.random.uniform(-1, 1)
-
-a_x0 = np.random.uniform(-1, 1)
-a_xx = np.random.uniform(-1, 1)
-a_xy = np.random.uniform(-1, 1)
-a_xz = np.random.uniform(-1, 1)
-
-a_y0 = np.random.uniform(-1, 1)
-a_yx = np.random.uniform(-1, 1)
-a_yy = np.random.uniform(-1, 1)
-a_yz = np.random.uniform(-1, 1)
-
-a_z0 = np.random.uniform(-1, 1)
-a_zx = np.random.uniform(-1, 1)
-a_zy = np.random.uniform(-1, 1)
-a_zz = np.random.uniform(-1, 1)
-
-hamiltonian_1 = \
- a_00*guan.sigma_00()+a_0x*guan.sigma_0x()+a_0y*guan.sigma_0y()+a_0z*guan.sigma_0z()+ \
- a_x0*guan.sigma_x0()+a_xx*guan.sigma_xx()+a_xy*guan.sigma_xy()+a_xz*guan.sigma_xz()+ \
- a_y0*guan.sigma_y0()+a_yx*guan.sigma_yx()+a_yy*guan.sigma_yy()+a_yz*guan.sigma_yz()+ \
- a_z0*guan.sigma_z0()+a_zx*guan.sigma_zx()+a_zy*guan.sigma_zy()+a_zz*guan.sigma_zz()
-eigenvalue_1 = guan.calculate_eigenvalue(hamiltonian_1)
-
-
-# only guan.sigma_0x() is changed to guan.sigma_x0()
-hamiltonian_3 = \
- a_00*guan.sigma_00()+a_0x*guan.sigma_x0()+a_0y*guan.sigma_0y()+a_0z*guan.sigma_0z()+ \
- a_x0*guan.sigma_x0()+a_xx*guan.sigma_xx()+a_xy*guan.sigma_xy()+a_xz*guan.sigma_xz()+ \
- a_y0*guan.sigma_y0()+a_yx*guan.sigma_yx()+a_yy*guan.sigma_yy()+a_yz*guan.sigma_yz()+ \
- a_z0*guan.sigma_z0()+a_zx*guan.sigma_zx()+a_zy*guan.sigma_zy()+a_zz*guan.sigma_zz()
-eigenvalue_3 = guan.calculate_eigenvalue(hamiltonian_3)
-
-print()
-print('Eigenvalue:')
-print(eigenvalue_1)
-print(eigenvalue_3)
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/12731
+"""
+
+import numpy as np
+import guan
+
+a_00 = np.random.uniform(-1, 1)
+a_0x = np.random.uniform(-1, 1)
+a_0y = np.random.uniform(-1, 1)
+a_0z = np.random.uniform(-1, 1)
+
+a_x0 = np.random.uniform(-1, 1)
+a_xx = np.random.uniform(-1, 1)
+a_xy = np.random.uniform(-1, 1)
+a_xz = np.random.uniform(-1, 1)
+
+a_y0 = np.random.uniform(-1, 1)
+a_yx = np.random.uniform(-1, 1)
+a_yy = np.random.uniform(-1, 1)
+a_yz = np.random.uniform(-1, 1)
+
+a_z0 = np.random.uniform(-1, 1)
+a_zx = np.random.uniform(-1, 1)
+a_zy = np.random.uniform(-1, 1)
+a_zz = np.random.uniform(-1, 1)
+
+hamiltonian_1 = \
+ a_00*guan.sigma_00()+a_0x*guan.sigma_0x()+a_0y*guan.sigma_0y()+a_0z*guan.sigma_0z()+ \
+ a_x0*guan.sigma_x0()+a_xx*guan.sigma_xx()+a_xy*guan.sigma_xy()+a_xz*guan.sigma_xz()+ \
+ a_y0*guan.sigma_y0()+a_yx*guan.sigma_yx()+a_yy*guan.sigma_yy()+a_yz*guan.sigma_yz()+ \
+ a_z0*guan.sigma_z0()+a_zx*guan.sigma_zx()+a_zy*guan.sigma_zy()+a_zz*guan.sigma_zz()
+eigenvalue_1 = guan.calculate_eigenvalue(hamiltonian_1)
+
+
+# only guan.sigma_0x() is changed to guan.sigma_x0()
+hamiltonian_3 = \
+ a_00*guan.sigma_00()+a_0x*guan.sigma_x0()+a_0y*guan.sigma_0y()+a_0z*guan.sigma_0z()+ \
+ a_x0*guan.sigma_x0()+a_xx*guan.sigma_xx()+a_xy*guan.sigma_xy()+a_xz*guan.sigma_xz()+ \
+ a_y0*guan.sigma_y0()+a_yx*guan.sigma_yx()+a_yy*guan.sigma_yy()+a_yz*guan.sigma_yz()+ \
+ a_z0*guan.sigma_z0()+a_zx*guan.sigma_zx()+a_zy*guan.sigma_zy()+a_zz*guan.sigma_zz()
+eigenvalue_3 = guan.calculate_eigenvalue(hamiltonian_3)
+
+print()
+print('Eigenvalue:')
+print(eigenvalue_1)
+print(eigenvalue_3)
print()
\ No newline at end of file
diff --git a/academic_codes/2021.07.28_bands_of_SSH_model_with_two_kinds_of_fourier_transform/bands_of_SSH_model_with_two_kinds_of_fourier_transform.py b/academic_codes/models_and_bands/2021.07.28_bands_of_SSH_model_with_two_kinds_of_fourier_transform/bands_of_SSH_model_with_two_kinds_of_fourier_transform.py
similarity index 100%
rename from academic_codes/2021.07.28_bands_of_SSH_model_with_two_kinds_of_fourier_transform/bands_of_SSH_model_with_two_kinds_of_fourier_transform.py
rename to academic_codes/models_and_bands/2021.07.28_bands_of_SSH_model_with_two_kinds_of_fourier_transform/bands_of_SSH_model_with_two_kinds_of_fourier_transform.py
diff --git a/academic_codes/2021.08.09_flat_bands_of_Kagome lattice/flat_bands_of_Kagome lattice.nb b/academic_codes/models_and_bands/2021.08.09_flat_bands_of_Kagome lattice/flat_bands_of_Kagome lattice.nb
similarity index 100%
rename from academic_codes/2021.08.09_flat_bands_of_Kagome lattice/flat_bands_of_Kagome lattice.nb
rename to academic_codes/models_and_bands/2021.08.09_flat_bands_of_Kagome lattice/flat_bands_of_Kagome lattice.nb
diff --git a/academic_codes/2021.08.09_flat_bands_of_kagome lattice/flat_bands_of_kagome lattice.py b/academic_codes/models_and_bands/2021.08.09_flat_bands_of_Kagome lattice/flat_bands_of_Kagome lattice.py
similarity index 100%
rename from academic_codes/2021.08.09_flat_bands_of_kagome lattice/flat_bands_of_kagome lattice.py
rename to academic_codes/models_and_bands/2021.08.09_flat_bands_of_Kagome lattice/flat_bands_of_Kagome lattice.py
diff --git a/academic_codes/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/Haldane_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py b/academic_codes/models_and_bands/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/Haldane_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
similarity index 100%
rename from academic_codes/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/Haldane_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
rename to academic_codes/models_and_bands/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/Haldane_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
diff --git a/academic_codes/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/graphene_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py b/academic_codes/models_and_bands/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/graphene_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
similarity index 100%
rename from academic_codes/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/graphene_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
rename to academic_codes/models_and_bands/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/graphene_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
diff --git a/academic_codes/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/square_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py b/academic_codes/models_and_bands/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/square_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
similarity index 100%
rename from academic_codes/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/square_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
rename to academic_codes/models_and_bands/2022.07.14_relation_of_2D_discrete_ky_and_ribbon_with_Ny/square_relation_of_2D_discrete_ky_and_ribbon_with_Ny.py
diff --git a/academic_codes/2022.11.06_band_folding/band_folding.py b/academic_codes/models_and_bands/2022.11.06_band_folding/band_folding.py
similarity index 100%
rename from academic_codes/2022.11.06_band_folding/band_folding.py
rename to academic_codes/models_and_bands/2022.11.06_band_folding/band_folding.py
diff --git a/academic_codes/2022.11.07_band_folding_correspondence/band_folding_correspondence_1.py b/academic_codes/models_and_bands/2022.11.07_band_folding_correspondence/band_folding_correspondence_1.py
similarity index 100%
rename from academic_codes/2022.11.07_band_folding_correspondence/band_folding_correspondence_1.py
rename to academic_codes/models_and_bands/2022.11.07_band_folding_correspondence/band_folding_correspondence_1.py
diff --git a/academic_codes/2022.11.07_band_folding_correspondence/band_folding_correspondence_2.py b/academic_codes/models_and_bands/2022.11.07_band_folding_correspondence/band_folding_correspondence_2.py
similarity index 100%
rename from academic_codes/2022.11.07_band_folding_correspondence/band_folding_correspondence_2.py
rename to academic_codes/models_and_bands/2022.11.07_band_folding_correspondence/band_folding_correspondence_2.py
diff --git a/academic_codes/2019.11.03_three_body_moving_on_2D_space/big_mass_in_one_body/step_0.1.py b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/big_mass_in_one_body/step_0.1.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.03_three_body_moving_on_2D_space/big_mass_in_one_body/step_0.1.py
rename to academic_codes/others/2019.11.03_three_body_moving_on_2D_space/big_mass_in_one_body/step_0.1.py
index a715bf4..8a73f3e
--- a/academic_codes/2019.11.03_three_body_moving_on_2D_space/big_mass_in_one_body/step_0.1.py
+++ b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/big_mass_in_one_body/step_0.1.py
@@ -1,114 +1,114 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-# os.chdir('E:/data') # 设置路径
-
-# 万有引力常数
-G = 1
-
-# 三体的质量
-m1 = 10 # 绿色
-m2 = 1000 # 红色
-m3 = 10 # 蓝色
-
-# 三体的初始位置
-x1 = 0 # 绿色
-y1 = 500
-x2 = 0 # 红色
-y2 = 0
-x3 = 0 # 蓝色
-y3 = 1000
-
-# 三体的初始速度
-v1_x = 1.5 # 绿色
-v1_y = 0
-v2_x = 0 # 红色
-v2_y = 0
-v3_x = 0.8 # 蓝色
-v3_y = 0
-
-# 步长
-t = 0.1
-
-plt.ion() # 开启交互模式
-observation_max = 100 # 视线范围初始值
-x1_all = [x1] # 轨迹初始值
-y1_all = [y1]
-x2_all = [x2]
-y2_all = [y2]
-x3_all = [x3]
-y3_all = [y3]
-
-i0 = 0
-for i in range(1000000):
- distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
- distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
- distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
-
- # 对物体1的计算
- a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
- a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
- a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
- a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
- v1_x = v1_x + a1_x*t # 物体1的速度
- v1_y = v1_y + a1_y*t # 物体1的速度
- x1 = x1 + v1_x*t # 物体1的水平位置
- y1 = y1 + v1_y*t # 物体1的垂直位置
- x1_all = np.append(x1_all, x1) # 记录轨迹
- y1_all = np.append(y1_all, y1) # 记录轨迹
-
- # 对物体2的计算
- a2_1 = G*m1/(distance12**2)
- a2_3 = G*m3/(distance23**2)
- a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
- a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
- v2_x = v2_x + a2_x*t
- v2_y = v2_y + a2_y*t
- x2 = x2 + v2_x*t
- y2 = y2 + v2_y*t
- x2_all = np.append(x2_all, x2)
- y2_all = np.append(y2_all, y2)
-
- # 对物体3的计算
- a3_1 = G*m1/(distance13**2)
- a3_2 = G*m2/(distance23**2)
- a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
- a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
- v3_x = v3_x + a3_x*t
- v3_y = v3_y + a3_y*t
- x3 = x3 + v3_x*t
- y3 = y3 + v3_y*t
- x3_all = np.append(x3_all, x3)
- y3_all = np.append(y3_all, y3)
-
- # 选择观测坐标
- axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
- axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
- while True:
- if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
- np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
- observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
- elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
- np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
- observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
- else:
- break
-
- plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
-
- plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，球面积越大。视线范围越宽，球看起来越小。
- plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
- plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
- plt.plot(x1_all, y1_all, '-g') # 画轨迹
- plt.plot(x2_all, y2_all, '-r')
- plt.plot(x3_all, y3_all, '-b')
-
-
- # plt.show() # 显示图像
- # plt.pause(0.00001) # 暂停0.00001，防止画图过快
-
- if np.mod(i, 100) == 0: # 当运动明显时，把图画出来
- print(i0)
- plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
- i0 += 1
- plt.clf() # 清空
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+# os.chdir('E:/data') # 设置路径
+
+# 万有引力常数
+G = 1
+
+# 三体的质量
+m1 = 10 # 绿色
+m2 = 1000 # 红色
+m3 = 10 # 蓝色
+
+# 三体的初始位置
+x1 = 0 # 绿色
+y1 = 500
+x2 = 0 # 红色
+y2 = 0
+x3 = 0 # 蓝色
+y3 = 1000
+
+# 三体的初始速度
+v1_x = 1.5 # 绿色
+v1_y = 0
+v2_x = 0 # 红色
+v2_y = 0
+v3_x = 0.8 # 蓝色
+v3_y = 0
+
+# 步长
+t = 0.1
+
+plt.ion() # 开启交互模式
+observation_max = 100 # 视线范围初始值
+x1_all = [x1] # 轨迹初始值
+y1_all = [y1]
+x2_all = [x2]
+y2_all = [y2]
+x3_all = [x3]
+y3_all = [y3]
+
+i0 = 0
+for i in range(1000000):
+ distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
+ distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
+ distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
+
+ # 对物体1的计算
+ a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
+ a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
+ a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
+ a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
+ v1_x = v1_x + a1_x*t # 物体1的速度
+ v1_y = v1_y + a1_y*t # 物体1的速度
+ x1 = x1 + v1_x*t # 物体1的水平位置
+ y1 = y1 + v1_y*t # 物体1的垂直位置
+ x1_all = np.append(x1_all, x1) # 记录轨迹
+ y1_all = np.append(y1_all, y1) # 记录轨迹
+
+ # 对物体2的计算
+ a2_1 = G*m1/(distance12**2)
+ a2_3 = G*m3/(distance23**2)
+ a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
+ a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
+ v2_x = v2_x + a2_x*t
+ v2_y = v2_y + a2_y*t
+ x2 = x2 + v2_x*t
+ y2 = y2 + v2_y*t
+ x2_all = np.append(x2_all, x2)
+ y2_all = np.append(y2_all, y2)
+
+ # 对物体3的计算
+ a3_1 = G*m1/(distance13**2)
+ a3_2 = G*m2/(distance23**2)
+ a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
+ a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
+ v3_x = v3_x + a3_x*t
+ v3_y = v3_y + a3_y*t
+ x3 = x3 + v3_x*t
+ y3 = y3 + v3_y*t
+ x3_all = np.append(x3_all, x3)
+ y3_all = np.append(y3_all, y3)
+
+ # 选择观测坐标
+ axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
+ axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
+ while True:
+ if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
+ np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
+ observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
+ elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
+ np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
+ observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
+ else:
+ break
+
+ plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
+
+ plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，球面积越大。视线范围越宽，球看起来越小。
+ plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
+ plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
+ plt.plot(x1_all, y1_all, '-g') # 画轨迹
+ plt.plot(x2_all, y2_all, '-r')
+ plt.plot(x3_all, y3_all, '-b')
+
+
+ # plt.show() # 显示图像
+ # plt.pause(0.00001) # 暂停0.00001，防止画图过快
+
+ if np.mod(i, 100) == 0: # 当运动明显时，把图画出来
+ print(i0)
+ plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
+ i0 += 1
+ plt.clf() # 清空
diff --git a/academic_codes/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.05.py b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.05.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.05.py
rename to academic_codes/others/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.05.py
index 0a63032..f5a7873
--- a/academic_codes/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.05.py
+++ b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.05.py
@@ -1,110 +1,110 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-# os.chdir('E:/data') # 设置路径
-
-# 万有引力常数
-G = 1
-
-# 三体的质量
-m1 = 3 # 绿色
-m2 = 100 # 红色
-m3 = 10 # 蓝色
-
-# 三体的初始位置
-x1 = 0 # 绿色
-y1 = -100
-x2 = 0 # 红色
-y2 = 0
-x3 = 0 # 蓝色
-y3 = 50
-
-# 三体的初始速度
-v1_x = 1 # 绿色
-v1_y = 0
-v2_x = 0 # 红色
-v2_y = 0
-v3_x = 2 # 蓝色
-v3_y = 0
-
-# 步长
-t = 0.05
-
-plt.ion() # 开启交互模式
-observation_max = 100 # 观测坐标范围初始值
-x1_all = [x1] # 轨迹初始值
-y1_all = [y1]
-x2_all = [x2]
-y2_all = [y2]
-x3_all = [x3]
-y3_all = [y3]
-
-i0 = 0
-for i in range(100000000000):
- distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
- distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
- distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
- # 对物体1的计算
- a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
- a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
- a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
- a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
- v1_x = v1_x + a1_x*t # 物体1的速度
- v1_y = v1_y + a1_y*t # 物体1的速度
- x1 = x1 + v1_x*t # 物体1的水平位置
- y1 = y1 + v1_y*t # 物体1的垂直位置
- x1_all = np.append(x1_all, x1) # 记录轨迹
- y1_all = np.append(y1_all, y1) # 记录轨迹
- # 对物体2的计算
- a2_1 = G*m1/(distance12**2)
- a2_3 = G*m3/(distance23**2)
- a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
- a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
- v2_x = v2_x + a2_x*t
- v2_y = v2_y + a2_y*t
- x2 = x2 + v2_x*t
- y2 = y2 + v2_y*t
- x2_all = np.append(x2_all, x2)
- y2_all = np.append(y2_all, y2)
- # 对物体3的计算
- a3_1 = G*m1/(distance13**2)
- a3_2 = G*m2/(distance23**2)
- a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
- a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
- v3_x = v3_x + a3_x*t
- v3_y = v3_y + a3_y*t
- x3 = x3 + v3_x*t
- y3 = y3 + v3_y*t
- x3_all = np.append(x3_all, x3)
- y3_all = np.append(y3_all, y3)
-
- # 选择观测坐标
- axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
- axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
- while True:
- if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
- np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
- observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
- elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
- np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
- observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
- else:
- break
-
- plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
-
- plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，画出来的面积越大。视线范围越宽，球看起来越小。
- plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
- plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
- plt.plot(x1_all, y1_all, '-g') # 画轨迹
- plt.plot(x2_all, y2_all, '-r')
- plt.plot(x3_all, y3_all, '-b')
-
- # plt.show() # 显示图像
- # plt.pause(0.00001) # 暂停0.00001，防止画图过快
-
- if np.mod(i, 200) == 0:
- print(i0)
- plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
- i0 += 1
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+# os.chdir('E:/data') # 设置路径
+
+# 万有引力常数
+G = 1
+
+# 三体的质量
+m1 = 3 # 绿色
+m2 = 100 # 红色
+m3 = 10 # 蓝色
+
+# 三体的初始位置
+x1 = 0 # 绿色
+y1 = -100
+x2 = 0 # 红色
+y2 = 0
+x3 = 0 # 蓝色
+y3 = 50
+
+# 三体的初始速度
+v1_x = 1 # 绿色
+v1_y = 0
+v2_x = 0 # 红色
+v2_y = 0
+v3_x = 2 # 蓝色
+v3_y = 0
+
+# 步长
+t = 0.05
+
+plt.ion() # 开启交互模式
+observation_max = 100 # 观测坐标范围初始值
+x1_all = [x1] # 轨迹初始值
+y1_all = [y1]
+x2_all = [x2]
+y2_all = [y2]
+x3_all = [x3]
+y3_all = [y3]
+
+i0 = 0
+for i in range(100000000000):
+ distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
+ distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
+ distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
+ # 对物体1的计算
+ a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
+ a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
+ a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
+ a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
+ v1_x = v1_x + a1_x*t # 物体1的速度
+ v1_y = v1_y + a1_y*t # 物体1的速度
+ x1 = x1 + v1_x*t # 物体1的水平位置
+ y1 = y1 + v1_y*t # 物体1的垂直位置
+ x1_all = np.append(x1_all, x1) # 记录轨迹
+ y1_all = np.append(y1_all, y1) # 记录轨迹
+ # 对物体2的计算
+ a2_1 = G*m1/(distance12**2)
+ a2_3 = G*m3/(distance23**2)
+ a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
+ a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
+ v2_x = v2_x + a2_x*t
+ v2_y = v2_y + a2_y*t
+ x2 = x2 + v2_x*t
+ y2 = y2 + v2_y*t
+ x2_all = np.append(x2_all, x2)
+ y2_all = np.append(y2_all, y2)
+ # 对物体3的计算
+ a3_1 = G*m1/(distance13**2)
+ a3_2 = G*m2/(distance23**2)
+ a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
+ a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
+ v3_x = v3_x + a3_x*t
+ v3_y = v3_y + a3_y*t
+ x3 = x3 + v3_x*t
+ y3 = y3 + v3_y*t
+ x3_all = np.append(x3_all, x3)
+ y3_all = np.append(y3_all, y3)
+
+ # 选择观测坐标
+ axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
+ axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
+ while True:
+ if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
+ np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
+ observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
+ elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
+ np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
+ observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
+ else:
+ break
+
+ plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
+
+ plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，画出来的面积越大。视线范围越宽，球看起来越小。
+ plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
+ plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
+ plt.plot(x1_all, y1_all, '-g') # 画轨迹
+ plt.plot(x2_all, y2_all, '-r')
+ plt.plot(x3_all, y3_all, '-b')
+
+ # plt.show() # 显示图像
+ # plt.pause(0.00001) # 暂停0.00001，防止画图过快
+
+ if np.mod(i, 200) == 0:
+ print(i0)
+ plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
+ i0 += 1
plt.clf() # 清空
\ No newline at end of file
diff --git a/academic_codes/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.1.py b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.1.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.1.py
rename to academic_codes/others/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.1.py
index 3b23a24..0544ef6
--- a/academic_codes/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.1.py
+++ b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/relatively_big_mass_in_one_body/step_0.1.py
@@ -1,110 +1,110 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-# os.chdir('E:/data') # 设置路径
-
-# 万有引力常数
-G = 1
-
-# 三体的质量
-m1 = 3 # 绿色
-m2 = 100 # 红色
-m3 = 10 # 蓝色
-
-# 三体的初始位置
-x1 = 0 # 绿色
-y1 = -100
-x2 = 0 # 红色
-y2 = 0
-x3 = 0 # 蓝色
-y3 = 50
-
-# 三体的初始速度
-v1_x = 1 # 绿色
-v1_y = 0
-v2_x = 0 # 红色
-v2_y = 0
-v3_x = 2 # 蓝色
-v3_y = 0
-
-# 步长
-t = 0.1
-
-plt.ion() # 开启交互模式
-observation_max = 100 # 观测坐标范围初始值
-x1_all = [x1] # 轨迹初始值
-y1_all = [y1]
-x2_all = [x2]
-y2_all = [y2]
-x3_all = [x3]
-y3_all = [y3]
-
-i0 = 0
-for i in range(100000000000):
- distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
- distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
- distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
- # 对物体1的计算
- a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
- a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
- a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
- a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
- v1_x = v1_x + a1_x*t # 物体1的速度
- v1_y = v1_y + a1_y*t # 物体1的速度
- x1 = x1 + v1_x*t # 物体1的水平位置
- y1 = y1 + v1_y*t # 物体1的垂直位置
- x1_all = np.append(x1_all, x1) # 记录轨迹
- y1_all = np.append(y1_all, y1) # 记录轨迹
- # 对物体2的计算
- a2_1 = G*m1/(distance12**2)
- a2_3 = G*m3/(distance23**2)
- a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
- a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
- v2_x = v2_x + a2_x*t
- v2_y = v2_y + a2_y*t
- x2 = x2 + v2_x*t
- y2 = y2 + v2_y*t
- x2_all = np.append(x2_all, x2)
- y2_all = np.append(y2_all, y2)
- # 对物体3的计算
- a3_1 = G*m1/(distance13**2)
- a3_2 = G*m2/(distance23**2)
- a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
- a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
- v3_x = v3_x + a3_x*t
- v3_y = v3_y + a3_y*t
- x3 = x3 + v3_x*t
- y3 = y3 + v3_y*t
- x3_all = np.append(x3_all, x3)
- y3_all = np.append(y3_all, y3)
-
- # 选择观测坐标
- axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
- axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
- while True:
- if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
- np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
- observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
- elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
- np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
- observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
- else:
- break
-
- plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
-
- plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，画出来的面积越大。视线范围越宽，球看起来越小。
- plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
- plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
- plt.plot(x1_all, y1_all, '-g') # 画轨迹
- plt.plot(x2_all, y2_all, '-r')
- plt.plot(x3_all, y3_all, '-b')
-
- # plt.show() # 显示图像
- # plt.pause(0.00001) # 暂停0.00001，防止画图过快
-
- if np.mod(i, 100) == 0:
- print(i0)
- plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
- i0 += 1
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+# os.chdir('E:/data') # 设置路径
+
+# 万有引力常数
+G = 1
+
+# 三体的质量
+m1 = 3 # 绿色
+m2 = 100 # 红色
+m3 = 10 # 蓝色
+
+# 三体的初始位置
+x1 = 0 # 绿色
+y1 = -100
+x2 = 0 # 红色
+y2 = 0
+x3 = 0 # 蓝色
+y3 = 50
+
+# 三体的初始速度
+v1_x = 1 # 绿色
+v1_y = 0
+v2_x = 0 # 红色
+v2_y = 0
+v3_x = 2 # 蓝色
+v3_y = 0
+
+# 步长
+t = 0.1
+
+plt.ion() # 开启交互模式
+observation_max = 100 # 观测坐标范围初始值
+x1_all = [x1] # 轨迹初始值
+y1_all = [y1]
+x2_all = [x2]
+y2_all = [y2]
+x3_all = [x3]
+y3_all = [y3]
+
+i0 = 0
+for i in range(100000000000):
+ distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
+ distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
+ distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
+ # 对物体1的计算
+ a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
+ a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
+ a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
+ a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
+ v1_x = v1_x + a1_x*t # 物体1的速度
+ v1_y = v1_y + a1_y*t # 物体1的速度
+ x1 = x1 + v1_x*t # 物体1的水平位置
+ y1 = y1 + v1_y*t # 物体1的垂直位置
+ x1_all = np.append(x1_all, x1) # 记录轨迹
+ y1_all = np.append(y1_all, y1) # 记录轨迹
+ # 对物体2的计算
+ a2_1 = G*m1/(distance12**2)
+ a2_3 = G*m3/(distance23**2)
+ a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
+ a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
+ v2_x = v2_x + a2_x*t
+ v2_y = v2_y + a2_y*t
+ x2 = x2 + v2_x*t
+ y2 = y2 + v2_y*t
+ x2_all = np.append(x2_all, x2)
+ y2_all = np.append(y2_all, y2)
+ # 对物体3的计算
+ a3_1 = G*m1/(distance13**2)
+ a3_2 = G*m2/(distance23**2)
+ a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
+ a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
+ v3_x = v3_x + a3_x*t
+ v3_y = v3_y + a3_y*t
+ x3 = x3 + v3_x*t
+ y3 = y3 + v3_y*t
+ x3_all = np.append(x3_all, x3)
+ y3_all = np.append(y3_all, y3)
+
+ # 选择观测坐标
+ axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
+ axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
+ while True:
+ if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
+ np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
+ observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
+ elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
+ np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
+ observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
+ else:
+ break
+
+ plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
+
+ plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，画出来的面积越大。视线范围越宽，球看起来越小。
+ plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
+ plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
+ plt.plot(x1_all, y1_all, '-g') # 画轨迹
+ plt.plot(x2_all, y2_all, '-r')
+ plt.plot(x3_all, y3_all, '-b')
+
+ # plt.show() # 显示图像
+ # plt.pause(0.00001) # 暂停0.00001，防止画图过快
+
+ if np.mod(i, 100) == 0:
+ print(i0)
+ plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
+ i0 += 1
plt.clf() # 清空
\ No newline at end of file
diff --git a/academic_codes/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.05.py b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.05.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.05.py
rename to academic_codes/others/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.05.py
index a2f84f6..cd4e41f
--- a/academic_codes/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.05.py
+++ b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.05.py
@@ -1,113 +1,113 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-# os.chdir('E:/data') # 设置路径
-
-# 万有引力常数
-G = 1
-
-# 三体的质量
-m1 = 15 # 绿色
-m2 = 12 # 红色
-m3 = 8 # 蓝色
-
-# 三体的初始位置
-x1 = 300 # 绿色
-y1 = 50
-x2 = -100 # 红色
-y2 = -200
-x3 = -100 # 蓝色
-y3 = 150
-
-# 三体的初始速度
-v1_x = 0 # 绿色
-v1_y = 0
-v2_x = 0 # 红色
-v2_y = 0
-v3_x = 0 # 蓝色
-v3_y = 0
-
-# 步长
-t = 0.05
-
-plt.ion() # 开启交互模式
-observation_max = 100 # 视线范围初始值
-x1_all = [x1] # 轨迹初始值
-y1_all = [y1]
-x2_all = [x2]
-y2_all = [y2]
-x3_all = [x3]
-y3_all = [y3]
-
-i0 = 0
-for i in range(1000000):
- distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
- distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
- distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
-
- # 对物体1的计算
- a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
- a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
- a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
- a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
- v1_x = v1_x + a1_x*t # 物体1的速度
- v1_y = v1_y + a1_y*t # 物体1的速度
- x1 = x1 + v1_x*t # 物体1的水平位置
- y1 = y1 + v1_y*t # 物体1的垂直位置
- x1_all = np.append(x1_all, x1) # 记录轨迹
- y1_all = np.append(y1_all, y1) # 记录轨迹
-
- # 对物体2的计算
- a2_1 = G*m1/(distance12**2)
- a2_3 = G*m3/(distance23**2)
- a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
- a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
- v2_x = v2_x + a2_x*t
- v2_y = v2_y + a2_y*t
- x2 = x2 + v2_x*t
- y2 = y2 + v2_y*t
- x2_all = np.append(x2_all, x2)
- y2_all = np.append(y2_all, y2)
-
- # 对物体3的计算
- a3_1 = G*m1/(distance13**2)
- a3_2 = G*m2/(distance23**2)
- a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
- a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
- v3_x = v3_x + a3_x*t
- v3_y = v3_y + a3_y*t
- x3 = x3 + v3_x*t
- y3 = y3 + v3_y*t
- x3_all = np.append(x3_all, x3)
- y3_all = np.append(y3_all, y3)
-
- # 选择观测坐标
- axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
- axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
- while True:
- if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
- np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
- observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
- elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
- np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
- observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
- else:
- break
-
- plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
-
- plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，球面积越大。视线范围越宽，球看起来越小。
- plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
- plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
- plt.plot(x1_all, y1_all, '-g') # 画轨迹
- plt.plot(x2_all, y2_all, '-r')
- plt.plot(x3_all, y3_all, '-b')
-
- # plt.show() # 显示图像
- # plt.pause(0.00001) # 暂停0.00001，防止画图过快
-
- if np.mod(i, 1000) == 0: # 当运动明显时，把图画出来
- print(i0)
- plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
- i0 += 1
- plt.clf() # 清空
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+# os.chdir('E:/data') # 设置路径
+
+# 万有引力常数
+G = 1
+
+# 三体的质量
+m1 = 15 # 绿色
+m2 = 12 # 红色
+m3 = 8 # 蓝色
+
+# 三体的初始位置
+x1 = 300 # 绿色
+y1 = 50
+x2 = -100 # 红色
+y2 = -200
+x3 = -100 # 蓝色
+y3 = 150
+
+# 三体的初始速度
+v1_x = 0 # 绿色
+v1_y = 0
+v2_x = 0 # 红色
+v2_y = 0
+v3_x = 0 # 蓝色
+v3_y = 0
+
+# 步长
+t = 0.05
+
+plt.ion() # 开启交互模式
+observation_max = 100 # 视线范围初始值
+x1_all = [x1] # 轨迹初始值
+y1_all = [y1]
+x2_all = [x2]
+y2_all = [y2]
+x3_all = [x3]
+y3_all = [y3]
+
+i0 = 0
+for i in range(1000000):
+ distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
+ distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
+ distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
+
+ # 对物体1的计算
+ a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
+ a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
+ a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
+ a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
+ v1_x = v1_x + a1_x*t # 物体1的速度
+ v1_y = v1_y + a1_y*t # 物体1的速度
+ x1 = x1 + v1_x*t # 物体1的水平位置
+ y1 = y1 + v1_y*t # 物体1的垂直位置
+ x1_all = np.append(x1_all, x1) # 记录轨迹
+ y1_all = np.append(y1_all, y1) # 记录轨迹
+
+ # 对物体2的计算
+ a2_1 = G*m1/(distance12**2)
+ a2_3 = G*m3/(distance23**2)
+ a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
+ a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
+ v2_x = v2_x + a2_x*t
+ v2_y = v2_y + a2_y*t
+ x2 = x2 + v2_x*t
+ y2 = y2 + v2_y*t
+ x2_all = np.append(x2_all, x2)
+ y2_all = np.append(y2_all, y2)
+
+ # 对物体3的计算
+ a3_1 = G*m1/(distance13**2)
+ a3_2 = G*m2/(distance23**2)
+ a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
+ a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
+ v3_x = v3_x + a3_x*t
+ v3_y = v3_y + a3_y*t
+ x3 = x3 + v3_x*t
+ y3 = y3 + v3_y*t
+ x3_all = np.append(x3_all, x3)
+ y3_all = np.append(y3_all, y3)
+
+ # 选择观测坐标
+ axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
+ axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
+ while True:
+ if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
+ np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
+ observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
+ elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
+ np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
+ observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
+ else:
+ break
+
+ plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
+
+ plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，球面积越大。视线范围越宽，球看起来越小。
+ plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
+ plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
+ plt.plot(x1_all, y1_all, '-g') # 画轨迹
+ plt.plot(x2_all, y2_all, '-r')
+ plt.plot(x3_all, y3_all, '-b')
+
+ # plt.show() # 显示图像
+ # plt.pause(0.00001) # 暂停0.00001，防止画图过快
+
+ if np.mod(i, 1000) == 0: # 当运动明显时，把图画出来
+ print(i0)
+ plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
+ i0 += 1
+ plt.clf() # 清空
diff --git a/academic_codes/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.1.py b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.1.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.1.py
rename to academic_codes/others/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.1.py
index 2a37f99..c579920
--- a/academic_codes/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.1.py
+++ b/academic_codes/others/2019.11.03_three_body_moving_on_2D_space/three_body_mass_with_little_difference/step_0.1.py
@@ -1,113 +1,113 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import os
-# os.chdir('E:/data') # 设置路径
-
-# 万有引力常数
-G = 1
-
-# 三体的质量
-m1 = 15 # 绿色
-m2 = 12 # 红色
-m3 = 8 # 蓝色
-
-# 三体的初始位置
-x1 = 300 # 绿色
-y1 = 50
-x2 = -100 # 红色
-y2 = -200
-x3 = -100 # 蓝色
-y3 = 150
-
-# 三体的初始速度
-v1_x = 0 # 绿色
-v1_y = 0
-v2_x = 0 # 红色
-v2_y = 0
-v3_x = 0 # 蓝色
-v3_y = 0
-
-# 步长
-t = 0.1
-
-plt.ion() # 开启交互模式
-observation_max = 100 # 视线范围初始值
-x1_all = [x1] # 轨迹初始值
-y1_all = [y1]
-x2_all = [x2]
-y2_all = [y2]
-x3_all = [x3]
-y3_all = [y3]
-
-i0 = 0
-for i in range(1000000):
- distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
- distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
- distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
-
- # 对物体1的计算
- a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
- a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
- a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
- a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
- v1_x = v1_x + a1_x*t # 物体1的速度
- v1_y = v1_y + a1_y*t # 物体1的速度
- x1 = x1 + v1_x*t # 物体1的水平位置
- y1 = y1 + v1_y*t # 物体1的垂直位置
- x1_all = np.append(x1_all, x1) # 记录轨迹
- y1_all = np.append(y1_all, y1) # 记录轨迹
-
- # 对物体2的计算
- a2_1 = G*m1/(distance12**2)
- a2_3 = G*m3/(distance23**2)
- a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
- a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
- v2_x = v2_x + a2_x*t
- v2_y = v2_y + a2_y*t
- x2 = x2 + v2_x*t
- y2 = y2 + v2_y*t
- x2_all = np.append(x2_all, x2)
- y2_all = np.append(y2_all, y2)
-
- # 对物体3的计算
- a3_1 = G*m1/(distance13**2)
- a3_2 = G*m2/(distance23**2)
- a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
- a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
- v3_x = v3_x + a3_x*t
- v3_y = v3_y + a3_y*t
- x3 = x3 + v3_x*t
- y3 = y3 + v3_y*t
- x3_all = np.append(x3_all, x3)
- y3_all = np.append(y3_all, y3)
-
- # 选择观测坐标
- axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
- axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
- while True:
- if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
- np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
- observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
- elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
- np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
- observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
- else:
- break
-
- plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
-
- plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，球面积越大。视线范围越宽，球看起来越小。
- plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
- plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
- plt.plot(x1_all, y1_all, '-g') # 画轨迹
- plt.plot(x2_all, y2_all, '-r')
- plt.plot(x3_all, y3_all, '-b')
-
- # plt.show() # 显示图像
- # plt.pause(0.00001) # 暂停0.00001，防止画图过快
-
- if np.mod(i, 500) == 0: # 当运动明显时，把图画出来
- print(i0)
- plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
- i0 += 1
- plt.clf() # 清空
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+# os.chdir('E:/data') # 设置路径
+
+# 万有引力常数
+G = 1
+
+# 三体的质量
+m1 = 15 # 绿色
+m2 = 12 # 红色
+m3 = 8 # 蓝色
+
+# 三体的初始位置
+x1 = 300 # 绿色
+y1 = 50
+x2 = -100 # 红色
+y2 = -200
+x3 = -100 # 蓝色
+y3 = 150
+
+# 三体的初始速度
+v1_x = 0 # 绿色
+v1_y = 0
+v2_x = 0 # 红色
+v2_y = 0
+v3_x = 0 # 蓝色
+v3_y = 0
+
+# 步长
+t = 0.1
+
+plt.ion() # 开启交互模式
+observation_max = 100 # 视线范围初始值
+x1_all = [x1] # 轨迹初始值
+y1_all = [y1]
+x2_all = [x2]
+y2_all = [y2]
+x3_all = [x3]
+y3_all = [y3]
+
+i0 = 0
+for i in range(1000000):
+ distance12 = np.sqrt((x1-x2)**2+(y1-y2)**2) # 物体1和物体2之间的距离
+ distance13 = np.sqrt((x1-x3)**2+(y1-y3)**2) # 物体1和物体3之间的距离
+ distance23 = np.sqrt((x2-x3)**2+(y2-y3)**2) # 物体2和物体3之间的距离
+
+ # 对物体1的计算
+ a1_2 = G*m2/(distance12**2) # 物体2对物体1的加速度（用上万有引力公式）
+ a1_3 = G*m3/(distance13**2) # 物体3对物体1的加速度
+ a1_x = a1_2*(x2-x1)/distance12 + a1_3*(x3-x1)/distance13 # 物体1受到的水平加速度
+ a1_y = a1_2*(y2-y1)/distance12 + a1_3*(y3-y1)/distance13 # 物体1受到的垂直加速度
+ v1_x = v1_x + a1_x*t # 物体1的速度
+ v1_y = v1_y + a1_y*t # 物体1的速度
+ x1 = x1 + v1_x*t # 物体1的水平位置
+ y1 = y1 + v1_y*t # 物体1的垂直位置
+ x1_all = np.append(x1_all, x1) # 记录轨迹
+ y1_all = np.append(y1_all, y1) # 记录轨迹
+
+ # 对物体2的计算
+ a2_1 = G*m1/(distance12**2)
+ a2_3 = G*m3/(distance23**2)
+ a2_x = a2_1*(x1-x2)/distance12 + a2_3*(x3-x2)/distance23
+ a2_y = a2_1*(y1-y2)/distance12 + a2_3*(y3-y2)/distance23
+ v2_x = v2_x + a2_x*t
+ v2_y = v2_y + a2_y*t
+ x2 = x2 + v2_x*t
+ y2 = y2 + v2_y*t
+ x2_all = np.append(x2_all, x2)
+ y2_all = np.append(y2_all, y2)
+
+ # 对物体3的计算
+ a3_1 = G*m1/(distance13**2)
+ a3_2 = G*m2/(distance23**2)
+ a3_x = a3_1*(x1-x3)/distance13 + a3_2*(x2-x3)/distance23
+ a3_y = a3_1*(y1-y3)/distance13 + a3_2*(y2-y3)/distance23
+ v3_x = v3_x + a3_x*t
+ v3_y = v3_y + a3_y*t
+ x3 = x3 + v3_x*t
+ y3 = y3 + v3_y*t
+ x3_all = np.append(x3_all, x3)
+ y3_all = np.append(y3_all, y3)
+
+ # 选择观测坐标
+ axis_x = np.mean([x1, x2, x3]) # 观测坐标中心固定在平均值的地方
+ axis_y = np.mean([y1, y2, y3]) # 观测坐标中心固定在平均值的地方
+ while True:
+ if np.abs(x1-axis_x) > observation_max or np.abs(x2-axis_x) > observation_max or np.abs(x3-axis_x) > observation_max or\
+ np.abs(y1-axis_y) > observation_max or np.abs(y2-axis_y) > observation_max or np.abs(y3-axis_y) > observation_max:
+ observation_max = observation_max * 2 # 有一个物体超出视线时，视线范围翻倍
+ elif np.abs(x1-axis_x) < observation_max/10 and np.abs(x2-axis_x) < observation_max/10 and np.abs(x3-axis_x) < observation_max/10 and\
+ np.abs(y1-axis_y) < observation_max/10 and np.abs(y2-axis_y) < observation_max/10 and np.abs(y3-axis_y) < observation_max/10:
+ observation_max = observation_max / 2 # 所有物体都在的视线的10分之一内，视线范围减半
+ else:
+ break
+
+ plt.axis([axis_x-observation_max, axis_x+observation_max, axis_y-observation_max, axis_y+observation_max])
+
+ plt.plot(x1, y1, 'og', markersize=m1*100/observation_max) # 默认密度相同，质量越大的，球面积越大。视线范围越宽，球看起来越小。
+ plt.plot(x2, y2, 'or', markersize=m2*100/observation_max)
+ plt.plot(x3, y3, 'ob', markersize=m3*100/observation_max)
+ plt.plot(x1_all, y1_all, '-g') # 画轨迹
+ plt.plot(x2_all, y2_all, '-r')
+ plt.plot(x3_all, y3_all, '-b')
+
+ # plt.show() # 显示图像
+ # plt.pause(0.00001) # 暂停0.00001，防止画图过快
+
+ if np.mod(i, 500) == 0: # 当运动明显时，把图画出来
+ print(i0)
+ plt.savefig(str(i0)+'.jpg') # 保存为图片可以用来做动画
+ i0 += 1
+ plt.clf() # 清空
diff --git a/academic_codes/2019.11.07_definite_integration_with_Monte_Carlo_method/definite_integration_with_Monte_Carlo_method.py b/academic_codes/others/2019.11.07_definite_integration_with_Monte_Carlo_method/definite_integration_with_Monte_Carlo_method.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2019.11.07_definite_integration_with_Monte_Carlo_method/definite_integration_with_Monte_Carlo_method.py
rename to academic_codes/others/2019.11.07_definite_integration_with_Monte_Carlo_method/definite_integration_with_Monte_Carlo_method.py
index 3c01139..686fecb
--- a/academic_codes/2019.11.07_definite_integration_with_Monte_Carlo_method/definite_integration_with_Monte_Carlo_method.py
+++ b/academic_codes/others/2019.11.07_definite_integration_with_Monte_Carlo_method/definite_integration_with_Monte_Carlo_method.py
@@ -1,73 +1,73 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/1145
-"""
-
-import numpy as np
-import random
-import time
-
-
-def integral(): # 直接数值积分
- integral_value = 0
- for x in np.arange(0, 1, 1/10**7):
- integral_value = integral_value + x**2*(1/10**7) # 对x^2在0和1之间积分
- return integral_value
-
-
-def MC_1(): # 蒙特卡洛求定积分1：投点法
- n = 10**7
- x_min, x_max = 0.0, 1.0
- y_min, y_max = 0.0, 1.0
- count = 0
- for i in range(0, n):
- x = random.uniform(x_min, x_max)
- y = random.uniform(y_min, y_max)
- # x*x > y，表示该点位于曲线的下面。所求的积分值即为曲线下方的面积与正方形面积的比。
- if x * x > y:
- count += 1
- integral_value = count / n
- return integral_value
-
-
-def MC_2(): # 蒙特卡洛求定积分2：期望法
- n = 10**7
- x_min, x_max = 0.0, 1.0
- integral_value = 0
- for i in range(n):
- x = random.uniform(x_min, x_max)
- integral_value = integral_value + (1-0)*x**2
- integral_value = integral_value/n
- return integral_value
-
-
-print('【计算时间】')
-start_clock = time.perf_counter() # 或者用time.clock()
-a00 = 1/3 # 理论值
-end_clock = time.perf_counter()
-print('理论值（解析）：', end_clock-start_clock)
-start_clock = time.perf_counter()
-a0 = integral() # 直接数值积分
-end_clock = time.perf_counter()
-print('直接数值积分：', end_clock-start_clock)
-start_clock = time.perf_counter()
-a1 = MC_1() # 用蒙特卡洛求积分投点法
-end_clock = time.perf_counter()
-print('用蒙特卡洛求积分_投点法：', end_clock-start_clock)
-start_clock = time.perf_counter()
-a2 = MC_2()
-end_clock = time.perf_counter()
-print('用蒙特卡洛求积分_期望法：', end_clock-start_clock, '\n')
-
-print('【计算结果】')
-print('理论值（解析）：', a00)
-print('直接数值积分：', a0)
-print('用蒙特卡洛求积分_投点法：', a1)
-print('用蒙特卡洛求积分_期望法：', a2, '\n')
-
-print('【计算误差】')
-print('理论值（解析）：', 0)
-print('直接数值积分：', abs(a0-1/3))
-print('用蒙特卡洛求积分_投点法：', abs(a1-1/3))
-print('用蒙特卡洛求积分_期望法：', abs(a2-1/3))
-
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/1145
+"""
+
+import numpy as np
+import random
+import time
+
+
+def integral(): # 直接数值积分
+ integral_value = 0
+ for x in np.arange(0, 1, 1/10**7):
+ integral_value = integral_value + x**2*(1/10**7) # 对x^2在0和1之间积分
+ return integral_value
+
+
+def MC_1(): # 蒙特卡洛求定积分1：投点法
+ n = 10**7
+ x_min, x_max = 0.0, 1.0
+ y_min, y_max = 0.0, 1.0
+ count = 0
+ for i in range(0, n):
+ x = random.uniform(x_min, x_max)
+ y = random.uniform(y_min, y_max)
+ # x*x > y，表示该点位于曲线的下面。所求的积分值即为曲线下方的面积与正方形面积的比。
+ if x * x > y:
+ count += 1
+ integral_value = count / n
+ return integral_value
+
+
+def MC_2(): # 蒙特卡洛求定积分2：期望法
+ n = 10**7
+ x_min, x_max = 0.0, 1.0
+ integral_value = 0
+ for i in range(n):
+ x = random.uniform(x_min, x_max)
+ integral_value = integral_value + (1-0)*x**2
+ integral_value = integral_value/n
+ return integral_value
+
+
+print('【计算时间】')
+start_clock = time.perf_counter() # 或者用time.clock()
+a00 = 1/3 # 理论值
+end_clock = time.perf_counter()
+print('理论值（解析）：', end_clock-start_clock)
+start_clock = time.perf_counter()
+a0 = integral() # 直接数值积分
+end_clock = time.perf_counter()
+print('直接数值积分：', end_clock-start_clock)
+start_clock = time.perf_counter()
+a1 = MC_1() # 用蒙特卡洛求积分投点法
+end_clock = time.perf_counter()
+print('用蒙特卡洛求积分_投点法：', end_clock-start_clock)
+start_clock = time.perf_counter()
+a2 = MC_2()
+end_clock = time.perf_counter()
+print('用蒙特卡洛求积分_期望法：', end_clock-start_clock, '\n')
+
+print('【计算结果】')
+print('理论值（解析）：', a00)
+print('直接数值积分：', a0)
+print('用蒙特卡洛求积分_投点法：', a1)
+print('用蒙特卡洛求积分_期望法：', a2, '\n')
+
+print('【计算误差】')
+print('理论值（解析）：', 0)
+print('直接数值积分：', abs(a0-1/3))
+print('用蒙特卡洛求积分_投点法：', abs(a1-1/3))
+print('用蒙特卡洛求积分_期望法：', abs(a2-1/3))
+
diff --git a/academic_codes/2019.12.01_Metropolis_sampling/Metropolis_sampling_example_1.py b/academic_codes/others/2019.12.01_Metropolis_sampling/Metropolis_sampling_example_1.py
similarity index 100%
rename from academic_codes/2019.12.01_Metropolis_sampling/Metropolis_sampling_example_1.py
rename to academic_codes/others/2019.12.01_Metropolis_sampling/Metropolis_sampling_example_1.py
diff --git a/academic_codes/2019.12.01_Metropolis_sampling/Metropolis_sampling_example_2.m b/academic_codes/others/2019.12.01_Metropolis_sampling/Metropolis_sampling_example_2.m
similarity index 100%
rename from academic_codes/2019.12.01_Metropolis_sampling/Metropolis_sampling_example_2.m
rename to academic_codes/others/2019.12.01_Metropolis_sampling/Metropolis_sampling_example_2.m
diff --git a/academic_codes/2019.12.01_simulation_of_Ising_model_with_Monte_Carlo_method/Ising_model.py b/academic_codes/others/2019.12.01_simulation_of_Ising_model_with_Monte_Carlo_method/Ising_model.py
similarity index 100%
rename from academic_codes/2019.12.01_simulation_of_Ising_model_with_Monte_Carlo_method/Ising_model.py
rename to academic_codes/others/2019.12.01_simulation_of_Ising_model_with_Monte_Carlo_method/Ising_model.py
diff --git a/academic_codes/2019.12.01_simulation_of_Ising_model_with_Monte_Carlo_method/get_the_transition_temperature_by_calculating_magnetic_moments_in_Ising_model.py b/academic_codes/others/2019.12.01_simulation_of_Ising_model_with_Monte_Carlo_method/get_the_transition_temperature_by_calculating_magnetic_moments_in_Ising_model.py
similarity index 100%
rename from academic_codes/2019.12.01_simulation_of_Ising_model_with_Monte_Carlo_method/get_the_transition_temperature_by_calculating_magnetic_moments_in_Ising_model.py
rename to academic_codes/others/2019.12.01_simulation_of_Ising_model_with_Monte_Carlo_method/get_the_transition_temperature_by_calculating_magnetic_moments_in_Ising_model.py
diff --git a/academic_codes/2020.10.30_time_complexity_dealing_with_matrix/time_complexity_dealing_with_matrix.py b/academic_codes/others/2020.10.30_time_complexity_dealing_with_matrix/time_complexity_dealing_with_matrix.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.10.30_time_complexity_dealing_with_matrix/time_complexity_dealing_with_matrix.py
rename to academic_codes/others/2020.10.30_time_complexity_dealing_with_matrix/time_complexity_dealing_with_matrix.py
index 5412639..a93ea23
--- a/academic_codes/2020.10.30_time_complexity_dealing_with_matrix/time_complexity_dealing_with_matrix.py
+++ b/academic_codes/others/2020.10.30_time_complexity_dealing_with_matrix/time_complexity_dealing_with_matrix.py
@@ -1,61 +1,61 @@
-import numpy as np
-import matplotlib.pyplot as plt
-import time
-
-
-
-time_1 = np.array([])
-time_2 = np.array([])
-time_3 = np.array([])
-n_all = np.arange(2,5000,200) # 测试的范围
-start_all = time.process_time()
-for n in n_all:
- print(n)
- matrix_1 = np.zeros((n,n))
- matrix_2 = np.zeros((n,n))
- for i0 in range(n):
- for j0 in range(n):
- matrix_1[i0,j0] = np.random.uniform(-10, 10)
- for i0 in range(n):
- for j0 in range(n):
- matrix_2[i0,j0] = np.random.uniform(-10, 10)
-
- start = time.process_time()
- matrix_3 = np.dot(matrix_1, matrix_2) # 矩阵乘积
- end = time.process_time()
- time_1 = np.append(time_1, [end-start], axis=0)
-
- start = time.process_time()
- matrix_4 = np.linalg.inv(matrix_1) # 矩阵求逆
- end = time.process_time()
- time_2 = np.append(time_2, [end-start], axis=0)
-
- start = time.process_time()
- eigenvalue, eigenvector = np.linalg.eig(matrix_1) # 求矩阵本征值
- end = time.process_time()
- time_3 = np.append(time_3, [end-start], axis=0)
-
-
-
-end_all = time.process_time()
-print('总共运行时间：', (end_all-start_all)/60, '分')
-
-plt.subplot(131)
-plt.xlabel('n^3/10^9')
-plt.ylabel('时间（秒）')
-plt.title('矩阵乘积')
-plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_1, 'o-')
-
-plt.subplot(132)
-plt.xlabel('n^3/10^9')
-plt.title('矩阵求逆')
-plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_2, 'o-')
-
-plt.subplot(133)
-plt.xlabel('n^3/10^9')
-plt.title('求矩阵本征值')
-plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_3, 'o-')
-
-plt.rcParams['font.sans-serif'] = ['SimHei'] # 在画图中正常显示中文
-plt.rcParams['axes.unicode_minus'] = False # 中文化后，加上这个使正常显示负号
+import numpy as np
+import matplotlib.pyplot as plt
+import time
+
+
+
+time_1 = np.array([])
+time_2 = np.array([])
+time_3 = np.array([])
+n_all = np.arange(2,5000,200) # 测试的范围
+start_all = time.process_time()
+for n in n_all:
+ print(n)
+ matrix_1 = np.zeros((n,n))
+ matrix_2 = np.zeros((n,n))
+ for i0 in range(n):
+ for j0 in range(n):
+ matrix_1[i0,j0] = np.random.uniform(-10, 10)
+ for i0 in range(n):
+ for j0 in range(n):
+ matrix_2[i0,j0] = np.random.uniform(-10, 10)
+
+ start = time.process_time()
+ matrix_3 = np.dot(matrix_1, matrix_2) # 矩阵乘积
+ end = time.process_time()
+ time_1 = np.append(time_1, [end-start], axis=0)
+
+ start = time.process_time()
+ matrix_4 = np.linalg.inv(matrix_1) # 矩阵求逆
+ end = time.process_time()
+ time_2 = np.append(time_2, [end-start], axis=0)
+
+ start = time.process_time()
+ eigenvalue, eigenvector = np.linalg.eig(matrix_1) # 求矩阵本征值
+ end = time.process_time()
+ time_3 = np.append(time_3, [end-start], axis=0)
+
+
+
+end_all = time.process_time()
+print('总共运行时间：', (end_all-start_all)/60, '分')
+
+plt.subplot(131)
+plt.xlabel('n^3/10^9')
+plt.ylabel('时间（秒）')
+plt.title('矩阵乘积')
+plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_1, 'o-')
+
+plt.subplot(132)
+plt.xlabel('n^3/10^9')
+plt.title('矩阵求逆')
+plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_2, 'o-')
+
+plt.subplot(133)
+plt.xlabel('n^3/10^9')
+plt.title('求矩阵本征值')
+plt.plot((n_all/10**3)*(n_all/10**3)*(n_all/10**3), time_3, 'o-')
+
+plt.rcParams['font.sans-serif'] = ['SimHei'] # 在画图中正常显示中文
+plt.rcParams['axes.unicode_minus'] = False # 中文化后，加上这个使正常显示负号
plt.show()
\ No newline at end of file
diff --git a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_Eigenvectors.nb b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_Eigenvectors.nb
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_Eigenvectors.nb
rename to academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_Eigenvectors.nb
index e654c98..fedd8ff
--- a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_Eigenvectors.nb
+++ b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_Eigenvectors.nb
@@ -1,73 +1,73 @@
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-
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-
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-
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-(* Internal cache information:
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-CellTagsIndexPosition[ 2032, 64]
-WindowFrame->Normal*)
-
-(* Beginning of Notebook Content *)
-Notebook[{
-Cell[BoxData[
- RowBox[{"\[IndentingNewLine]",
- RowBox[{
- RowBox[{"Clear", "[", "\"\<`*\>\"", "]"}], "\[IndentingNewLine]",
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- RowBox[{"{",
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- RowBox[{"3", ",", "2", ",",
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- RowBox[{"{",
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-*)
-
+(* Content-type: application/vnd.wolfram.mathematica *)
+
+(*** Wolfram Notebook File ***)
+(* http://www.wolfram.com/nb *)
+
+(* CreatedBy='Mathematica 12.0' *)
+
+(*CacheID: 234*)
+(* Internal cache information:
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+NotebookFileLineBreakTest
+NotebookDataPosition[ 158, 7]
+NotebookDataLength[ 2028, 65]
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+CellTagsIndexPosition[ 2032, 64]
+WindowFrame->Normal*)
+
+(* Beginning of Notebook Content *)
+Notebook[{
+Cell[BoxData[
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+ 3.824909243121014*^9}},
+ CellLabel->"In[24]:=",ExpressionUUID->"54dc89aa-0cf7-4c91-9e21-ea7b96cf97e8"]
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+}
+]
+*)
+
diff --git a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_eig.m b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_eig.m
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_eig.m
rename to academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_eig.m
index 1439f2f..4a3ae9d
--- a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_eig.m
+++ b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_eig.m
@@ -1,3 +1,3 @@
-clc;clear all;
-A = [3, 2, -1; -2, -2, 2; 3, 6, -1]
+clc;clear all;
+A = [3, 2, -1; -2, -2, 2; 3, 6, -1]
[V,D] = eig(A)
\ No newline at end of file
diff --git a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_geev.f90 b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_geev.f90
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_geev.f90
rename to academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_geev.f90
index 55b5d7f..a3f02f9
--- a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_geev.f90
+++ b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_geev.f90
@@ -1,40 +1,40 @@
-program main
- use lapack95
- implicit none
- integer i,j,info
- complex*16 A(3,3), eigenvalues(3), eigenvectors(3,3)
-
- A(1,1)=(3.d0, 0.d0)
- A(1,2)=(2.d0, 0.d0)
- A(1,3)=(-1.d0, 0.d0)
- A(2,1)=(-2.d0, 0.d0)
- A(2,2)=(-2.d0, 0.d0)
- A(2,3)=(2.d0, 0.d0)
- A(3,1)=(3.d0, 0.d0)
- A(3,2)=(6.d0, 0.d0)
- A(3,3)=(-1.d0, 0.d0)
-
- write(*,*) 'matrix:'
- do i=1,3
- do j=1,3
- write(*,"(f10.4, '+1i*',f7.4)",advance='no') A(i,j) ! ѭΪеָ
- enddo
- write(*,*) ''
- enddo
-
- call geev(A=A, W=eigenvalues, VR=eigenvectors, INFO=info)
-
- write(*,*) 'eigenvectors:'
- do i=1,3
- do j=1,3
- write(*,"(f10.4, '+1i*',f7.4)",advance='no') eigenvectors(i,j) ! ѭΪеָꡣΪ
- enddo
- write(*,*) ''
- enddo
- write(*,*) 'eigenvalues:'
- do i=1,3
- write(*,"(f10.4, '+1i*',f7.4)",advance='no') eigenvalues(i)
- enddo
- write(*,*) ''
- write(*,*) ''
+program main
+ use lapack95
+ implicit none
+ integer i,j,info
+ complex*16 A(3,3), eigenvalues(3), eigenvectors(3,3)
+
+ A(1,1)=(3.d0, 0.d0)
+ A(1,2)=(2.d0, 0.d0)
+ A(1,3)=(-1.d0, 0.d0)
+ A(2,1)=(-2.d0, 0.d0)
+ A(2,2)=(-2.d0, 0.d0)
+ A(2,3)=(2.d0, 0.d0)
+ A(3,1)=(3.d0, 0.d0)
+ A(3,2)=(6.d0, 0.d0)
+ A(3,3)=(-1.d0, 0.d0)
+
+ write(*,*) 'matrix:'
+ do i=1,3
+ do j=1,3
+ write(*,"(f10.4, '+1i*',f7.4)",advance='no') A(i,j) ! ѭΪеָ
+ enddo
+ write(*,*) ''
+ enddo
+
+ call geev(A=A, W=eigenvalues, VR=eigenvectors, INFO=info)
+
+ write(*,*) 'eigenvectors:'
+ do i=1,3
+ do j=1,3
+ write(*,"(f10.4, '+1i*',f7.4)",advance='no') eigenvectors(i,j) ! ѭΪеָꡣΪ
+ enddo
+ write(*,*) ''
+ enddo
+ write(*,*) 'eigenvalues:'
+ do i=1,3
+ write(*,"(f10.4, '+1i*',f7.4)",advance='no') eigenvalues(i)
+ enddo
+ write(*,*) ''
+ write(*,*) ''
end program
\ No newline at end of file
diff --git a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_hermitian_matrix.py b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_hermitian_matrix.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_hermitian_matrix.py
rename to academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_hermitian_matrix.py
index 8815fd0..ff43857
--- a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_hermitian_matrix.py
+++ b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_hermitian_matrix.py
@@ -1,21 +1,21 @@
-import numpy as np
-
-A = np.array([[3, 2+1j, -1], [2-1j, -2, 6], [-1, 6, 1]])
-eigenvalue, eigenvector = np.linalg.eig(A)
-print('矩阵：\n', A)
-print('特征值:\n', eigenvalue)
-print('特征向量:\n', eigenvector)
-
-print('\n判断是否正交：\n', np.dot(eigenvector.transpose().conj(), eigenvector))
-print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose().conj()))
-
-print('特征向量矩阵的列向量模方和：')
-for i in range(3):
- print(np.abs(eigenvector[0, i])**2+np.abs(eigenvector[1, i])**2+np.abs(eigenvector[2, i])**2)
-print('特征向量矩阵的行向量模方和：')
-for i in range(3):
- print(np.abs(eigenvector[i, 0])**2+np.abs(eigenvector[i, 1])**2+np.abs(eigenvector[i, 2])**2)
-
-print('\n对角化验证：')
-print(np.dot(np.dot(eigenvector.transpose().conj(), A), eigenvector))
+import numpy as np
+
+A = np.array([[3, 2+1j, -1], [2-1j, -2, 6], [-1, 6, 1]])
+eigenvalue, eigenvector = np.linalg.eig(A)
+print('矩阵：\n', A)
+print('特征值:\n', eigenvalue)
+print('特征向量:\n', eigenvector)
+
+print('\n判断是否正交：\n', np.dot(eigenvector.transpose().conj(), eigenvector))
+print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose().conj()))
+
+print('特征向量矩阵的列向量模方和：')
+for i in range(3):
+ print(np.abs(eigenvector[0, i])**2+np.abs(eigenvector[1, i])**2+np.abs(eigenvector[2, i])**2)
+print('特征向量矩阵的行向量模方和：')
+for i in range(3):
+ print(np.abs(eigenvector[i, 0])**2+np.abs(eigenvector[i, 1])**2+np.abs(eigenvector[i, 2])**2)
+
+print('\n对角化验证：')
+print(np.dot(np.dot(eigenvector.transpose().conj(), A), eigenvector))
print(np.dot(np.dot(eigenvector, A), eigenvector.transpose().conj()))
\ No newline at end of file
diff --git a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_numpy.linalg.eig.py b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_numpy.linalg.eig.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_numpy.linalg.eig.py
rename to academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_numpy.linalg.eig.py
index 62e5d19..0a113bf
--- a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_numpy.linalg.eig.py
+++ b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_numpy.linalg.eig.py
@@ -1,18 +1,18 @@
-import numpy as np
-
-A = np.array([[3, 2, -1], [-2, -2, 2], [3, 6, -1]])
-eigenvalue, eigenvector = np.linalg.eig(A)
-print('矩阵：\n', A)
-print('特征值:\n', eigenvalue)
-print('特征向量:\n', eigenvector)
-print('特征值为-4对应的特征向量理论值:\n', np.array([1/3, -2/3, 1])/np.sqrt((1/3)**2+(-2/3)**2+1**2))
-
-print('\n判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
-print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
-
-print('特征向量矩阵的列向量模方和：')
-for i in range(3):
- print(eigenvector[0, i]**2+eigenvector[1, i]**2+eigenvector[2, i]**2)
-print('特征向量矩阵的行向量模方和：')
-for i in range(3):
+import numpy as np
+
+A = np.array([[3, 2, -1], [-2, -2, 2], [3, 6, -1]])
+eigenvalue, eigenvector = np.linalg.eig(A)
+print('矩阵：\n', A)
+print('特征值:\n', eigenvalue)
+print('特征向量:\n', eigenvector)
+print('特征值为-4对应的特征向量理论值:\n', np.array([1/3, -2/3, 1])/np.sqrt((1/3)**2+(-2/3)**2+1**2))
+
+print('\n判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
+print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
+
+print('特征向量矩阵的列向量模方和：')
+for i in range(3):
+ print(eigenvector[0, i]**2+eigenvector[1, i]**2+eigenvector[2, i]**2)
+print('特征向量矩阵的行向量模方和：')
+for i in range(3):
print(eigenvector[i, 0]**2+eigenvector[i, 1]**2+eigenvector[i, 2]**2)
\ No newline at end of file
diff --git a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix.py b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix.py
rename to academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix.py
index 99f34e5..b98f737
--- a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix.py
+++ b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix.py
@@ -1,21 +1,21 @@
-import numpy as np
-
-A = np.array([[3, 2, -1], [2, -2, 6], [-1, 6, 1]])
-eigenvalue, eigenvector = np.linalg.eig(A)
-print('矩阵：\n', A)
-print('特征值:\n', eigenvalue)
-print('特征向量:\n', eigenvector)
-
-print('\n判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
-print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
-
-print('特征向量矩阵的列向量模方和：')
-for i in range(3):
- print(eigenvector[0, i]**2+eigenvector[1, i]**2+eigenvector[2, i]**2)
-print('特征向量矩阵的行向量模方和：')
-for i in range(3):
- print(eigenvector[i, 0]**2+eigenvector[i, 1]**2+eigenvector[i, 2]**2)
-
-print('\n对角化验证：')
-print(np.dot(np.dot(eigenvector.transpose(), A), eigenvector))
+import numpy as np
+
+A = np.array([[3, 2, -1], [2, -2, 6], [-1, 6, 1]])
+eigenvalue, eigenvector = np.linalg.eig(A)
+print('矩阵：\n', A)
+print('特征值:\n', eigenvalue)
+print('特征向量:\n', eigenvector)
+
+print('\n判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
+print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
+
+print('特征向量矩阵的列向量模方和：')
+for i in range(3):
+ print(eigenvector[0, i]**2+eigenvector[1, i]**2+eigenvector[2, i]**2)
+print('特征向量矩阵的行向量模方和：')
+for i in range(3):
+ print(eigenvector[i, 0]**2+eigenvector[i, 1]**2+eigenvector[i, 2]**2)
+
+print('\n对角化验证：')
+print(np.dot(np.dot(eigenvector.transpose(), A), eigenvector))
print(np.dot(np.dot(eigenvector, A), eigenvector.transpose()))
\ No newline at end of file
diff --git a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix2.py b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix2.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix2.py
rename to academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix2.py
index c7d9d97..dd07a26
--- a/academic_codes/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix2.py
+++ b/academic_codes/others/2021.03.17_verify_the_orientation_of_eigenvector_in_matrix/example_real_symmetric_matrix2.py
@@ -1,32 +1,32 @@
-import numpy as np
-
-A = np.array([[0, 1, 1, -1], [1, 0, -1, 1], [1, -1, 0, 1], [-1, 1, 1, 0]])
-eigenvalue, eigenvector = np.linalg.eig(A)
-print('矩阵：\n', A)
-print('特征值:\n', eigenvalue)
-print('特征向量:\n', eigenvector)
-
-print('\n判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
-print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
-
-print('特征向量矩阵的列向量模方和：')
-for i in range(4):
- print(eigenvector[0, i]**2+eigenvector[1, i]**2+eigenvector[2, i]**2+eigenvector[3, i]**2)
-print('特征向量矩阵的行向量模方和：')
-for i in range(4):
- print(eigenvector[i, 0]**2+eigenvector[i, 1]**2+eigenvector[i, 2]**2+eigenvector[i, 3]**2)
-
-print('\n对角化验证：')
-print(np.dot(np.dot(eigenvector.transpose(), A), eigenvector))
-print(np.dot(np.dot(eigenvector, A), eigenvector.transpose()))
-
-print('\n特征向量理论值:')
-T = np.array([[1/np.sqrt(2), 1/np.sqrt(6), -1/np.sqrt(12), 1/2], [1/np.sqrt(2), -1/np.sqrt(6), 1/np.sqrt(12), -1/2], [0, 2/np.sqrt(6), 1/np.sqrt(12), -1/2], [0, 0, 3/np.sqrt(12), 1/2]])
-print(T)
-
-print('\n判断是否正交：\n', np.dot(T.transpose(), T))
-print('判断是否正交：\n', np.dot(T, T.transpose()))
-
-print('\n对角化验证：')
-print(np.dot(np.dot(T.transpose(), A), T))
+import numpy as np
+
+A = np.array([[0, 1, 1, -1], [1, 0, -1, 1], [1, -1, 0, 1], [-1, 1, 1, 0]])
+eigenvalue, eigenvector = np.linalg.eig(A)
+print('矩阵：\n', A)
+print('特征值:\n', eigenvalue)
+print('特征向量:\n', eigenvector)
+
+print('\n判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
+print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
+
+print('特征向量矩阵的列向量模方和：')
+for i in range(4):
+ print(eigenvector[0, i]**2+eigenvector[1, i]**2+eigenvector[2, i]**2+eigenvector[3, i]**2)
+print('特征向量矩阵的行向量模方和：')
+for i in range(4):
+ print(eigenvector[i, 0]**2+eigenvector[i, 1]**2+eigenvector[i, 2]**2+eigenvector[i, 3]**2)
+
+print('\n对角化验证：')
+print(np.dot(np.dot(eigenvector.transpose(), A), eigenvector))
+print(np.dot(np.dot(eigenvector, A), eigenvector.transpose()))
+
+print('\n特征向量理论值:')
+T = np.array([[1/np.sqrt(2), 1/np.sqrt(6), -1/np.sqrt(12), 1/2], [1/np.sqrt(2), -1/np.sqrt(6), 1/np.sqrt(12), -1/2], [0, 2/np.sqrt(6), 1/np.sqrt(12), -1/2], [0, 0, 3/np.sqrt(12), 1/2]])
+print(T)
+
+print('\n判断是否正交：\n', np.dot(T.transpose(), T))
+print('判断是否正交：\n', np.dot(T, T.transpose()))
+
+print('\n对角化验证：')
+print(np.dot(np.dot(T.transpose(), A), T))
print(np.dot(np.dot(T, A), T.transpose()))
\ No newline at end of file
diff --git a/academic_codes/2021.03.19_Schmidt_orthogonalization/Schmidt_orthogonalization.py b/academic_codes/others/2021.03.19_Schmidt_orthogonalization/Schmidt_orthogonalization.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.03.19_Schmidt_orthogonalization/Schmidt_orthogonalization.py
rename to academic_codes/others/2021.03.19_Schmidt_orthogonalization/Schmidt_orthogonalization.py
index a19fde6..8e916a4
--- a/academic_codes/2021.03.19_Schmidt_orthogonalization/Schmidt_orthogonalization.py
+++ b/academic_codes/others/2021.03.19_Schmidt_orthogonalization/Schmidt_orthogonalization.py
@@ -1,44 +1,44 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10890
-"""
-
-import numpy as np
-
-
-def main():
- A = np.array([[0, 1, 1, -1], [1, 0, -1, 1], [1, -1, 0, 1], [-1, 1, 1, 0]])
- eigenvalue, eigenvector = np.linalg.eig(A)
- print('矩阵：\n', A)
- print('特征值:\n', eigenvalue)
- print('特征向量:\n', eigenvector)
-
- print('\n判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
- print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
-
- print('对角化验证：')
- print(np.dot(np.dot(eigenvector.transpose(), A), eigenvector))
-
- # 施密斯正交化
- eigenvector = Schmidt_orthogonalization(eigenvector)
-
- print('\n施密斯正交化后，特征向量：\n', eigenvector)
-
- print('施密斯正交化后，判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
- print('施密斯正交化后，判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
-
- print('施密斯正交化后，对角化验证：')
- print(np.dot(np.dot(eigenvector.transpose(), A), eigenvector))
-
-
-def Schmidt_orthogonalization(eigenvector):
- num = eigenvector.shape[1]
- for i in range(num):
- for i0 in range(i):
- eigenvector[:, i] = eigenvector[:, i] - eigenvector[:, i0]*np.dot(eigenvector[:, i].transpose().conj(), eigenvector[:, i0])/(np.dot(eigenvector[:, i0].transpose().conj(),eigenvector[:, i0]))
- eigenvector[:, i] = eigenvector[:, i]/np.linalg.norm(eigenvector[:, i])
- return eigenvector
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10890
+"""
+
+import numpy as np
+
+
+def main():
+ A = np.array([[0, 1, 1, -1], [1, 0, -1, 1], [1, -1, 0, 1], [-1, 1, 1, 0]])
+ eigenvalue, eigenvector = np.linalg.eig(A)
+ print('矩阵：\n', A)
+ print('特征值:\n', eigenvalue)
+ print('特征向量:\n', eigenvector)
+
+ print('\n判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
+ print('判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
+
+ print('对角化验证：')
+ print(np.dot(np.dot(eigenvector.transpose(), A), eigenvector))
+
+ # 施密斯正交化
+ eigenvector = Schmidt_orthogonalization(eigenvector)
+
+ print('\n施密斯正交化后，特征向量：\n', eigenvector)
+
+ print('施密斯正交化后，判断是否正交：\n', np.dot(eigenvector.transpose(), eigenvector))
+ print('施密斯正交化后，判断是否正交：\n', np.dot(eigenvector, eigenvector.transpose()))
+
+ print('施密斯正交化后，对角化验证：')
+ print(np.dot(np.dot(eigenvector.transpose(), A), eigenvector))
+
+
+def Schmidt_orthogonalization(eigenvector):
+ num = eigenvector.shape[1]
+ for i in range(num):
+ for i0 in range(i):
+ eigenvector[:, i] = eigenvector[:, i] - eigenvector[:, i0]*np.dot(eigenvector[:, i].transpose().conj(), eigenvector[:, i0])/(np.dot(eigenvector[:, i0].transpose().conj(),eigenvector[:, i0]))
+ eigenvector[:, i] = eigenvector[:, i]/np.linalg.norm(eigenvector[:, i])
+ return eigenvector
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors.py b/academic_codes/others/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors.py
similarity index 100%
rename from academic_codes/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors.py
rename to academic_codes/others/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors.py
diff --git a/academic_codes/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors_with_guan.py b/academic_codes/others/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors_with_guan.py
similarity index 100%
rename from academic_codes/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors_with_guan.py
rename to academic_codes/others/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors_with_guan.py
diff --git a/academic_codes/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors_with_sympy.py b/academic_codes/others/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors_with_sympy.py
similarity index 100%
rename from academic_codes/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors_with_sympy.py
rename to academic_codes/others/2021.07.19_calculate_reciprocal_lattice_vectors/calculate_reciprocal_lattice_vectors_with_sympy.py
diff --git a/academic_codes/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_using_Green_functions.py b/academic_codes/quantum_transport/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_using_Green_functions.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_using_Green_functions.py
rename to academic_codes/quantum_transport/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_using_Green_functions.py
index 94aad57..12a8040
--- a/academic_codes/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_using_Green_functions.py
+++ b/academic_codes/quantum_transport/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_using_Green_functions.py
@@ -1,101 +1,101 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/948
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-import copy
-import time
-
-
-def matrix_00(width=10): # 不赋值时默认为10
- h00 = np.zeros((width, width))
- for width0 in range(width-1):
- h00[width0, width0+1] = 1
- h00[width0+1, width0] = 1
- return h00
-
-
-def matrix_01(width=10): # 不赋值时默认为10
- h01 = np.identity(width)
- return h01
-
-
-def main():
- start_time = time.time()
- h00 = matrix_00(width=5)
- h01 = matrix_01(width=5)
- fermi_energy_array = np.arange(-4, 4, .01)
- plot_conductance_energy(fermi_energy_array, h00, h01)
- end_time = time.time()
- print('运行时间=', end_time-start_time)
-
-
-def plot_conductance_energy(fermi_energy_array, h00, h01): # 画电导与费米能关系图
- dim = fermi_energy_array.shape[0]
- cond = np.zeros(dim)
- i0 = 0
- for fermi_energy0 in fermi_energy_array:
- cond0 = np.real(conductance(fermi_energy0 + 1e-12j, h00, h01))
- cond[i0] = cond0
- i0 += 1
- plt.plot(fermi_energy_array, cond, '-k')
- plt.show()
-
-
-def transfer_matrix(fermi_energy, h00, h01, dim): # 转移矩阵T。dim是传递矩阵h00和h01的维度
- transfer = np.zeros((2*dim, 2*dim), dtype=complex)
- transfer[0:dim, 0:dim] = np.dot(np.linalg.inv(h01), fermi_energy*np.identity(dim)-h00) # np.dot()等效于np.matmul()
- 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 # a:b代表 a <= x < b，左闭右开
- return transfer # 返回转移矩阵
-
-
-def green_function_lead(fermi_energy, h00, h01, dim): # 电极的表面格林函数
- transfer = transfer_matrix(fermi_energy, h00, h01, dim)
- eigenvalue, eigenvector = np.linalg.eig(transfer)
- ind = np.argsort(np.abs(eigenvalue))
- temp = np.zeros((2*dim, 2*dim), dtype=complex)
- i0 = 0
- for ind0 in ind:
- temp[:, i0] = eigenvector[:, ind0]
- i0 += 1
- s1 = temp[dim:2*dim, 0:dim]
- s2 = temp[0:dim, 0:dim]
- s3 = temp[dim:2*dim, dim:2*dim]
- 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)))
- return right_lead_surface, left_lead_surface # 返回右电极的表面格林函数和左电极的表面格林函数
-
-
-def self_energy_lead(fermi_energy, h00, h01, dim): # 电极的自能
- right_lead_surface, left_lead_surface = green_function_lead(fermi_energy, h00, h01, dim)
- right_self_energy = np.dot(np.dot(h01, right_lead_surface), h01.transpose().conj())
- left_self_energy = np.dot(np.dot(h01.transpose().conj(), left_lead_surface), h01)
- return right_self_energy, left_self_energy # 返回右边电极自能和左边电极自能
-
-
-def conductance(fermi_energy, h00, h01, nx=300): # 计算电导
- dim = h00.shape[0]
- right_self_energy, left_self_energy = self_energy_lead(fermi_energy, h00, h01, dim)
- for ix in range(nx):
- if ix == 0:
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-left_self_energy)
- green_0n_n = copy.deepcopy(green_nn_n) # 如果直接用等于，两个变量会指向相同的id，改变一个值，另外一个值可能会发生改变，容易出错，所以要用上这个COPY
- elif ix != nx-1:
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
- green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
- else:
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01)-right_self_energy)
- green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
- gamma_right = (right_self_energy - right_self_energy.transpose().conj())*(0+1j)
- gamma_left = (left_self_energy - left_self_energy.transpose().conj())*(0+1j)
- transmission = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
- return transmission # 返回电导值
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/948
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import copy
+import time
+
+
+def matrix_00(width=10): # 不赋值时默认为10
+ h00 = np.zeros((width, width))
+ for width0 in range(width-1):
+ h00[width0, width0+1] = 1
+ h00[width0+1, width0] = 1
+ return h00
+
+
+def matrix_01(width=10): # 不赋值时默认为10
+ h01 = np.identity(width)
+ return h01
+
+
+def main():
+ start_time = time.time()
+ h00 = matrix_00(width=5)
+ h01 = matrix_01(width=5)
+ fermi_energy_array = np.arange(-4, 4, .01)
+ plot_conductance_energy(fermi_energy_array, h00, h01)
+ end_time = time.time()
+ print('运行时间=', end_time-start_time)
+
+
+def plot_conductance_energy(fermi_energy_array, h00, h01): # 画电导与费米能关系图
+ dim = fermi_energy_array.shape[0]
+ cond = np.zeros(dim)
+ i0 = 0
+ for fermi_energy0 in fermi_energy_array:
+ cond0 = np.real(conductance(fermi_energy0 + 1e-12j, h00, h01))
+ cond[i0] = cond0
+ i0 += 1
+ plt.plot(fermi_energy_array, cond, '-k')
+ plt.show()
+
+
+def transfer_matrix(fermi_energy, h00, h01, dim): # 转移矩阵T。dim是传递矩阵h00和h01的维度
+ transfer = np.zeros((2*dim, 2*dim), dtype=complex)
+ transfer[0:dim, 0:dim] = np.dot(np.linalg.inv(h01), fermi_energy*np.identity(dim)-h00) # np.dot()等效于np.matmul()
+ 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 # a:b代表 a <= x < b，左闭右开
+ return transfer # 返回转移矩阵
+
+
+def green_function_lead(fermi_energy, h00, h01, dim): # 电极的表面格林函数
+ transfer = transfer_matrix(fermi_energy, h00, h01, dim)
+ eigenvalue, eigenvector = np.linalg.eig(transfer)
+ ind = np.argsort(np.abs(eigenvalue))
+ temp = np.zeros((2*dim, 2*dim), dtype=complex)
+ i0 = 0
+ for ind0 in ind:
+ temp[:, i0] = eigenvector[:, ind0]
+ i0 += 1
+ s1 = temp[dim:2*dim, 0:dim]
+ s2 = temp[0:dim, 0:dim]
+ s3 = temp[dim:2*dim, dim:2*dim]
+ 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)))
+ return right_lead_surface, left_lead_surface # 返回右电极的表面格林函数和左电极的表面格林函数
+
+
+def self_energy_lead(fermi_energy, h00, h01, dim): # 电极的自能
+ right_lead_surface, left_lead_surface = green_function_lead(fermi_energy, h00, h01, dim)
+ right_self_energy = np.dot(np.dot(h01, right_lead_surface), h01.transpose().conj())
+ left_self_energy = np.dot(np.dot(h01.transpose().conj(), left_lead_surface), h01)
+ return right_self_energy, left_self_energy # 返回右边电极自能和左边电极自能
+
+
+def conductance(fermi_energy, h00, h01, nx=300): # 计算电导
+ dim = h00.shape[0]
+ right_self_energy, left_self_energy = self_energy_lead(fermi_energy, h00, h01, dim)
+ for ix in range(nx):
+ if ix == 0:
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-left_self_energy)
+ green_0n_n = copy.deepcopy(green_nn_n) # 如果直接用等于，两个变量会指向相同的id，改变一个值，另外一个值可能会发生改变，容易出错，所以要用上这个COPY
+ elif ix != nx-1:
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
+ green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
+ else:
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01)-right_self_energy)
+ green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
+ gamma_right = (right_self_energy - right_self_energy.transpose().conj())*(0+1j)
+ gamma_left = (left_self_energy - left_self_energy.transpose().conj())*(0+1j)
+ transmission = np.trace(np.dot(np.dot(np.dot(gamma_left, green_0n_n), gamma_right), green_0n_n.transpose().conj()))
+ return transmission # 返回电导值
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_with_guan.py.py b/academic_codes/quantum_transport/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_with_guan.py.py
similarity index 100%
rename from academic_codes/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_with_guan.py.py
rename to academic_codes/quantum_transport/2019.11.01_conductance_calculation_using_Green_functions/conductance_calculation_with_guan.py.py
diff --git a/academic_codes/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/Hamiltonian_of_square_lattice_in_kwant.py b/academic_codes/quantum_transport/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/Hamiltonian_of_square_lattice_in_kwant.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/Hamiltonian_of_square_lattice_in_kwant.py
rename to academic_codes/quantum_transport/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/Hamiltonian_of_square_lattice_in_kwant.py
index 9331db0..b9e35f4
--- a/academic_codes/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/Hamiltonian_of_square_lattice_in_kwant.py
+++ b/academic_codes/quantum_transport/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/Hamiltonian_of_square_lattice_in_kwant.py
@@ -1,56 +1,56 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/2033
-"""
-
-import kwant
-import matplotlib.pyplot as plt
-
-
-def main():
- L = 3
- W = 2
-
- lat = kwant.lattice.square(a=1, norbs=1) # 定义lattice
- print('\nlat:\n', lat)
-
- syst = kwant.Builder() # 定义中心区的Builder
- print('\nsyst:\n', syst)
-
- syst_finalized = syst.finalized()
- hamiltonian = syst_finalized.hamiltonian_submatrix() # 查看哈密顿量
- print('\nhamiltonian_1（定义）:\n', hamiltonian)
-
- syst[(lat(x, y) for x in range(L) for y in range(W))] = 0 # 晶格初始化为0
-
- kwant.plot(syst) # 画出syst的示意图
-
- syst_finalized = syst.finalized()
- hamiltonian = syst_finalized.hamiltonian_submatrix() # 查看哈密顿量
- print('\nhamiltonian_2（初始化）:\n', hamiltonian)
-
- syst[lat.neighbors()] = 1 # 添加最近邻跃迁
-
- kwant.plot(syst) # 画出syst的示意图
-
- syst_finalized = syst.finalized()
- hamiltonian = syst_finalized.hamiltonian_submatrix() # 查看哈密顿量
- print('\nhamiltonian_3（最近邻赋值）:\n', hamiltonian)
-
- lead = kwant.Builder(kwant.TranslationalSymmetry((-1, 0))) # 定义电极的Builder
- lead[(lat(0, j) for j in range(W))] = 0 # 电极晶格初始化
- lead[lat.neighbors()] = 1 # 添加最近邻跃迁
- syst.attach_lead(lead) # 中心区加上左电极
- syst.attach_lead(lead.reversed()) # 用reversed()方法得到右电极
-
- # 画出syst的示意图
- kwant.plot(syst)
-
- # 查看哈密顿量（加了电极后，并不影响syst哈密顿量）
- syst_finalized = syst.finalized()
- hamiltonian = syst_finalized.hamiltonian_submatrix()
- print('\nhamiltonian_4（加了电极后）:\n', hamiltonian)
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/2033
+"""
+
+import kwant
+import matplotlib.pyplot as plt
+
+
+def main():
+ L = 3
+ W = 2
+
+ lat = kwant.lattice.square(a=1, norbs=1) # 定义lattice
+ print('\nlat:\n', lat)
+
+ syst = kwant.Builder() # 定义中心区的Builder
+ print('\nsyst:\n', syst)
+
+ syst_finalized = syst.finalized()
+ hamiltonian = syst_finalized.hamiltonian_submatrix() # 查看哈密顿量
+ print('\nhamiltonian_1（定义）:\n', hamiltonian)
+
+ syst[(lat(x, y) for x in range(L) for y in range(W))] = 0 # 晶格初始化为0
+
+ kwant.plot(syst) # 画出syst的示意图
+
+ syst_finalized = syst.finalized()
+ hamiltonian = syst_finalized.hamiltonian_submatrix() # 查看哈密顿量
+ print('\nhamiltonian_2（初始化）:\n', hamiltonian)
+
+ syst[lat.neighbors()] = 1 # 添加最近邻跃迁
+
+ kwant.plot(syst) # 画出syst的示意图
+
+ syst_finalized = syst.finalized()
+ hamiltonian = syst_finalized.hamiltonian_submatrix() # 查看哈密顿量
+ print('\nhamiltonian_3（最近邻赋值）:\n', hamiltonian)
+
+ lead = kwant.Builder(kwant.TranslationalSymmetry((-1, 0))) # 定义电极的Builder
+ lead[(lat(0, j) for j in range(W))] = 0 # 电极晶格初始化
+ lead[lat.neighbors()] = 1 # 添加最近邻跃迁
+ syst.attach_lead(lead) # 中心区加上左电极
+ syst.attach_lead(lead.reversed()) # 用reversed()方法得到右电极
+
+ # 画出syst的示意图
+ kwant.plot(syst)
+
+ # 查看哈密顿量（加了电极后，并不影响syst哈密顿量）
+ syst_finalized = syst.finalized()
+ hamiltonian = syst_finalized.hamiltonian_submatrix()
+ print('\nhamiltonian_4（加了电极后）:\n', hamiltonian)
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/kwant_example.py b/academic_codes/quantum_transport/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/kwant_example.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/kwant_example.py
rename to academic_codes/quantum_transport/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/kwant_example.py
index 072cf5f..db66d0f
--- a/academic_codes/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/kwant_example.py
+++ b/academic_codes/quantum_transport/2019.11.19_kwant_a_package_of_calculations_in_quantum_transport/kwant_example.py
@@ -1,94 +1,94 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/2033
-"""
-
-import kwant
-import numpy as np
-from matplotlib import pyplot
-
-
-def make_system():
- a = 1 # 晶格常数
- lat = kwant.lattice.square(a) # 创建晶格，方格子
-
- syst = kwant.Builder() # 建立中心体系
- t = 1.0 # hopping值
- W = 5 # 中心体系宽度
- L = 40 # 中心体系长度
-
- # 给中心体系赋值
- for i in range(L):
- for j in range(W):
- syst[lat(i, j)] = 0
- if j > 0:
- syst[lat(i, j), lat(i, j-1)] = -t # hopping in y-direction
- if i > 0:
- syst[lat(i, j), lat(i-1, j)] = -t # hopping in x-direction
-
- sym_left_lead = kwant.TranslationalSymmetry((-a, 0)) # 电极的平移对称性，(-a, 0)代表远离中心区的方向，向左
- left_lead = kwant.Builder(sym_left_lead) # 建立左电极体系
- # 给电极体系赋值
- for j in range(W):
- left_lead[lat(0, j)] = 0
- if j > 0:
- left_lead[lat(0, j), lat(0, j - 1)] = -t
- left_lead[lat(1, j), lat(0, j)] = -t # 这里写一个即可，因为平移对称性已经声明了
- syst.attach_lead(left_lead) # 把左电极接在中心区
-
- sym_right_lead = kwant.TranslationalSymmetry((a, 0))
- right_lead = kwant.Builder(sym_right_lead)
- for j in range(W):
- right_lead[lat(0, j)] = 0
- if j > 0:
- right_lead[lat(0, j), lat(0, j - 1)] = -t
- right_lead[lat(1, j), lat(0, j)] = -t
- syst.attach_lead(right_lead) # 把右电极接在中心区
-
- kwant.plot(syst) # 把电极-中心区-电极图画出来，通过图像可以看出有没有写错
- syst = syst.finalized() # 结束体系的制作。这个语句不可以省。这个语句是把Builder对象转换成可计算的对象。
- return syst
-
-
-def make_system_with_less_code():
- a = 1 # 晶格常数
- lat = kwant.lattice.square(a) # 创建晶格，方格子
-
- syst = kwant.Builder() # 建立中心体系
- t = 1.0 # hopping值
- W = 5 # 中心体系宽度
- L = 40 # 中心体系长度
-
- # 中心区
- syst[(lat(x, y) for x in range(L) for y in range(W))] = 0
- syst[lat.neighbors()] = -t # 用neighbors()方法
-
- # 电极
- lead = kwant.Builder(kwant.TranslationalSymmetry((-a, 0)))
- lead[(lat(0, j) for j in range(W))] = 0
- lead[lat.neighbors()] = -t # 用neighbors()方法
- syst.attach_lead(lead) # 左电极
- syst.attach_lead(lead.reversed()) # 用reversed()方法得到右电极
-
- kwant.plot(syst) # 把电极-中心区-电极图画出来，通过图像可以看出有没有写错
- syst = syst.finalized() # 结束体系的制作。这个语句不可以省。这个语句是把Builder对象转换成可计算的对象。
- return syst
-
-
-def main():
- syst = make_system()
- # syst = make_system_with_less_code() # 和上面的一样，只是用更少的代码写
- energies = np.linspace(-4, 4, 200)
- data = []
- for energy in energies:
- smatrix = kwant.smatrix(syst, energy) # compute the transmission probability from lead 0 to lead 1
- data.append(smatrix.transmission(1, 0))
- pyplot.plot(energies, data)
- pyplot.xlabel("energy [t]")
- pyplot.ylabel("conductance [e^2/h]")
- pyplot.show()
- # pyplot.savefig('conductance' + '.eps')
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/2033
+"""
+
+import kwant
+import numpy as np
+from matplotlib import pyplot
+
+
+def make_system():
+ a = 1 # 晶格常数
+ lat = kwant.lattice.square(a) # 创建晶格，方格子
+
+ syst = kwant.Builder() # 建立中心体系
+ t = 1.0 # hopping值
+ W = 5 # 中心体系宽度
+ L = 40 # 中心体系长度
+
+ # 给中心体系赋值
+ for i in range(L):
+ for j in range(W):
+ syst[lat(i, j)] = 0
+ if j > 0:
+ syst[lat(i, j), lat(i, j-1)] = -t # hopping in y-direction
+ if i > 0:
+ syst[lat(i, j), lat(i-1, j)] = -t # hopping in x-direction
+
+ sym_left_lead = kwant.TranslationalSymmetry((-a, 0)) # 电极的平移对称性，(-a, 0)代表远离中心区的方向，向左
+ left_lead = kwant.Builder(sym_left_lead) # 建立左电极体系
+ # 给电极体系赋值
+ for j in range(W):
+ left_lead[lat(0, j)] = 0
+ if j > 0:
+ left_lead[lat(0, j), lat(0, j - 1)] = -t
+ left_lead[lat(1, j), lat(0, j)] = -t # 这里写一个即可，因为平移对称性已经声明了
+ syst.attach_lead(left_lead) # 把左电极接在中心区
+
+ sym_right_lead = kwant.TranslationalSymmetry((a, 0))
+ right_lead = kwant.Builder(sym_right_lead)
+ for j in range(W):
+ right_lead[lat(0, j)] = 0
+ if j > 0:
+ right_lead[lat(0, j), lat(0, j - 1)] = -t
+ right_lead[lat(1, j), lat(0, j)] = -t
+ syst.attach_lead(right_lead) # 把右电极接在中心区
+
+ kwant.plot(syst) # 把电极-中心区-电极图画出来，通过图像可以看出有没有写错
+ syst = syst.finalized() # 结束体系的制作。这个语句不可以省。这个语句是把Builder对象转换成可计算的对象。
+ return syst
+
+
+def make_system_with_less_code():
+ a = 1 # 晶格常数
+ lat = kwant.lattice.square(a) # 创建晶格，方格子
+
+ syst = kwant.Builder() # 建立中心体系
+ t = 1.0 # hopping值
+ W = 5 # 中心体系宽度
+ L = 40 # 中心体系长度
+
+ # 中心区
+ syst[(lat(x, y) for x in range(L) for y in range(W))] = 0
+ syst[lat.neighbors()] = -t # 用neighbors()方法
+
+ # 电极
+ lead = kwant.Builder(kwant.TranslationalSymmetry((-a, 0)))
+ lead[(lat(0, j) for j in range(W))] = 0
+ lead[lat.neighbors()] = -t # 用neighbors()方法
+ syst.attach_lead(lead) # 左电极
+ syst.attach_lead(lead.reversed()) # 用reversed()方法得到右电极
+
+ kwant.plot(syst) # 把电极-中心区-电极图画出来，通过图像可以看出有没有写错
+ syst = syst.finalized() # 结束体系的制作。这个语句不可以省。这个语句是把Builder对象转换成可计算的对象。
+ return syst
+
+
+def main():
+ syst = make_system()
+ # syst = make_system_with_less_code() # 和上面的一样，只是用更少的代码写
+ energies = np.linspace(-4, 4, 200)
+ data = []
+ for energy in energies:
+ smatrix = kwant.smatrix(syst, energy) # compute the transmission probability from lead 0 to lead 1
+ data.append(smatrix.transmission(1, 0))
+ pyplot.plot(energies, data)
+ pyplot.xlabel("energy [t]")
+ pyplot.ylabel("conductance [e^2/h]")
+ pyplot.show()
+ # pyplot.savefig('conductance' + '.eps')
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.01.03_calculation_of_local_currents/calculation_of_local_currents.py b/academic_codes/quantum_transport/2020.01.03_calculation_of_local_currents/calculation_of_local_currents.py
similarity index 100%
rename from academic_codes/2020.01.03_calculation_of_local_currents/calculation_of_local_currents.py
rename to academic_codes/quantum_transport/2020.01.03_calculation_of_local_currents/calculation_of_local_currents.py
diff --git a/academic_codes/2020.01.03_calculation_of_local_currents/calculation_of_local_currents_with_Kwant.py b/academic_codes/quantum_transport/2020.01.03_calculation_of_local_currents/calculation_of_local_currents_with_Kwant.py
similarity index 100%
rename from academic_codes/2020.01.03_calculation_of_local_currents/calculation_of_local_currents_with_Kwant.py
rename to academic_codes/quantum_transport/2020.01.03_calculation_of_local_currents/calculation_of_local_currents_with_Kwant.py
diff --git a/academic_codes/2020.01.03_calculation_of_local_currents/calculation_of_local_currents_with_guan.py b/academic_codes/quantum_transport/2020.01.03_calculation_of_local_currents/calculation_of_local_currents_with_guan.py
similarity index 100%
rename from academic_codes/2020.01.03_calculation_of_local_currents/calculation_of_local_currents_with_guan.py
rename to academic_codes/quantum_transport/2020.01.03_calculation_of_local_currents/calculation_of_local_currents_with_guan.py
diff --git a/academic_codes/2020.10.02_calculate_scattering_matrix_by_mode_matching_method/calculate_scattering_matrix_by_mode_matching_method.py b/academic_codes/quantum_transport/2020.10.02_calculate_scattering_matrix_by_mode_matching_method/calculate_scattering_matrix_by_mode_matching_method.py
old mode 100755
new mode 100644
similarity index 98%
rename from academic_codes/2020.10.02_calculate_scattering_matrix_by_mode_matching_method/calculate_scattering_matrix_by_mode_matching_method.py
rename to academic_codes/quantum_transport/2020.10.02_calculate_scattering_matrix_by_mode_matching_method/calculate_scattering_matrix_by_mode_matching_method.py
index 0002c57..4f5bafc
--- a/academic_codes/2020.10.02_calculate_scattering_matrix_by_mode_matching_method/calculate_scattering_matrix_by_mode_matching_method.py
+++ b/academic_codes/quantum_transport/2020.10.02_calculate_scattering_matrix_by_mode_matching_method/calculate_scattering_matrix_by_mode_matching_method.py
@@ -1,240 +1,240 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6352
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import copy
-import time
-# import os
-# os.chdir('D:/data')
-
-
-def main():
- start_time = time.time()
- h00 = matrix_00() # 方格子模型
- h01 = matrix_01() # 方格子模型
- fermi_energy = 0.1
- write_transmission_matrix(fermi_energy, h00, h01) # 输出无散射的散射矩阵
- end_time = time.time()
- print('运行时间=', end_time - start_time, '秒')
-
-
-def matrix_00(width=4):
- h00 = np.zeros((width, width))
- for width0 in range(width-1):
- h00[width0, width0+1] = 1
- h00[width0+1, width0] = 1
- return h00
-
-
-def matrix_01(width=4):
- h01 = np.identity(width)
- return h01
-
-
-def transfer_matrix(fermi_energy, h00, h01, dim): # 转移矩阵
- transfer = np.zeros((2*dim, 2*dim))*(0+0j)
- transfer[0:dim, 0:dim] = np.dot(np.linalg.inv(h01), fermi_energy*np.identity(dim)-h00)
- 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
- return transfer
-
-
-def complex_wave_vector(fermi_energy, h00, h01, dim): # 获取通道的复数波矢，并按照波矢的实部Re(k)排序
- transfer = transfer_matrix(fermi_energy, h00, h01, dim)
- eigenvalue, eigenvector = np.linalg.eig(transfer)
- k_channel = np.log(eigenvalue)/1j
- ind = np.argsort(np.real(k_channel))
- k_channel = np.sort(k_channel)
- temp = np.zeros((2*dim, 2*dim))*(1+0j)
- temp2 = np.zeros((2*dim))*(1+0j)
- i0 = 0
- for ind0 in ind:
- temp[:, i0] = eigenvector[:, ind0]
- temp2[i0] = eigenvalue[ind0]
- i0 += 1
- eigenvalue = copy.deepcopy(temp2)
- temp = normalization_of_eigenvector(temp[0:dim, :], dim)
- velocity = np.zeros((2*dim))*(1+0j)
- for dim0 in range(2*dim):
- velocity[dim0] = eigenvalue[dim0]*np.dot(np.dot(temp[0:dim, :].transpose().conj(), h01),temp[0:dim, :])[dim0, dim0]
- velocity = -2*np.imag(velocity)
- eigenvector = copy.deepcopy(temp)
- return k_channel, velocity, eigenvalue, eigenvector # 返回通道的对应的波矢、费米速度、本征值、本征态
-
-
-def normalization_of_eigenvector(eigenvector, dim): # 波函数归一化
- factor = np.zeros(2*dim)*(1+0j)
- for dim0 in range(dim):
- factor = factor+np.square(np.abs(eigenvector[dim0, :]))
- for dim0 in range(2*dim):
- eigenvector[:, dim0] = eigenvector[:, dim0]/np.sqrt(factor[dim0])
- return eigenvector
-
-
-def calculation_of_lambda_u_f(fermi_energy, h00, h01, dim): # 对所有通道（包括active和evanescent）进行分类，并计算F
- k_channel, velocity, eigenvalue, eigenvector = complex_wave_vector(fermi_energy, h00, h01, dim)
- ind_right_active = 0; ind_right_evanescent = 0; ind_left_active = 0; ind_left_evanescent = 0
- k_right = np.zeros(dim)*(1+0j); k_left = np.zeros(dim)*(1+0j)
- velocity_right = np.zeros(dim)*(1+0j); velocity_left = np.zeros(dim)*(1+0j)
- lambda_right = np.zeros(dim)*(1+0j); lambda_left = np.zeros(dim)*(1+0j)
- u_right = np.zeros((dim, dim))*(1+0j); u_left = np.zeros((dim, dim))*(1+0j)
- for dim0 in range(2*dim):
- if_active = if_active_channel(k_channel[dim0])
- direction = direction_of_channel(velocity[dim0], k_channel[dim0])
- if direction == 1: # 向右运动的通道
- if if_active == 1: # 可传播通道（active channel）
- k_right[ind_right_active] = k_channel[dim0]
- velocity_right[ind_right_active] = velocity[dim0]
- lambda_right[ind_right_active] = eigenvalue[dim0]
- u_right[:, ind_right_active] = eigenvector[:, dim0]
- ind_right_active += 1
- else: # 衰减通道（evanescent channel）
- k_right[dim-1-ind_right_evanescent] = k_channel[dim0]
- velocity_right[dim-1-ind_right_evanescent] = velocity[dim0]
- lambda_right[dim-1-ind_right_evanescent] = eigenvalue[dim0]
- u_right[:, dim-1-ind_right_evanescent] = eigenvector[:, dim0]
- ind_right_evanescent += 1
- else: # 向左运动的通道
- if if_active == 1: # 可传播通道（active channel）
- k_left[ind_left_active] = k_channel[dim0]
- velocity_left[ind_left_active] = velocity[dim0]
- lambda_left[ind_left_active] = eigenvalue[dim0]
- u_left[:, ind_left_active] = eigenvector[:, dim0]
- ind_left_active += 1
- else: # 衰减通道（evanescent channel）
- k_left[dim-1-ind_left_evanescent] = k_channel[dim0]
- velocity_left[dim-1-ind_left_evanescent] = velocity[dim0]
- lambda_left[dim-1-ind_left_evanescent] = eigenvalue[dim0]
- u_left[:, dim-1-ind_left_evanescent] = eigenvector[:, dim0]
- ind_left_evanescent += 1
- lambda_matrix_right = np.diag(lambda_right)
- 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))
- return k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active
- # 分别返回向右和向左的运动的波矢k、费米速度velocity、F值、U值、可向右传播的通道数
-
-
-def if_active_channel(k_channel): # 判断是可传播通道还是衰减通道
- if np.abs(np.imag(k_channel)) < 1e-7:
- if_active = 1
- else:
- if_active = 0
- return if_active
-
-
-def direction_of_channel(velocity, k_channel): # 判断通道对应的费米速度方向
- if if_active_channel(k_channel) == 1:
- direction = np.sign(velocity)
- else:
- direction = np.sign(np.imag(k_channel))
- return direction
-
-
-def calculation_of_green_function(fermi_energy, h00, h01, dim, scatter_type=0, scatter_intensity=0.2, scatter_length=20): # 计算格林函数
- k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active = calculation_of_lambda_u_f(fermi_energy, h00, h01, dim)
- right_self_energy = np.dot(h01, f_right)
- left_self_energy = np.dot(h01.transpose().conj(), np.linalg.inv(f_left))
- nx = 300
- for nx0 in range(nx):
- if nx0 == 0:
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-left_self_energy)
- green_00_n = copy.deepcopy(green_nn_n)
- green_0n_n = copy.deepcopy(green_nn_n)
- green_n0_n = copy.deepcopy(green_nn_n)
- elif nx0 != nx-1:
- if scatter_type == 0: # 无散射
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
- elif scatter_type == 1: # 势垒散射
- h00_scatter = h00 + scatter_intensity * np.identity(dim)
- if int(nx/2)-int(scatter_length/2) <= nx0 < int(nx/2)+int((scatter_length+1)/2):
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00_scatter - np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
- else:
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
- else:
- green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-right_self_energy-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
-
- green_00_n = green_00_n+np.dot(np.dot(np.dot(np.dot(green_0n_n, h01), green_nn_n), h01.transpose().conj()), green_n0_n)
- green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
- green_n0_n = np.dot(np.dot(green_nn_n, h01.transpose().conj()), green_n0_n)
- return green_00_n, green_n0_n, k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active
-
-
-def transmission_with_detailed_modes(fermi_energy, h00, h01, scatter_type=0, scatter_intensity=0.2, scatter_length=20): # 计算散射矩阵
- dim = h00.shape[0]
- green_00_n, green_n0_n, k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active = calculation_of_green_function(fermi_energy, h00, h01, dim, scatter_type, scatter_intensity, scatter_length)
- temp = np.dot(h01.transpose().conj(), np.linalg.inv(f_right)-np.linalg.inv(f_left))
- transmission_matrix = np.dot(np.dot(np.linalg.inv(u_right), np.dot(green_n0_n, temp)), u_right)
- reflection_matrix = np.dot(np.dot(np.linalg.inv(u_left), np.dot(green_00_n, temp)-np.identity(dim)), u_right)
- for dim0 in range(dim):
- for dim1 in range(dim):
- if_active = if_active_channel(k_right[dim0])*if_active_channel(k_right[dim1])
- if if_active == 1:
- transmission_matrix[dim0, dim1] = np.sqrt(np.abs(velocity_right[dim0]/velocity_right[dim1])) * transmission_matrix[dim0, dim1]
- reflection_matrix[dim0, dim1] = np.sqrt(np.abs(velocity_left[dim0] / velocity_right[dim1]))*reflection_matrix[dim0, dim1]
- else:
- transmission_matrix[dim0, dim1] = 0
- reflection_matrix[dim0, dim1] = 0
- sum_of_tran_refl_array = 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)
- for sum_of_tran_refl in sum_of_tran_refl_array:
- if sum_of_tran_refl > 1.001:
- print('错误警告：散射矩阵的计算结果不归一！ Error Alert: scattering matrix is not normalized!')
- return transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active
-
-
-def write_transmission_matrix(fermi_energy, h00, h01, scatter_type=0, scatter_intensity=0.2, scatter_length=20): # 输出
- transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active, \
- = transmission_with_detailed_modes(fermi_energy, h00, h01, scatter_type, scatter_intensity, scatter_length)
- dim = h00.shape[0]
- np.set_printoptions(suppress=True) # 取消科学计数法输出
- print('\n可传播的通道数（向右）active_channel (right) = ', ind_right_active)
- print('衰减的通道数（向右） evanescent_channel (right) = ', dim-ind_right_active, '\n')
- print('向右可传播的通道数对应的波矢 k_right:\n', np.real(k_right[0:ind_right_active]))
- print('向左可传播的通道数对应的波矢 k_left:\n', np.real(k_left[0:ind_right_active]), '\n')
- print('向右可传播的通道数对应的费米速度 velocity_right:\n', np.real(velocity_right[0:ind_right_active]))
- print('向左可传播的通道数对应的费米速度 velocity_left:\n', np.real(velocity_left[0:ind_right_active]), '\n')
- print('透射矩阵 transmission_matrix:\n', np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])))
- print('反射矩阵 reflection_matrix:\n', np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), '\n')
- print('透射矩阵列求和 total transmission of channels =\n', np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0))
- print('反射矩阵列求和 total reflection of channels =\n',np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0))
- print('透射以及反射矩阵列求和 sum of transmission and reflection of channels =\n', 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))
- print('总电导 total conductance = ', np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active]))), '\n')
-
- # 下面把以上信息写入文件中
- with open('a.txt', 'w') as f:
- f.write('Active_channel (right or left) = ' + str(ind_right_active) + '\n')
- f.write('Evanescent_channel (right or left) = ' + str(dim - ind_right_active) + '\n\n')
- f.write('Channel K Velocity\n')
- for ind0 in range(ind_right_active):
- f.write(' '+str(ind0 + 1) + ' | '+str(np.real(k_right[ind0]))+' ' + str(np.real(velocity_right[ind0]))+'\n')
- f.write('\n')
- for ind0 in range(ind_right_active):
- f.write(' -' + str(ind0 + 1) + ' | ' + str(np.real(k_left[ind0])) + ' ' + str(np.real(velocity_left[ind0])) + '\n')
- f.write('\n\nScattering_matrix:\n ')
- for ind0 in range(ind_right_active):
- f.write(str(ind0+1)+' ')
- f.write('\n')
- for ind1 in range(ind_right_active):
- f.write(' '+str(ind1+1)+' ')
- for ind2 in range(ind_right_active):
- f.write('%f' % np.square(np.abs(transmission_matrix[ind1, ind2]))+' ')
- f.write('\n')
- f.write('\n')
- for ind1 in range(ind_right_active):
- f.write(' -'+str(ind1+1)+' ')
- for ind2 in range(ind_right_active):
- f.write('%f' % np.square(np.abs(reflection_matrix[ind1, ind2]))+' ')
- f.write('\n')
- f.write('\n')
- f.write('Total transmission of channels:\n'+str(np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0))+'\n')
- f.write('Total conductance = '+str(np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active]))))+'\n')
-
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6352
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import copy
+import time
+# import os
+# os.chdir('D:/data')
+
+
+def main():
+ start_time = time.time()
+ h00 = matrix_00() # 方格子模型
+ h01 = matrix_01() # 方格子模型
+ fermi_energy = 0.1
+ write_transmission_matrix(fermi_energy, h00, h01) # 输出无散射的散射矩阵
+ end_time = time.time()
+ print('运行时间=', end_time - start_time, '秒')
+
+
+def matrix_00(width=4):
+ h00 = np.zeros((width, width))
+ for width0 in range(width-1):
+ h00[width0, width0+1] = 1
+ h00[width0+1, width0] = 1
+ return h00
+
+
+def matrix_01(width=4):
+ h01 = np.identity(width)
+ return h01
+
+
+def transfer_matrix(fermi_energy, h00, h01, dim): # 转移矩阵
+ transfer = np.zeros((2*dim, 2*dim))*(0+0j)
+ transfer[0:dim, 0:dim] = np.dot(np.linalg.inv(h01), fermi_energy*np.identity(dim)-h00)
+ 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
+ return transfer
+
+
+def complex_wave_vector(fermi_energy, h00, h01, dim): # 获取通道的复数波矢，并按照波矢的实部Re(k)排序
+ transfer = transfer_matrix(fermi_energy, h00, h01, dim)
+ eigenvalue, eigenvector = np.linalg.eig(transfer)
+ k_channel = np.log(eigenvalue)/1j
+ ind = np.argsort(np.real(k_channel))
+ k_channel = np.sort(k_channel)
+ temp = np.zeros((2*dim, 2*dim))*(1+0j)
+ temp2 = np.zeros((2*dim))*(1+0j)
+ i0 = 0
+ for ind0 in ind:
+ temp[:, i0] = eigenvector[:, ind0]
+ temp2[i0] = eigenvalue[ind0]
+ i0 += 1
+ eigenvalue = copy.deepcopy(temp2)
+ temp = normalization_of_eigenvector(temp[0:dim, :], dim)
+ velocity = np.zeros((2*dim))*(1+0j)
+ for dim0 in range(2*dim):
+ velocity[dim0] = eigenvalue[dim0]*np.dot(np.dot(temp[0:dim, :].transpose().conj(), h01),temp[0:dim, :])[dim0, dim0]
+ velocity = -2*np.imag(velocity)
+ eigenvector = copy.deepcopy(temp)
+ return k_channel, velocity, eigenvalue, eigenvector # 返回通道的对应的波矢、费米速度、本征值、本征态
+
+
+def normalization_of_eigenvector(eigenvector, dim): # 波函数归一化
+ factor = np.zeros(2*dim)*(1+0j)
+ for dim0 in range(dim):
+ factor = factor+np.square(np.abs(eigenvector[dim0, :]))
+ for dim0 in range(2*dim):
+ eigenvector[:, dim0] = eigenvector[:, dim0]/np.sqrt(factor[dim0])
+ return eigenvector
+
+
+def calculation_of_lambda_u_f(fermi_energy, h00, h01, dim): # 对所有通道（包括active和evanescent）进行分类，并计算F
+ k_channel, velocity, eigenvalue, eigenvector = complex_wave_vector(fermi_energy, h00, h01, dim)
+ ind_right_active = 0; ind_right_evanescent = 0; ind_left_active = 0; ind_left_evanescent = 0
+ k_right = np.zeros(dim)*(1+0j); k_left = np.zeros(dim)*(1+0j)
+ velocity_right = np.zeros(dim)*(1+0j); velocity_left = np.zeros(dim)*(1+0j)
+ lambda_right = np.zeros(dim)*(1+0j); lambda_left = np.zeros(dim)*(1+0j)
+ u_right = np.zeros((dim, dim))*(1+0j); u_left = np.zeros((dim, dim))*(1+0j)
+ for dim0 in range(2*dim):
+ if_active = if_active_channel(k_channel[dim0])
+ direction = direction_of_channel(velocity[dim0], k_channel[dim0])
+ if direction == 1: # 向右运动的通道
+ if if_active == 1: # 可传播通道（active channel）
+ k_right[ind_right_active] = k_channel[dim0]
+ velocity_right[ind_right_active] = velocity[dim0]
+ lambda_right[ind_right_active] = eigenvalue[dim0]
+ u_right[:, ind_right_active] = eigenvector[:, dim0]
+ ind_right_active += 1
+ else: # 衰减通道（evanescent channel）
+ k_right[dim-1-ind_right_evanescent] = k_channel[dim0]
+ velocity_right[dim-1-ind_right_evanescent] = velocity[dim0]
+ lambda_right[dim-1-ind_right_evanescent] = eigenvalue[dim0]
+ u_right[:, dim-1-ind_right_evanescent] = eigenvector[:, dim0]
+ ind_right_evanescent += 1
+ else: # 向左运动的通道
+ if if_active == 1: # 可传播通道（active channel）
+ k_left[ind_left_active] = k_channel[dim0]
+ velocity_left[ind_left_active] = velocity[dim0]
+ lambda_left[ind_left_active] = eigenvalue[dim0]
+ u_left[:, ind_left_active] = eigenvector[:, dim0]
+ ind_left_active += 1
+ else: # 衰减通道（evanescent channel）
+ k_left[dim-1-ind_left_evanescent] = k_channel[dim0]
+ velocity_left[dim-1-ind_left_evanescent] = velocity[dim0]
+ lambda_left[dim-1-ind_left_evanescent] = eigenvalue[dim0]
+ u_left[:, dim-1-ind_left_evanescent] = eigenvector[:, dim0]
+ ind_left_evanescent += 1
+ lambda_matrix_right = np.diag(lambda_right)
+ 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))
+ return k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active
+ # 分别返回向右和向左的运动的波矢k、费米速度velocity、F值、U值、可向右传播的通道数
+
+
+def if_active_channel(k_channel): # 判断是可传播通道还是衰减通道
+ if np.abs(np.imag(k_channel)) < 1e-7:
+ if_active = 1
+ else:
+ if_active = 0
+ return if_active
+
+
+def direction_of_channel(velocity, k_channel): # 判断通道对应的费米速度方向
+ if if_active_channel(k_channel) == 1:
+ direction = np.sign(velocity)
+ else:
+ direction = np.sign(np.imag(k_channel))
+ return direction
+
+
+def calculation_of_green_function(fermi_energy, h00, h01, dim, scatter_type=0, scatter_intensity=0.2, scatter_length=20): # 计算格林函数
+ k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active = calculation_of_lambda_u_f(fermi_energy, h00, h01, dim)
+ right_self_energy = np.dot(h01, f_right)
+ left_self_energy = np.dot(h01.transpose().conj(), np.linalg.inv(f_left))
+ nx = 300
+ for nx0 in range(nx):
+ if nx0 == 0:
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-left_self_energy)
+ green_00_n = copy.deepcopy(green_nn_n)
+ green_0n_n = copy.deepcopy(green_nn_n)
+ green_n0_n = copy.deepcopy(green_nn_n)
+ elif nx0 != nx-1:
+ if scatter_type == 0: # 无散射
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
+ elif scatter_type == 1: # 势垒散射
+ h00_scatter = h00 + scatter_intensity * np.identity(dim)
+ if int(nx/2)-int(scatter_length/2) <= nx0 < int(nx/2)+int((scatter_length+1)/2):
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00_scatter - np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
+ else:
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
+ else:
+ green_nn_n = np.linalg.inv(fermi_energy*np.identity(dim)-h00-right_self_energy-np.dot(np.dot(h01.transpose().conj(), green_nn_n), h01))
+
+ green_00_n = green_00_n+np.dot(np.dot(np.dot(np.dot(green_0n_n, h01), green_nn_n), h01.transpose().conj()), green_n0_n)
+ green_0n_n = np.dot(np.dot(green_0n_n, h01), green_nn_n)
+ green_n0_n = np.dot(np.dot(green_nn_n, h01.transpose().conj()), green_n0_n)
+ return green_00_n, green_n0_n, k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active
+
+
+def transmission_with_detailed_modes(fermi_energy, h00, h01, scatter_type=0, scatter_intensity=0.2, scatter_length=20): # 计算散射矩阵
+ dim = h00.shape[0]
+ green_00_n, green_n0_n, k_right, k_left, velocity_right, velocity_left, f_right, f_left, u_right, u_left, ind_right_active = calculation_of_green_function(fermi_energy, h00, h01, dim, scatter_type, scatter_intensity, scatter_length)
+ temp = np.dot(h01.transpose().conj(), np.linalg.inv(f_right)-np.linalg.inv(f_left))
+ transmission_matrix = np.dot(np.dot(np.linalg.inv(u_right), np.dot(green_n0_n, temp)), u_right)
+ reflection_matrix = np.dot(np.dot(np.linalg.inv(u_left), np.dot(green_00_n, temp)-np.identity(dim)), u_right)
+ for dim0 in range(dim):
+ for dim1 in range(dim):
+ if_active = if_active_channel(k_right[dim0])*if_active_channel(k_right[dim1])
+ if if_active == 1:
+ transmission_matrix[dim0, dim1] = np.sqrt(np.abs(velocity_right[dim0]/velocity_right[dim1])) * transmission_matrix[dim0, dim1]
+ reflection_matrix[dim0, dim1] = np.sqrt(np.abs(velocity_left[dim0] / velocity_right[dim1]))*reflection_matrix[dim0, dim1]
+ else:
+ transmission_matrix[dim0, dim1] = 0
+ reflection_matrix[dim0, dim1] = 0
+ sum_of_tran_refl_array = 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)
+ for sum_of_tran_refl in sum_of_tran_refl_array:
+ if sum_of_tran_refl > 1.001:
+ print('错误警告：散射矩阵的计算结果不归一！ Error Alert: scattering matrix is not normalized!')
+ return transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active
+
+
+def write_transmission_matrix(fermi_energy, h00, h01, scatter_type=0, scatter_intensity=0.2, scatter_length=20): # 输出
+ transmission_matrix, reflection_matrix, k_right, k_left, velocity_right, velocity_left, ind_right_active, \
+ = transmission_with_detailed_modes(fermi_energy, h00, h01, scatter_type, scatter_intensity, scatter_length)
+ dim = h00.shape[0]
+ np.set_printoptions(suppress=True) # 取消科学计数法输出
+ print('\n可传播的通道数（向右）active_channel (right) = ', ind_right_active)
+ print('衰减的通道数（向右） evanescent_channel (right) = ', dim-ind_right_active, '\n')
+ print('向右可传播的通道数对应的波矢 k_right:\n', np.real(k_right[0:ind_right_active]))
+ print('向左可传播的通道数对应的波矢 k_left:\n', np.real(k_left[0:ind_right_active]), '\n')
+ print('向右可传播的通道数对应的费米速度 velocity_right:\n', np.real(velocity_right[0:ind_right_active]))
+ print('向左可传播的通道数对应的费米速度 velocity_left:\n', np.real(velocity_left[0:ind_right_active]), '\n')
+ print('透射矩阵 transmission_matrix:\n', np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])))
+ print('反射矩阵 reflection_matrix:\n', np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), '\n')
+ print('透射矩阵列求和 total transmission of channels =\n', np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0))
+ print('反射矩阵列求和 total reflection of channels =\n',np.sum(np.square(np.abs(reflection_matrix[0:ind_right_active, 0:ind_right_active])), axis=0))
+ print('透射以及反射矩阵列求和 sum of transmission and reflection of channels =\n', 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))
+ print('总电导 total conductance = ', np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active]))), '\n')
+
+ # 下面把以上信息写入文件中
+ with open('a.txt', 'w') as f:
+ f.write('Active_channel (right or left) = ' + str(ind_right_active) + '\n')
+ f.write('Evanescent_channel (right or left) = ' + str(dim - ind_right_active) + '\n\n')
+ f.write('Channel K Velocity\n')
+ for ind0 in range(ind_right_active):
+ f.write(' '+str(ind0 + 1) + ' | '+str(np.real(k_right[ind0]))+' ' + str(np.real(velocity_right[ind0]))+'\n')
+ f.write('\n')
+ for ind0 in range(ind_right_active):
+ f.write(' -' + str(ind0 + 1) + ' | ' + str(np.real(k_left[ind0])) + ' ' + str(np.real(velocity_left[ind0])) + '\n')
+ f.write('\n\nScattering_matrix:\n ')
+ for ind0 in range(ind_right_active):
+ f.write(str(ind0+1)+' ')
+ f.write('\n')
+ for ind1 in range(ind_right_active):
+ f.write(' '+str(ind1+1)+' ')
+ for ind2 in range(ind_right_active):
+ f.write('%f' % np.square(np.abs(transmission_matrix[ind1, ind2]))+' ')
+ f.write('\n')
+ f.write('\n')
+ for ind1 in range(ind_right_active):
+ f.write(' -'+str(ind1+1)+' ')
+ for ind2 in range(ind_right_active):
+ f.write('%f' % np.square(np.abs(reflection_matrix[ind1, ind2]))+' ')
+ f.write('\n')
+ f.write('\n')
+ f.write('Total transmission of channels:\n'+str(np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active])), axis=0))+'\n')
+ f.write('Total conductance = '+str(np.sum(np.square(np.abs(transmission_matrix[0:ind_right_active, 0:ind_right_active]))))+'\n')
+
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems.py b/academic_codes/quantum_transport/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems.py
rename to academic_codes/quantum_transport/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems.py
index bdd7b0d..247edca
--- a/academic_codes/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems.py
+++ b/academic_codes/quantum_transport/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems.py
@@ -1,180 +1,180 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6075
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-import copy
-import time
-
-
-def get_lead_h00(width):
- h00 = np.zeros((width, width))
- for i0 in range(width-1):
- h00[i0, i0+1] = 1
- h00[i0+1, i0] = 1
- return h00
-
-
-def get_lead_h01(width):
- h01 = np.identity(width)
- return h01
-
-
-def get_center_hamiltonian(Nx, Ny):
- h = np.zeros((Nx*Ny, Nx*Ny))
- for x0 in range(Nx-1):
- for y0 in range(Ny):
- h[x0*Ny+y0, (x0+1)*Ny+y0] = 1 # x方向的跃迁
- h[(x0+1)*Ny+y0, x0*Ny+y0] = 1
- for x0 in range(Nx):
- for y0 in range(Ny-1):
- h[x0*Ny+y0, x0*Ny+y0+1] = 1 # y方向的跃迁
- h[x0*Ny+y0+1, x0*Ny+y0] = 1
- return h
-
-
-def main():
- start_time = time.time()
- width = 5
- length = 50
- fermi_energy_array = np.arange(-4, 4, .01)
-
- # 中心区的哈密顿量
- H_center = get_center_hamiltonian(Nx=length, Ny=width)
-
- # 电极的h00和h01
- lead_h00 = get_lead_h00(width)
- lead_h01 = get_lead_h01(width)
-
- transmission_12_array = []
- transmission_13_array = []
- transmission_14_array = []
- transmission_15_array = []
- transmission_16_array = []
- transmission_1_all_array = []
-
- for fermi_energy in fermi_energy_array:
- print(fermi_energy)
- # 表面格林函数
- right_lead_surface, left_lead_surface = surface_green_function_lead(fermi_energy + 1e-9j, lead_h00, lead_h01, dim=width)
-
- # 由于镜面对称以及xy各项同性，因此这里六个电极的表面格林函数具有相同的形式
- lead_1 = copy.deepcopy(right_lead_surface) # 镜面对称使得lead_1=lead_4
- lead_2 = copy.deepcopy(right_lead_surface) # xy各向同性使得lead_2=lead_4
- lead_3 = copy.deepcopy(right_lead_surface)
- lead_4 = copy.deepcopy(right_lead_surface)
- lead_5 = copy.deepcopy(right_lead_surface)
- lead_6 = copy.deepcopy(right_lead_surface)
-
- # 几何形状如下所示：
- # lead2 lead3
- # lead1(L) lead4(R)
- # lead6 lead5
-
- # 电极到中心区的跃迁矩阵
- H_lead_1_to_center = np.zeros((width, width*length), dtype=complex)
- H_lead_2_to_center = np.zeros((width, width*length), dtype=complex)
- H_lead_3_to_center = np.zeros((width, width*length), dtype=complex)
- H_lead_4_to_center = np.zeros((width, width*length), dtype=complex)
- H_lead_5_to_center = np.zeros((width, width*length), dtype=complex)
- H_lead_6_to_center = np.zeros((width, width*length), dtype=complex)
- move = 0 # the step of leads 2,3,6,5 moving to center
- for i0 in range(width):
- H_lead_1_to_center[i0, i0] = 1
- H_lead_2_to_center[i0, width*(move+i0)+(width-1)] = 1
- H_lead_3_to_center[i0, width*(length-move-1-i0)+(width-1)] = 1
- H_lead_4_to_center[i0, width*(length-1)+i0] = 1
- H_lead_5_to_center[i0, width*(length-move-1-i0)+0] = 1
- H_lead_6_to_center[i0, width*(move+i0)+0] = 1
-
- # 自能
- self_energy_1 = np.dot(np.dot(H_lead_1_to_center.transpose().conj(), lead_1), H_lead_1_to_center)
- self_energy_2 = np.dot(np.dot(H_lead_2_to_center.transpose().conj(), lead_2), H_lead_2_to_center)
- self_energy_3 = np.dot(np.dot(H_lead_3_to_center.transpose().conj(), lead_3), H_lead_3_to_center)
- self_energy_4 = np.dot(np.dot(H_lead_4_to_center.transpose().conj(), lead_4), H_lead_4_to_center)
- self_energy_5 = np.dot(np.dot(H_lead_5_to_center.transpose().conj(), lead_5), H_lead_5_to_center)
- self_energy_6 = np.dot(np.dot(H_lead_6_to_center.transpose().conj(), lead_6), H_lead_6_to_center)
-
- # 整体格林函数
- green = np.linalg.inv(fermi_energy*np.eye(width*length)-H_center-self_energy_1-self_energy_2-self_energy_3-self_energy_4-self_energy_5-self_energy_6)
-
- # Gamma矩阵
- gamma_1 = 1j*(self_energy_1-self_energy_1.transpose().conj())
- gamma_2 = 1j*(self_energy_2-self_energy_2.transpose().conj())
- gamma_3 = 1j*(self_energy_3-self_energy_3.transpose().conj())
- gamma_4 = 1j*(self_energy_4-self_energy_4.transpose().conj())
- gamma_5 = 1j*(self_energy_5-self_energy_5.transpose().conj())
- gamma_6 = 1j*(self_energy_6-self_energy_6.transpose().conj())
-
- # Transmission
- transmission_12 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_2), green.transpose().conj()))
- transmission_13 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_3), green.transpose().conj()))
- transmission_14 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_4), green.transpose().conj()))
- transmission_15 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_5), green.transpose().conj()))
- transmission_16 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_6), green.transpose().conj()))
-
- transmission_12_array.append(np.real(transmission_12))
- transmission_13_array.append(np.real(transmission_13))
- transmission_14_array.append(np.real(transmission_14))
- transmission_15_array.append(np.real(transmission_15))
- transmission_16_array.append(np.real(transmission_16))
- transmission_1_all_array.append(np.real(transmission_12+transmission_13+transmission_14+transmission_15+transmission_16))
-
- Plot_Line(fermi_energy_array, transmission_12_array, xlabel='Fermi energy', ylabel='Transmission_12', title='', filename='a')
- Plot_Line(fermi_energy_array, transmission_13_array, xlabel='Fermi energy', ylabel='Transmission_13', title='', filename='a')
- Plot_Line(fermi_energy_array, transmission_14_array, xlabel='Fermi energy', ylabel='Transmission_14', title='', filename='a')
- Plot_Line(fermi_energy_array, transmission_15_array, xlabel='Fermi energy', ylabel='Transmission_15', title='', filename='a')
- Plot_Line(fermi_energy_array, transmission_16_array, xlabel='Fermi energy', ylabel='Transmission_16', title='', filename='a')
- Plot_Line(fermi_energy_array, transmission_1_all_array, xlabel='Fermi energy', ylabel='Transmission_1_all', title='', filename='a')
- end_time = time.time()
- print('运行时间=', end_time-start_time)
-
-
-
-def Plot_Line(x, y, xlabel='x', ylabel='y', title='', filename='a'):
- import matplotlib.pyplot as plt
- fig, ax = plt.subplots()
- plt.subplots_adjust(bottom=0.20, left=0.18)
- ax.plot(x, y, '-')
- ax.grid()
- ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
- ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
- ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
- ax.tick_params(labelsize=20)
- labels = ax.get_xticklabels() + ax.get_yticklabels()
- [label.set_fontname('Times New Roman') for label in labels]
- # plt.savefig(filename+'.jpg', dpi=300)
- plt.show()
- plt.close('all')
-
-def transfer_matrix(fermi_energy, h00, h01, dim): # 转移矩阵T
- transfer = np.zeros((2*dim, 2*dim))*(0+0j)
- transfer[0:dim, 0:dim] = np.dot(np.linalg.inv(h01), fermi_energy*np.identity(dim)-h00)
- 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
- return transfer # 返回转移矩阵
-
-
-def surface_green_function_lead(fermi_energy, h00, h01, dim): # 电极的表面格林函数
- transfer = transfer_matrix(fermi_energy, h00, h01, dim)
- eigenvalue, eigenvector = np.linalg.eig(transfer)
- ind = np.argsort(np.abs(eigenvalue))
- temp = np.zeros((2*dim, 2*dim))*(1+0j)
- i0 = 0
- for ind0 in ind:
- temp[:, i0] = eigenvector[:, ind0]
- i0 += 1
- s1 = temp[dim:2*dim, 0:dim]
- s2 = temp[0:dim, 0:dim]
- s3 = temp[dim:2*dim, dim:2*dim]
- 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)))
- return right_lead_surface, left_lead_surface # 返回右电极的表面格林函数和左电极的表面格林函数
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6075
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+import copy
+import time
+
+
+def get_lead_h00(width):
+ h00 = np.zeros((width, width))
+ for i0 in range(width-1):
+ h00[i0, i0+1] = 1
+ h00[i0+1, i0] = 1
+ return h00
+
+
+def get_lead_h01(width):
+ h01 = np.identity(width)
+ return h01
+
+
+def get_center_hamiltonian(Nx, Ny):
+ h = np.zeros((Nx*Ny, Nx*Ny))
+ for x0 in range(Nx-1):
+ for y0 in range(Ny):
+ h[x0*Ny+y0, (x0+1)*Ny+y0] = 1 # x方向的跃迁
+ h[(x0+1)*Ny+y0, x0*Ny+y0] = 1
+ for x0 in range(Nx):
+ for y0 in range(Ny-1):
+ h[x0*Ny+y0, x0*Ny+y0+1] = 1 # y方向的跃迁
+ h[x0*Ny+y0+1, x0*Ny+y0] = 1
+ return h
+
+
+def main():
+ start_time = time.time()
+ width = 5
+ length = 50
+ fermi_energy_array = np.arange(-4, 4, .01)
+
+ # 中心区的哈密顿量
+ H_center = get_center_hamiltonian(Nx=length, Ny=width)
+
+ # 电极的h00和h01
+ lead_h00 = get_lead_h00(width)
+ lead_h01 = get_lead_h01(width)
+
+ transmission_12_array = []
+ transmission_13_array = []
+ transmission_14_array = []
+ transmission_15_array = []
+ transmission_16_array = []
+ transmission_1_all_array = []
+
+ for fermi_energy in fermi_energy_array:
+ print(fermi_energy)
+ # 表面格林函数
+ right_lead_surface, left_lead_surface = surface_green_function_lead(fermi_energy + 1e-9j, lead_h00, lead_h01, dim=width)
+
+ # 由于镜面对称以及xy各项同性，因此这里六个电极的表面格林函数具有相同的形式
+ lead_1 = copy.deepcopy(right_lead_surface) # 镜面对称使得lead_1=lead_4
+ lead_2 = copy.deepcopy(right_lead_surface) # xy各向同性使得lead_2=lead_4
+ lead_3 = copy.deepcopy(right_lead_surface)
+ lead_4 = copy.deepcopy(right_lead_surface)
+ lead_5 = copy.deepcopy(right_lead_surface)
+ lead_6 = copy.deepcopy(right_lead_surface)
+
+ # 几何形状如下所示：
+ # lead2 lead3
+ # lead1(L) lead4(R)
+ # lead6 lead5
+
+ # 电极到中心区的跃迁矩阵
+ H_lead_1_to_center = np.zeros((width, width*length), dtype=complex)
+ H_lead_2_to_center = np.zeros((width, width*length), dtype=complex)
+ H_lead_3_to_center = np.zeros((width, width*length), dtype=complex)
+ H_lead_4_to_center = np.zeros((width, width*length), dtype=complex)
+ H_lead_5_to_center = np.zeros((width, width*length), dtype=complex)
+ H_lead_6_to_center = np.zeros((width, width*length), dtype=complex)
+ move = 0 # the step of leads 2,3,6,5 moving to center
+ for i0 in range(width):
+ H_lead_1_to_center[i0, i0] = 1
+ H_lead_2_to_center[i0, width*(move+i0)+(width-1)] = 1
+ H_lead_3_to_center[i0, width*(length-move-1-i0)+(width-1)] = 1
+ H_lead_4_to_center[i0, width*(length-1)+i0] = 1
+ H_lead_5_to_center[i0, width*(length-move-1-i0)+0] = 1
+ H_lead_6_to_center[i0, width*(move+i0)+0] = 1
+
+ # 自能
+ self_energy_1 = np.dot(np.dot(H_lead_1_to_center.transpose().conj(), lead_1), H_lead_1_to_center)
+ self_energy_2 = np.dot(np.dot(H_lead_2_to_center.transpose().conj(), lead_2), H_lead_2_to_center)
+ self_energy_3 = np.dot(np.dot(H_lead_3_to_center.transpose().conj(), lead_3), H_lead_3_to_center)
+ self_energy_4 = np.dot(np.dot(H_lead_4_to_center.transpose().conj(), lead_4), H_lead_4_to_center)
+ self_energy_5 = np.dot(np.dot(H_lead_5_to_center.transpose().conj(), lead_5), H_lead_5_to_center)
+ self_energy_6 = np.dot(np.dot(H_lead_6_to_center.transpose().conj(), lead_6), H_lead_6_to_center)
+
+ # 整体格林函数
+ green = np.linalg.inv(fermi_energy*np.eye(width*length)-H_center-self_energy_1-self_energy_2-self_energy_3-self_energy_4-self_energy_5-self_energy_6)
+
+ # Gamma矩阵
+ gamma_1 = 1j*(self_energy_1-self_energy_1.transpose().conj())
+ gamma_2 = 1j*(self_energy_2-self_energy_2.transpose().conj())
+ gamma_3 = 1j*(self_energy_3-self_energy_3.transpose().conj())
+ gamma_4 = 1j*(self_energy_4-self_energy_4.transpose().conj())
+ gamma_5 = 1j*(self_energy_5-self_energy_5.transpose().conj())
+ gamma_6 = 1j*(self_energy_6-self_energy_6.transpose().conj())
+
+ # Transmission
+ transmission_12 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_2), green.transpose().conj()))
+ transmission_13 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_3), green.transpose().conj()))
+ transmission_14 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_4), green.transpose().conj()))
+ transmission_15 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_5), green.transpose().conj()))
+ transmission_16 = np.trace(np.dot(np.dot(np.dot(gamma_1, green), gamma_6), green.transpose().conj()))
+
+ transmission_12_array.append(np.real(transmission_12))
+ transmission_13_array.append(np.real(transmission_13))
+ transmission_14_array.append(np.real(transmission_14))
+ transmission_15_array.append(np.real(transmission_15))
+ transmission_16_array.append(np.real(transmission_16))
+ transmission_1_all_array.append(np.real(transmission_12+transmission_13+transmission_14+transmission_15+transmission_16))
+
+ Plot_Line(fermi_energy_array, transmission_12_array, xlabel='Fermi energy', ylabel='Transmission_12', title='', filename='a')
+ Plot_Line(fermi_energy_array, transmission_13_array, xlabel='Fermi energy', ylabel='Transmission_13', title='', filename='a')
+ Plot_Line(fermi_energy_array, transmission_14_array, xlabel='Fermi energy', ylabel='Transmission_14', title='', filename='a')
+ Plot_Line(fermi_energy_array, transmission_15_array, xlabel='Fermi energy', ylabel='Transmission_15', title='', filename='a')
+ Plot_Line(fermi_energy_array, transmission_16_array, xlabel='Fermi energy', ylabel='Transmission_16', title='', filename='a')
+ Plot_Line(fermi_energy_array, transmission_1_all_array, xlabel='Fermi energy', ylabel='Transmission_1_all', title='', filename='a')
+ end_time = time.time()
+ print('运行时间=', end_time-start_time)
+
+
+
+def Plot_Line(x, y, xlabel='x', ylabel='y', title='', filename='a'):
+ import matplotlib.pyplot as plt
+ fig, ax = plt.subplots()
+ plt.subplots_adjust(bottom=0.20, left=0.18)
+ ax.plot(x, y, '-')
+ ax.grid()
+ ax.set_title(title, fontsize=20, fontfamily='Times New Roman')
+ ax.set_xlabel(xlabel, fontsize=20, fontfamily='Times New Roman')
+ ax.set_ylabel(ylabel, fontsize=20, fontfamily='Times New Roman')
+ ax.tick_params(labelsize=20)
+ labels = ax.get_xticklabels() + ax.get_yticklabels()
+ [label.set_fontname('Times New Roman') for label in labels]
+ # plt.savefig(filename+'.jpg', dpi=300)
+ plt.show()
+ plt.close('all')
+
+def transfer_matrix(fermi_energy, h00, h01, dim): # 转移矩阵T
+ transfer = np.zeros((2*dim, 2*dim))*(0+0j)
+ transfer[0:dim, 0:dim] = np.dot(np.linalg.inv(h01), fermi_energy*np.identity(dim)-h00)
+ 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
+ return transfer # 返回转移矩阵
+
+
+def surface_green_function_lead(fermi_energy, h00, h01, dim): # 电极的表面格林函数
+ transfer = transfer_matrix(fermi_energy, h00, h01, dim)
+ eigenvalue, eigenvector = np.linalg.eig(transfer)
+ ind = np.argsort(np.abs(eigenvalue))
+ temp = np.zeros((2*dim, 2*dim))*(1+0j)
+ i0 = 0
+ for ind0 in ind:
+ temp[:, i0] = eigenvector[:, ind0]
+ i0 += 1
+ s1 = temp[dim:2*dim, 0:dim]
+ s2 = temp[0:dim, 0:dim]
+ s3 = temp[dim:2*dim, dim:2*dim]
+ 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)))
+ return right_lead_surface, left_lead_surface # 返回右电极的表面格林函数和左电极的表面格林函数
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems_with_guan.py b/academic_codes/quantum_transport/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems_with_guan.py
similarity index 100%
rename from academic_codes/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems_with_guan.py
rename to academic_codes/quantum_transport/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems_with_guan.py
diff --git a/academic_codes/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems_with_kwant.py b/academic_codes/quantum_transport/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems_with_kwant.py
similarity index 100%
rename from academic_codes/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems_with_kwant.py
rename to academic_codes/quantum_transport/2021.02.08_quantum_transport_in_multi_lead_systems/quantum_transport_in_multi_lead_systems_with_kwant.py
diff --git a/academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.m b/academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.m
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.m
rename to academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.m
index 8e6a02f..48d3a89
--- a/academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.m
+++ b/academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.m
@@ -1,50 +1,50 @@
-% This code is supported by the website: https://www.guanjihuan.com
-% The newest version of this code is on the web page: https://www.guanjihuan.com/archives/3932
-
-% 陈数定义法
-clear;clc;
-n=100; % 积分密度
-delta=1e-9; % 求导的偏离量
-C=0;
-for kx=-pi:(1/n):pi
- for ky=-pi:(1/n):pi
- VV=get_vector(HH(kx,ky));
- Vkx=get_vector(HH(kx+delta,ky)); % 略偏离kx的波函数
- Vky=get_vector(HH(kx,ky+delta)); % 略偏离ky的波函数
- Vkxky=get_vector(HH(kx+delta,ky+delta)); % 略偏离kx，ky的波函数
- if sum((abs(Vkx-VV)))>0.01 % 为了波函数的连续性（这里的不连续只遇到符号问题，所以可以直接这么处理）
- Vkx=-Vkx;
- end
-
- if sum((abs(Vky-VV)))>0.01
- Vky=-Vky;
- end
-
- if sum(abs(Vkxky-VV))>0.01
- Vkxky=-Vkxky;
- end
- % 价带的波函数的berry connection，求导后内积
- Ax=VV'*(Vkx-VV)/delta; % Berry connection Ax
- Ay=VV'*(Vky-VV)/delta; % Berry connection Ay
- Ax_delta_ky=Vky'*(Vkxky-Vky)/delta; % 略偏离ky的berry connection Ax
- Ay_delta_kx=Vkx'*(Vkxky-Vkx)/delta; % 略偏离kx的berry connection Ay
- % berry curvature
- F=((Ay_delta_kx-Ay)-(Ax_delta_ky-Ax))/delta;
- % chern number
- C=C+F*(1/n)^2;
- end
-end
-C=C/(2*pi*1i)
-
-function vector_new = get_vector(H)
-[vector,eigenvalue] = eig(H);
-[eigenvalue, index]=sort(diag(eigenvalue), 'descend');
-vector_new = vector(:, index(2));
-end
-
-function H=HH(kx,ky)
-H(1,2)=2*cos(kx)-1i*2*cos(ky);
-H(2,1)=2*cos(kx)+1i*2*cos(ky);
-H(1,1)=-1+2*0.5*sin(kx)+2*0.5*sin(ky)+2*cos(kx+ky);
-H(2,2)=-(-1+2*0.5*sin(kx)+2*0.5*sin(ky)+2*cos(kx+ky));
+% This code is supported by the website: https://www.guanjihuan.com
+% The newest version of this code is on the web page: https://www.guanjihuan.com/archives/3932
+
+% 陈数定义法
+clear;clc;
+n=100; % 积分密度
+delta=1e-9; % 求导的偏离量
+C=0;
+for kx=-pi:(1/n):pi
+ for ky=-pi:(1/n):pi
+ VV=get_vector(HH(kx,ky));
+ Vkx=get_vector(HH(kx+delta,ky)); % 略偏离kx的波函数
+ Vky=get_vector(HH(kx,ky+delta)); % 略偏离ky的波函数
+ Vkxky=get_vector(HH(kx+delta,ky+delta)); % 略偏离kx，ky的波函数
+ if sum((abs(Vkx-VV)))>0.01 % 为了波函数的连续性（这里的不连续只遇到符号问题，所以可以直接这么处理）
+ Vkx=-Vkx;
+ end
+
+ if sum((abs(Vky-VV)))>0.01
+ Vky=-Vky;
+ end
+
+ if sum(abs(Vkxky-VV))>0.01
+ Vkxky=-Vkxky;
+ end
+ % 价带的波函数的berry connection，求导后内积
+ Ax=VV'*(Vkx-VV)/delta; % Berry connection Ax
+ Ay=VV'*(Vky-VV)/delta; % Berry connection Ay
+ Ax_delta_ky=Vky'*(Vkxky-Vky)/delta; % 略偏离ky的berry connection Ax
+ Ay_delta_kx=Vkx'*(Vkxky-Vkx)/delta; % 略偏离kx的berry connection Ay
+ % berry curvature
+ F=((Ay_delta_kx-Ay)-(Ax_delta_ky-Ax))/delta;
+ % chern number
+ C=C+F*(1/n)^2;
+ end
+end
+C=C/(2*pi*1i)
+
+function vector_new = get_vector(H)
+[vector,eigenvalue] = eig(H);
+[eigenvalue, index]=sort(diag(eigenvalue), 'descend');
+vector_new = vector(:, index(2));
+end
+
+function H=HH(kx,ky)
+H(1,2)=2*cos(kx)-1i*2*cos(ky);
+H(2,1)=2*cos(kx)+1i*2*cos(ky);
+H(1,1)=-1+2*0.5*sin(kx)+2*0.5*sin(ky)+2*cos(kx+ky);
+H(2,2)=-(-1+2*0.5*sin(kx)+2*0.5*sin(ky)+2*cos(kx+ky));
end
\ No newline at end of file
diff --git a/academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.py b/academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.py
rename to academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.py
index 3ca0785..73167ad
--- a/academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.py
+++ b/academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/calculation_of_Chern_number_by_definition_method.py
@@ -1,69 +1,69 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/3932
-"""
-
-import numpy as np
-from math import *
-import time
-
-
-def hamiltonian(kx, ky): # 量子反常霍尔QAH模型（该参数对应的陈数为2）
- t1 = 1.0
- t2 = 1.0
- t3 = 0.5
- m = -1.0
- matrix = np.zeros((2, 2), dtype=complex)
- matrix[0, 1] = 2*t1*cos(kx)-1j*2*t1*cos(ky)
- matrix[1, 0] = 2*t1*cos(kx)+1j*2*t1*cos(ky)
- matrix[0, 0] = m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky)
- matrix[1, 1] = -(m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky))
- return matrix
-
-
-def main():
- start_time = time.time()
- n = 100 # 积分密度
- delta = 1e-9 # 求导的偏离量
- chern_number = 0 # 陈数初始化
- for kx in np.arange(-pi, pi, 2*pi/n):
- for ky in np.arange(-pi, pi, 2*pi/n):
- H = hamiltonian(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
- # print(np.argsort(np.real(eigenvalue))[0]) # 排序索引（从小到大）
- # print(eigenvalue) # 排序前的本征值
- # print(np.sort(np.real(eigenvalue))) # 排序后的本征值（从小到大）
-
- H_delta_kx = hamiltonian(kx+delta, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
-
- H_delta_ky = hamiltonian(kx, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
-
- H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
-
- # 价带的波函数的贝里联络(berry connection) # 求导后内积
- A_x = np.dot(vector.transpose().conj(), (vector_delta_kx-vector)/delta) # 贝里联络Ax（x分量）
- A_y = np.dot(vector.transpose().conj(), (vector_delta_ky-vector)/delta) # 贝里联络Ay（y分量）
-
- A_x_delta_ky = np.dot(vector_delta_ky.transpose().conj(), (vector_delta_kx_ky-vector_delta_ky)/delta) # 略偏离ky的贝里联络Ax
- A_y_delta_kx = np.dot(vector_delta_kx.transpose().conj(), (vector_delta_kx_ky-vector_delta_kx)/delta) # 略偏离kx的贝里联络Ay
-
- # 贝里曲率(berry curvature)
- F = (A_y_delta_kx-A_y)/delta-(A_x_delta_ky-A_x)/delta
-
- # 陈数(chern number)
- chern_number = chern_number + F*(2*pi/n)**2
- chern_number = chern_number/(2*pi*1j)
- print('Chern number = ', chern_number)
- end_time = time.time()
- print('运行时间(min)=', (end_time-start_time)/60)
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/3932
+"""
+
+import numpy as np
+from math import *
+import time
+
+
+def hamiltonian(kx, ky): # 量子反常霍尔QAH模型（该参数对应的陈数为2）
+ t1 = 1.0
+ t2 = 1.0
+ t3 = 0.5
+ m = -1.0
+ matrix = np.zeros((2, 2), dtype=complex)
+ matrix[0, 1] = 2*t1*cos(kx)-1j*2*t1*cos(ky)
+ matrix[1, 0] = 2*t1*cos(kx)+1j*2*t1*cos(ky)
+ matrix[0, 0] = m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky)
+ matrix[1, 1] = -(m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky))
+ return matrix
+
+
+def main():
+ start_time = time.time()
+ n = 100 # 积分密度
+ delta = 1e-9 # 求导的偏离量
+ chern_number = 0 # 陈数初始化
+ for kx in np.arange(-pi, pi, 2*pi/n):
+ for ky in np.arange(-pi, pi, 2*pi/n):
+ H = hamiltonian(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
+ # print(np.argsort(np.real(eigenvalue))[0]) # 排序索引（从小到大）
+ # print(eigenvalue) # 排序前的本征值
+ # print(np.sort(np.real(eigenvalue))) # 排序后的本征值（从小到大）
+
+ H_delta_kx = hamiltonian(kx+delta, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
+
+ H_delta_ky = hamiltonian(kx, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
+
+ H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
+
+ # 价带的波函数的贝里联络(berry connection) # 求导后内积
+ A_x = np.dot(vector.transpose().conj(), (vector_delta_kx-vector)/delta) # 贝里联络Ax（x分量）
+ A_y = np.dot(vector.transpose().conj(), (vector_delta_ky-vector)/delta) # 贝里联络Ay（y分量）
+
+ A_x_delta_ky = np.dot(vector_delta_ky.transpose().conj(), (vector_delta_kx_ky-vector_delta_ky)/delta) # 略偏离ky的贝里联络Ax
+ A_y_delta_kx = np.dot(vector_delta_kx.transpose().conj(), (vector_delta_kx_ky-vector_delta_kx)/delta) # 略偏离kx的贝里联络Ay
+
+ # 贝里曲率(berry curvature)
+ F = (A_y_delta_kx-A_y)/delta-(A_x_delta_ky-A_x)/delta
+
+ # 陈数(chern number)
+ chern_number = chern_number + F*(2*pi/n)**2
+ chern_number = chern_number/(2*pi*1j)
+ print('Chern number = ', chern_number)
+ end_time = time.time()
+ print('运行时间(min)=', (end_time-start_time)/60)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/find_smooth_gauge_and_calculate_Chern_number.py b/academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/find_smooth_gauge_and_calculate_Chern_number.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/find_smooth_gauge_and_calculate_Chern_number.py
rename to academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/find_smooth_gauge_and_calculate_Chern_number.py
index 6701ca2..1ed9c38
--- a/academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/find_smooth_gauge_and_calculate_Chern_number.py
+++ b/academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/find_smooth_gauge_and_calculate_Chern_number.py
@@ -1,107 +1,107 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/3932
-"""
-
-import numpy as np
-from math import *
-import time
-import cmath
-
-
-def hamiltonian(kx, ky): # 量子反常霍尔QAH模型（该参数对应的陈数为2）
- t1 = 1.0
- t2 = 1.0
- t3 = 0.5
- m = -1.0
- matrix = np.zeros((2, 2), dtype=complex)
- matrix[0, 1] = 2*t1*cos(kx)-1j*2*t1*cos(ky)
- matrix[1, 0] = 2*t1*cos(kx)+1j*2*t1*cos(ky)
- matrix[0, 0] = m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky)
- matrix[1, 1] = -(m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky))
- return matrix
-
-
-def main():
- start_time = time.time()
- n = 20 # 积分密度
- delta = 1e-9 # 求导的偏离量
- chern_number = 0 # 陈数初始化
- for kx in np.arange(-pi, pi, 2*pi/n):
- for ky in np.arange(-pi, pi, 2*pi/n):
- H = hamiltonian(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
-
- H_delta_kx = hamiltonian(kx+delta, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
-
- H_delta_ky = hamiltonian(kx, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
-
- H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
-
- vector_delta_kx = find_vector_with_the_same_gauge(vector_delta_kx, vector)
- vector_delta_ky = find_vector_with_the_same_gauge(vector_delta_ky, vector)
- vector_delta_kx_ky = find_vector_with_the_same_gauge(vector_delta_kx_ky, vector)
-
- # 价带的波函数的贝里联络(berry connection) # 求导后内积
- A_x = np.dot(vector.transpose().conj(), (vector_delta_kx-vector)/delta) # 贝里联络Ax（x分量）
- A_y = np.dot(vector.transpose().conj(), (vector_delta_ky-vector)/delta) # 贝里联络Ay（y分量）
-
- A_x_delta_ky = np.dot(vector_delta_ky.transpose().conj(), (vector_delta_kx_ky-vector_delta_ky)/delta) # 略偏离ky的贝里联络Ax
- A_y_delta_kx = np.dot(vector_delta_kx.transpose().conj(), (vector_delta_kx_ky-vector_delta_kx)/delta) # 略偏离kx的贝里联络Ay
-
- # 贝里曲率(berry curvature)
- F = (A_y_delta_kx-A_y)/delta-(A_x_delta_ky-A_x)/delta
-
- # 陈数(chern number)
- chern_number = chern_number + F*(2*pi/n)**2
- chern_number = chern_number/(2*pi*1j)
- print('Chern number = ', chern_number)
- end_time = time.time()
- print('运行时间(min)=', (end_time-start_time)/60)
-
-
-def find_vector_with_the_same_gauge(vector_1, vector_0):
- # 寻找近似的同一的规范
- phase_1_pre = 0
- phase_2_pre = pi
- n_test = 10001
- for i0 in range(n_test):
- test_1 = np.sum(np.abs(vector_1*cmath.exp(1j*phase_1_pre) - vector_0))
- test_2 = np.sum(np.abs(vector_1*cmath.exp(1j*phase_2_pre) - vector_0))
- if test_1 < 1e-8:
- phase = phase_1_pre
- # print('Done with i0=', i0)
- break
- if i0 == n_test-1:
- phase = phase_1_pre
- print('Gauge Not Found with i0=', i0)
- if test_1 < test_2:
- if i0 == 0:
- phase_1 = phase_1_pre-(phase_2_pre-phase_1_pre)/2
- phase_2 = phase_1_pre+(phase_2_pre-phase_1_pre)/2
- else:
- phase_1 = phase_1_pre
- phase_2 = phase_1_pre+(phase_2_pre-phase_1_pre)/2
- else:
- if i0 == 0:
- phase_1 = phase_2_pre-(phase_2_pre-phase_1_pre)/2
- phase_2 = phase_2_pre+(phase_2_pre-phase_1_pre)/2
- else:
- phase_1 = phase_2_pre-(phase_2_pre-phase_1_pre)/2
- phase_2 = phase_2_pre
- phase_1_pre = phase_1
- phase_2_pre = phase_2
- vector_1 = vector_1*cmath.exp(1j*phase)
- # print('二分查找找到的规范=', phase)
- return vector_1
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/3932
+"""
+
+import numpy as np
+from math import *
+import time
+import cmath
+
+
+def hamiltonian(kx, ky): # 量子反常霍尔QAH模型（该参数对应的陈数为2）
+ t1 = 1.0
+ t2 = 1.0
+ t3 = 0.5
+ m = -1.0
+ matrix = np.zeros((2, 2), dtype=complex)
+ matrix[0, 1] = 2*t1*cos(kx)-1j*2*t1*cos(ky)
+ matrix[1, 0] = 2*t1*cos(kx)+1j*2*t1*cos(ky)
+ matrix[0, 0] = m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky)
+ matrix[1, 1] = -(m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky))
+ return matrix
+
+
+def main():
+ start_time = time.time()
+ n = 20 # 积分密度
+ delta = 1e-9 # 求导的偏离量
+ chern_number = 0 # 陈数初始化
+ for kx in np.arange(-pi, pi, 2*pi/n):
+ for ky in np.arange(-pi, pi, 2*pi/n):
+ H = hamiltonian(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
+
+ H_delta_kx = hamiltonian(kx+delta, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
+
+ H_delta_ky = hamiltonian(kx, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
+
+ H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
+
+ vector_delta_kx = find_vector_with_the_same_gauge(vector_delta_kx, vector)
+ vector_delta_ky = find_vector_with_the_same_gauge(vector_delta_ky, vector)
+ vector_delta_kx_ky = find_vector_with_the_same_gauge(vector_delta_kx_ky, vector)
+
+ # 价带的波函数的贝里联络(berry connection) # 求导后内积
+ A_x = np.dot(vector.transpose().conj(), (vector_delta_kx-vector)/delta) # 贝里联络Ax（x分量）
+ A_y = np.dot(vector.transpose().conj(), (vector_delta_ky-vector)/delta) # 贝里联络Ay（y分量）
+
+ A_x_delta_ky = np.dot(vector_delta_ky.transpose().conj(), (vector_delta_kx_ky-vector_delta_ky)/delta) # 略偏离ky的贝里联络Ax
+ A_y_delta_kx = np.dot(vector_delta_kx.transpose().conj(), (vector_delta_kx_ky-vector_delta_kx)/delta) # 略偏离kx的贝里联络Ay
+
+ # 贝里曲率(berry curvature)
+ F = (A_y_delta_kx-A_y)/delta-(A_x_delta_ky-A_x)/delta
+
+ # 陈数(chern number)
+ chern_number = chern_number + F*(2*pi/n)**2
+ chern_number = chern_number/(2*pi*1j)
+ print('Chern number = ', chern_number)
+ end_time = time.time()
+ print('运行时间(min)=', (end_time-start_time)/60)
+
+
+def find_vector_with_the_same_gauge(vector_1, vector_0):
+ # 寻找近似的同一的规范
+ phase_1_pre = 0
+ phase_2_pre = pi
+ n_test = 10001
+ for i0 in range(n_test):
+ test_1 = np.sum(np.abs(vector_1*cmath.exp(1j*phase_1_pre) - vector_0))
+ test_2 = np.sum(np.abs(vector_1*cmath.exp(1j*phase_2_pre) - vector_0))
+ if test_1 < 1e-8:
+ phase = phase_1_pre
+ # print('Done with i0=', i0)
+ break
+ if i0 == n_test-1:
+ phase = phase_1_pre
+ print('Gauge Not Found with i0=', i0)
+ if test_1 < test_2:
+ if i0 == 0:
+ phase_1 = phase_1_pre-(phase_2_pre-phase_1_pre)/2
+ phase_2 = phase_1_pre+(phase_2_pre-phase_1_pre)/2
+ else:
+ phase_1 = phase_1_pre
+ phase_2 = phase_1_pre+(phase_2_pre-phase_1_pre)/2
+ else:
+ if i0 == 0:
+ phase_1 = phase_2_pre-(phase_2_pre-phase_1_pre)/2
+ phase_2 = phase_2_pre+(phase_2_pre-phase_1_pre)/2
+ else:
+ phase_1 = phase_2_pre-(phase_2_pre-phase_1_pre)/2
+ phase_2 = phase_2_pre
+ phase_1_pre = phase_1
+ phase_2_pre = phase_2
+ vector_1 = vector_1*cmath.exp(1j*phase)
+ # print('二分查找找到的规范=', phase)
+ return vector_1
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/fix_gauge_and_calculate_Chern_number.py b/academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/fix_gauge_and_calculate_Chern_number.py
similarity index 100%
rename from academic_codes/2020.01.05_calculation_of_Chern_number_by_definition_method/fix_gauge_and_calculate_Chern_number.py
rename to academic_codes/topological_invariant/2020.01.05_calculation_of_Chern_number_by_definition_method/fix_gauge_and_calculate_Chern_number.py
diff --git a/academic_codes/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.m b/academic_codes/topological_invariant/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.m
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.m
rename to academic_codes/topological_invariant/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.m
index 83815b9..9c230aa
--- a/academic_codes/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.m
+++ b/academic_codes/topological_invariant/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.m
@@ -1,40 +1,40 @@
-% This code is supported by the website: https://www.guanjihuan.com
-% The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4179
-
-% 陈数高效法
-clear;clc;
-n=1000 % 积分密度
-delta=2*pi/n;
-C=0;
-for kx=-pi:(2*pi/n):pi
- for ky=-pi:(2*pi/n):pi
- VV=get_vector(HH(kx,ky));
- Vkx=get_vector(HH(kx+delta,ky)); % 略偏离kx的波函数
- Vky=get_vector(HH(kx,ky+delta)); % 略偏离ky的波函数
- Vkxky=get_vector(HH(kx+delta,ky+delta)); % 略偏离kx，ky的波函数
-
- Ux = VV'*Vkx/abs(VV'*Vkx);
- Uy = VV'*Vky/abs(VV'*Vky);
- Ux_y = Vky'*Vkxky/abs(Vky'*Vkxky);
- Uy_x = Vkx'*Vkxky/abs(Vkx'*Vkxky);
-
- % berry curvature
- F=log(Ux*Uy_x*(1/Ux_y)*(1/Uy));
- % chern number
- C=C+F;
- end
-end
-C=C/(2*pi*1i)
-
-function vector_new = get_vector(H)
-[vector,eigenvalue] = eig(H);
-[eigenvalue, index]=sort(diag(eigenvalue), 'descend');
-vector_new = vector(:, index(2));
-end
-
-function H=HH(kx,ky)
-H(1,2)=2*cos(kx)-1i*2*cos(ky);
-H(2,1)=2*cos(kx)+1i*2*cos(ky);
-H(1,1)=-1+2*0.5*sin(kx)+2*0.5*sin(ky)+2*cos(kx+ky);
-H(2,2)=-(-1+2*0.5*sin(kx)+2*0.5*sin(ky)+2*cos(kx+ky));
+% This code is supported by the website: https://www.guanjihuan.com
+% The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4179
+
+% 陈数高效法
+clear;clc;
+n=1000 % 积分密度
+delta=2*pi/n;
+C=0;
+for kx=-pi:(2*pi/n):pi
+ for ky=-pi:(2*pi/n):pi
+ VV=get_vector(HH(kx,ky));
+ Vkx=get_vector(HH(kx+delta,ky)); % 略偏离kx的波函数
+ Vky=get_vector(HH(kx,ky+delta)); % 略偏离ky的波函数
+ Vkxky=get_vector(HH(kx+delta,ky+delta)); % 略偏离kx，ky的波函数
+
+ Ux = VV'*Vkx/abs(VV'*Vkx);
+ Uy = VV'*Vky/abs(VV'*Vky);
+ Ux_y = Vky'*Vkxky/abs(Vky'*Vkxky);
+ Uy_x = Vkx'*Vkxky/abs(Vkx'*Vkxky);
+
+ % berry curvature
+ F=log(Ux*Uy_x*(1/Ux_y)*(1/Uy));
+ % chern number
+ C=C+F;
+ end
+end
+C=C/(2*pi*1i)
+
+function vector_new = get_vector(H)
+[vector,eigenvalue] = eig(H);
+[eigenvalue, index]=sort(diag(eigenvalue), 'descend');
+vector_new = vector(:, index(2));
+end
+
+function H=HH(kx,ky)
+H(1,2)=2*cos(kx)-1i*2*cos(ky);
+H(2,1)=2*cos(kx)+1i*2*cos(ky);
+H(1,1)=-1+2*0.5*sin(kx)+2*0.5*sin(ky)+2*cos(kx+ky);
+H(2,2)=-(-1+2*0.5*sin(kx)+2*0.5*sin(ky)+2*cos(kx+ky));
end
\ No newline at end of file
diff --git a/academic_codes/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.py b/academic_codes/topological_invariant/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.py
rename to academic_codes/topological_invariant/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.py
index fe31f16..b2f61a5
--- a/academic_codes/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.py
+++ b/academic_codes/topological_invariant/2020.02.26_calculation_of_Chern_number_by_efficient_method/calculation_of_Chern_number_by_efficient_method.py
@@ -1,65 +1,65 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4179
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import * # 引入pi, cos等
-import cmath
-import time
-
-
-def hamiltonian(kx, ky): # 量子反常霍尔QAH模型（该参数对应的陈数为2）
- t1 = 1.0
- t2 = 1.0
- t3 = 0.5
- m = -1.0
- matrix = np.zeros((2, 2))*(1+0j)
- matrix[0, 1] = 2*t1*cos(kx)-1j*2*t1*cos(ky)
- matrix[1, 0] = 2*t1*cos(kx)+1j*2*t1*cos(ky)
- matrix[0, 0] = m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky)
- matrix[1, 1] = -(m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky))
- return matrix
-
-
-def main():
- start_time = time.time()
- n = 100
- delta = 2*pi/n
- chern_number = 0 # 陈数初始化
- for kx in np.arange(-pi, pi, 2*pi/n):
- for ky in np.arange(-pi, pi, 2*pi/n):
- H = hamiltonian(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
-
- H_delta_kx = hamiltonian(kx+delta, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
-
- H_delta_ky = hamiltonian(kx, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
-
- H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
-
- Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
- Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
- Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
- Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
-
- F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
-
- # 陈数(chern number)
- chern_number = chern_number + F
- chern_number = chern_number/(2*pi*1j)
- print('Chern number = ', chern_number)
- end_time = time.time()
- print('运行时间(min)=', (end_time-start_time)/60)
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/4179
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import * # 引入pi, cos等
+import cmath
+import time
+
+
+def hamiltonian(kx, ky): # 量子反常霍尔QAH模型（该参数对应的陈数为2）
+ t1 = 1.0
+ t2 = 1.0
+ t3 = 0.5
+ m = -1.0
+ matrix = np.zeros((2, 2))*(1+0j)
+ matrix[0, 1] = 2*t1*cos(kx)-1j*2*t1*cos(ky)
+ matrix[1, 0] = 2*t1*cos(kx)+1j*2*t1*cos(ky)
+ matrix[0, 0] = m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky)
+ matrix[1, 1] = -(m+2*t3*sin(kx)+2*t3*sin(ky)+2*t2*cos(kx+ky))
+ return matrix
+
+
+def main():
+ start_time = time.time()
+ n = 100
+ delta = 2*pi/n
+ chern_number = 0 # 陈数初始化
+ for kx in np.arange(-pi, pi, 2*pi/n):
+ for ky in np.arange(-pi, pi, 2*pi/n):
+ H = hamiltonian(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
+
+ H_delta_kx = hamiltonian(kx+delta, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
+
+ H_delta_ky = hamiltonian(kx, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
+
+ H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
+
+ Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
+ Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
+ Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
+ Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
+
+ F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
+
+ # 陈数(chern number)
+ chern_number = chern_number + F
+ chern_number = chern_number/(2*pi*1j)
+ print('Chern number = ', chern_number)
+ end_time = time.time()
+ print('运行时间(min)=', (end_time-start_time)/60)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model.py b/academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model.py
rename to academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model.py
index d6c8e66..3c9c4b5
--- a/academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model.py
+++ b/academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model.py
@@ -1,94 +1,94 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5133
-"""
-
-import numpy as np
-from math import * # 引入pi, cos等
-import cmath
-import time
-import functools # 使用偏函数functools.partial()
-
-
-def hamiltonian(k1, k2, M, t1, t2, phi, a=1/sqrt(3)): # Haldane哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
- # 初始化为零矩阵
- h0 = np.zeros((2, 2), dtype=complex)
- h1 = np.zeros((2, 2), dtype=complex)
- h2 = np.zeros((2, 2), dtype=complex)
-
- # 质量项(mass term), 用于打开带隙
- h0[0, 0] = M
- h0[1, 1] = -M
-
- # 最近邻项
- h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
- h1[0, 1] = h1[1, 0].conj()
-
- # # 最近邻项也可写成这种形式
- # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
- # h1[0, 1] = h1[1, 0].conj()
-
- #次近邻项 # 对应陈数为-1
- h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
- h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
-
- # # 次近邻项 # 对应陈数为1
- # h2[0, 0] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
- # h2[1, 1] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
-
- matrix = h0 + h1 + h2 + h2.transpose().conj()
- return matrix
-
-
-def main():
- start_clock = time.perf_counter()
- delta = 0.005
- chern_number = 0 # 陈数初始化
-
- # 几个坐标中常出现的项
- a = 1/sqrt(3)
- aa1 = 4*sqrt(3)*pi/9/a
- aa2 = 2*sqrt(3)*pi/9/a
- bb1 = 2*pi/3/a
-
- hamiltonian0 = functools.partial(hamiltonian, M=2/3, t1=1, t2=1/3, phi=pi/4, a=a) # 使用偏函数，固定一些参数
-
- for kx in np.arange(-aa1, aa1, delta):
- print(kx)
- for ky in np.arange(-bb1, bb1, delta):
- if (-aa2<=kx<=aa2) or (kx>aa2 and -(aa1-kx)*tan(pi/3)<=ky<=(aa1-kx)*tan(pi/3)) or (kx<-aa2 and -(kx-(-aa1))*tan(pi/3)<=ky<=(kx-(-aa1))*tan(pi/3)): # 限制在六角格子布里渊区内
-
- H = hamiltonian0(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
-
- H_delta_kx = hamiltonian0(kx+delta, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
-
- H_delta_ky = hamiltonian0(kx, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
-
- H_delta_kx_ky = hamiltonian0(kx+delta, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
-
- Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
- Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
- Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
- Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
-
- F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
-
- # 陈数(chern number)
- chern_number = chern_number + F
-
- chern_number = chern_number/(2*pi*1j)
- print('Chern number = ', chern_number)
- end_clock = time.perf_counter()
- print('CPU执行时间(min)=', (end_clock-start_clock)/60)
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5133
+"""
+
+import numpy as np
+from math import * # 引入pi, cos等
+import cmath
+import time
+import functools # 使用偏函数functools.partial()
+
+
+def hamiltonian(k1, k2, M, t1, t2, phi, a=1/sqrt(3)): # Haldane哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
+ # 初始化为零矩阵
+ h0 = np.zeros((2, 2), dtype=complex)
+ h1 = np.zeros((2, 2), dtype=complex)
+ h2 = np.zeros((2, 2), dtype=complex)
+
+ # 质量项(mass term), 用于打开带隙
+ h0[0, 0] = M
+ h0[1, 1] = -M
+
+ # 最近邻项
+ h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
+ h1[0, 1] = h1[1, 0].conj()
+
+ # # 最近邻项也可写成这种形式
+ # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
+ # h1[0, 1] = h1[1, 0].conj()
+
+ #次近邻项 # 对应陈数为-1
+ h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+ h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+
+ # # 次近邻项 # 对应陈数为1
+ # h2[0, 0] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+ # h2[1, 1] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+
+ matrix = h0 + h1 + h2 + h2.transpose().conj()
+ return matrix
+
+
+def main():
+ start_clock = time.perf_counter()
+ delta = 0.005
+ chern_number = 0 # 陈数初始化
+
+ # 几个坐标中常出现的项
+ a = 1/sqrt(3)
+ aa1 = 4*sqrt(3)*pi/9/a
+ aa2 = 2*sqrt(3)*pi/9/a
+ bb1 = 2*pi/3/a
+
+ hamiltonian0 = functools.partial(hamiltonian, M=2/3, t1=1, t2=1/3, phi=pi/4, a=a) # 使用偏函数，固定一些参数
+
+ for kx in np.arange(-aa1, aa1, delta):
+ print(kx)
+ for ky in np.arange(-bb1, bb1, delta):
+ if (-aa2<=kx<=aa2) or (kx>aa2 and -(aa1-kx)*tan(pi/3)<=ky<=(aa1-kx)*tan(pi/3)) or (kx<-aa2 and -(kx-(-aa1))*tan(pi/3)<=ky<=(kx-(-aa1))*tan(pi/3)): # 限制在六角格子布里渊区内
+
+ H = hamiltonian0(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
+
+ H_delta_kx = hamiltonian0(kx+delta, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
+
+ H_delta_ky = hamiltonian0(kx, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
+
+ H_delta_kx_ky = hamiltonian0(kx+delta, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
+
+ Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
+ Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
+ Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
+ Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
+
+ F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
+
+ # 陈数(chern number)
+ chern_number = chern_number + F
+
+ chern_number = chern_number/(2*pi*1j)
+ print('Chern number = ', chern_number)
+ end_clock = time.perf_counter()
+ print('CPU执行时间(min)=', (end_clock-start_clock)/60)
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_2.py b/academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_2.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_2.py
rename to academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_2.py
index d4a3cc2..b91bf0b
--- a/academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_2.py
+++ b/academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_2.py
@@ -1,91 +1,91 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5133
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import * # 引入pi, cos等
-import cmath
-import time
-import functools # 使用偏函数functools.partial()
-
-
-def hamiltonian(k1, k2, M, t1, t2, phi, a=1/sqrt(3)): # Haldane哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
- # 初始化为零矩阵
- h0 = np.zeros((2, 2), dtype=complex)
- h1 = np.zeros((2, 2), dtype=complex)
- h2 = np.zeros((2, 2), dtype=complex)
-
- # 质量项(mass term), 用于打开带隙
- h0[0, 0] = M
- h0[1, 1] = -M
-
- # 最近邻项
- h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
- h1[0, 1] = h1[1, 0].conj()
-
- # # 最近邻项也可写成这种形式
- # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
- # h1[0, 1] = h1[1, 0].conj()
-
- #次近邻项 # 对应陈数为-1
- h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
- h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
-
- # # 次近邻项 # 对应陈数为1
- # h2[0, 0] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
- # h2[1, 1] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
-
- matrix = h0 + h1 + h2 + h2.transpose().conj()
- return matrix
-
-
-def main():
- start_clock = time.perf_counter()
- delta = 0.005
- chern_number = 0 # 陈数初始化
-
- # 常出现的项
- a = 1/sqrt(3)
- bb1 = 2*sqrt(3)*pi/3/a
- bb2 = 2*pi/3/a
-
- hamiltonian0 = functools.partial(hamiltonian, M=2/3, t1=1, t2=1/3, phi=pi/4, a=a) # 使用偏函数，固定一些参数
-
- for kx in np.arange(0 , bb1, delta):
- print(kx)
- for ky in np.arange(0, 2*bb2, delta):
- H = hamiltonian0(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
-
- H_delta_kx = hamiltonian0(kx+delta, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
-
- H_delta_ky = hamiltonian0(kx, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
-
- H_delta_kx_ky = hamiltonian0(kx+delta, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
-
- Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
- Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
- Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
- Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
-
- F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
- # 陈数(chern number)
- chern_number = chern_number + F
-
- chern_number = chern_number/(2*pi*1j)
- print('Chern number = ', chern_number)
- end_clock = time.perf_counter()
- print('CPU执行时间(min)=', (end_clock-start_clock)/60)
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5133
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import * # 引入pi, cos等
+import cmath
+import time
+import functools # 使用偏函数functools.partial()
+
+
+def hamiltonian(k1, k2, M, t1, t2, phi, a=1/sqrt(3)): # Haldane哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
+ # 初始化为零矩阵
+ h0 = np.zeros((2, 2), dtype=complex)
+ h1 = np.zeros((2, 2), dtype=complex)
+ h2 = np.zeros((2, 2), dtype=complex)
+
+ # 质量项(mass term), 用于打开带隙
+ h0[0, 0] = M
+ h0[1, 1] = -M
+
+ # 最近邻项
+ h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
+ h1[0, 1] = h1[1, 0].conj()
+
+ # # 最近邻项也可写成这种形式
+ # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
+ # h1[0, 1] = h1[1, 0].conj()
+
+ #次近邻项 # 对应陈数为-1
+ h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+ h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+
+ # # 次近邻项 # 对应陈数为1
+ # h2[0, 0] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+ # h2[1, 1] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+
+ matrix = h0 + h1 + h2 + h2.transpose().conj()
+ return matrix
+
+
+def main():
+ start_clock = time.perf_counter()
+ delta = 0.005
+ chern_number = 0 # 陈数初始化
+
+ # 常出现的项
+ a = 1/sqrt(3)
+ bb1 = 2*sqrt(3)*pi/3/a
+ bb2 = 2*pi/3/a
+
+ hamiltonian0 = functools.partial(hamiltonian, M=2/3, t1=1, t2=1/3, phi=pi/4, a=a) # 使用偏函数，固定一些参数
+
+ for kx in np.arange(0 , bb1, delta):
+ print(kx)
+ for ky in np.arange(0, 2*bb2, delta):
+ H = hamiltonian0(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
+
+ H_delta_kx = hamiltonian0(kx+delta, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
+
+ H_delta_ky = hamiltonian0(kx, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
+
+ H_delta_kx_ky = hamiltonian0(kx+delta, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
+
+ Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
+ Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
+ Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
+ Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
+
+ F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
+ # 陈数(chern number)
+ chern_number = chern_number + F
+
+ chern_number = chern_number/(2*pi*1j)
+ print('Chern number = ', chern_number)
+ end_clock = time.perf_counter()
+ print('CPU执行时间(min)=', (end_clock-start_clock)/60)
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_3.py b/academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_3.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_3.py
rename to academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_3.py
index 09e3d9f..8b4c986
--- a/academic_codes/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_3.py
+++ b/academic_codes/topological_invariant/2020.07.13_calculation_of_Chern_number_in_Haldane_model/Chern_number_in_Haldane_model_by_integration_method_3.py
@@ -1,99 +1,99 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5133
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import * # 引入pi, cos等
-import cmath
-import time
-import functools # 使用偏函数functools.partial()
-
-
-def hamiltonian(k1, k2, M, t1, t2, phi, a=1/sqrt(3)): # Haldane哈密顿量# Haldane哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
- # 初始化为零矩阵
- h0 = np.zeros((2, 2), dtype=complex)
- h1 = np.zeros((2, 2), dtype=complex)
- h2 = np.zeros((2, 2), dtype=complex)
-
- # 质量项(mass term), 用于打开带隙
- h0[0, 0] = M
- h0[1, 1] = -M
-
- # 最近邻项
- h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
- h1[0, 1] = h1[1, 0].conj()
-
- # # 最近邻项也可写成这种形式
- # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
- # h1[0, 1] = h1[1, 0].conj()
-
- #次近邻项 # 对应陈数为-1
- h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
- h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
-
- # # 次近邻项 # 对应陈数为1
- # h2[0, 0] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
- # h2[1, 1] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
-
- matrix = h0 + h1 + h2 + h2.transpose().conj()
- return matrix
-
-
-def main():
- start_clock = time.perf_counter()
- delta = 0.005
- chern_number = 0 # 陈数初始化
-
- # 常出现的项
- a = 1/sqrt(3)
- bb1 = 2*sqrt(3)*pi/3/a
- bb2 = 2*pi/3/a
-
- hamiltonian0 = functools.partial(hamiltonian, M=2/3, t1=1, t2=1/3, phi=pi/4, a=a) # 使用偏函数，固定一些参数
-
- for k1 in np.arange(0 , 1, delta):
- print(k1)
- for k2 in np.arange(0, 1, delta):
- # 坐标变换
- kx = (k1-k2)*bb1
- ky = (k1+k2)*bb2
-
- # 这里乘2或除以2是为了保证“步长与积分个数的乘积刚好为布里渊区面积”
- delta_x = delta*bb1*2
- delta_y = delta*bb2*2/2
-
- H = hamiltonian0(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
-
- H_delta_kx = hamiltonian0(kx+delta_x, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
-
- H_delta_ky = hamiltonian0(kx, ky+delta_y)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
-
- H_delta_kx_ky = hamiltonian0(kx+delta_x, ky+delta_y)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
-
- Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
- Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
- Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
- Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
-
- F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
- # 陈数(chern number)
- chern_number = chern_number + F
-
- chern_number = chern_number/(2*pi*1j)
- print('Chern number = ', chern_number)
- end_clock = time.perf_counter()
- print('CPU执行时间(min)=', (end_clock-start_clock)/60)
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5133
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import * # 引入pi, cos等
+import cmath
+import time
+import functools # 使用偏函数functools.partial()
+
+
+def hamiltonian(k1, k2, M, t1, t2, phi, a=1/sqrt(3)): # Haldane哈密顿量# Haldane哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
+ # 初始化为零矩阵
+ h0 = np.zeros((2, 2), dtype=complex)
+ h1 = np.zeros((2, 2), dtype=complex)
+ h2 = np.zeros((2, 2), dtype=complex)
+
+ # 质量项(mass term), 用于打开带隙
+ h0[0, 0] = M
+ h0[1, 1] = -M
+
+ # 最近邻项
+ h1[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
+ h1[0, 1] = h1[1, 0].conj()
+
+ # # 最近邻项也可写成这种形式
+ # h1[1, 0] = t1+t1*cmath.exp(1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)+t1*cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a)
+ # h1[0, 1] = h1[1, 0].conj()
+
+ #次近邻项 # 对应陈数为-1
+ h2[0, 0] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+ h2[1, 1] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+
+ # # 次近邻项 # 对应陈数为1
+ # h2[0, 0] = t2*cmath.exp(1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+ # h2[1, 1] = t2*cmath.exp(-1j*phi)*(cmath.exp(1j*sqrt(3)*k1*a)+cmath.exp(-1j*sqrt(3)/2*k1*a+1j*3/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j*3/2*k2*a))
+
+ matrix = h0 + h1 + h2 + h2.transpose().conj()
+ return matrix
+
+
+def main():
+ start_clock = time.perf_counter()
+ delta = 0.005
+ chern_number = 0 # 陈数初始化
+
+ # 常出现的项
+ a = 1/sqrt(3)
+ bb1 = 2*sqrt(3)*pi/3/a
+ bb2 = 2*pi/3/a
+
+ hamiltonian0 = functools.partial(hamiltonian, M=2/3, t1=1, t2=1/3, phi=pi/4, a=a) # 使用偏函数，固定一些参数
+
+ for k1 in np.arange(0 , 1, delta):
+ print(k1)
+ for k2 in np.arange(0, 1, delta):
+ # 坐标变换
+ kx = (k1-k2)*bb1
+ ky = (k1+k2)*bb2
+
+ # 这里乘2或除以2是为了保证“步长与积分个数的乘积刚好为布里渊区面积”
+ delta_x = delta*bb1*2
+ delta_y = delta*bb2*2/2
+
+ H = hamiltonian0(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
+
+ H_delta_kx = hamiltonian0(kx+delta_x, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
+
+ H_delta_ky = hamiltonian0(kx, ky+delta_y)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
+
+ H_delta_kx_ky = hamiltonian0(kx+delta_x, ky+delta_y)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
+
+ Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
+ Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
+ Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
+ Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
+
+ F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
+ # 陈数(chern number)
+ chern_number = chern_number + F
+
+ chern_number = chern_number/(2*pi*1j)
+ print('Chern number = ', chern_number)
+ end_clock = time.perf_counter()
+ print('CPU执行时间(min)=', (end_clock-start_clock)/60)
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/Winding_number_in_SSH_model.py b/academic_codes/topological_invariant/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/Winding_number_in_SSH_model.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/Winding_number_in_SSH_model.py
rename to academic_codes/topological_invariant/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/Winding_number_in_SSH_model.py
index 93c5da1..130baff
--- a/academic_codes/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/Winding_number_in_SSH_model.py
+++ b/academic_codes/topological_invariant/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/Winding_number_in_SSH_model.py
@@ -1,41 +1,41 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5025
-"""
-
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import time
-
-def hamiltonian(k): # SSH模型
- v=0.6
- w=1
- matrix = np.zeros((2, 2), dtype=complex)
- matrix[0,1] = v+w*cmath.exp(-1j*k)
- matrix[1,0] = v+w*cmath.exp(1j*k)
- return matrix
-
-
-def main():
- start_clock = time.perf_counter()
- delta_1 = 1e-9 # 求导的步长（求导的步长可以尽可能短）
- delta_2 = 1e-5 # 积分的步长（积分步长和计算时间相关，因此取一个合理值即可）
- W = 0 # Winding number初始化
- for k in np.arange(-pi, pi, delta_2):
- H = hamiltonian(k)
- log0 = cmath.log(H[0, 1])
-
- H_delta = hamiltonian(k+delta_1)
- log1 = cmath.log(H_delta[0, 1])
-
- W = W + (log1-log0)/delta_1*delta_2 # Winding number
- print('Winding number = ', W/2/pi/1j)
- end_clock = time.perf_counter()
- print('CPU执行时间(min)=', (end_clock-start_clock)/60)
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5025
+"""
+
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import time
+
+def hamiltonian(k): # SSH模型
+ v=0.6
+ w=1
+ matrix = np.zeros((2, 2), dtype=complex)
+ matrix[0,1] = v+w*cmath.exp(-1j*k)
+ matrix[1,0] = v+w*cmath.exp(1j*k)
+ return matrix
+
+
+def main():
+ start_clock = time.perf_counter()
+ delta_1 = 1e-9 # 求导的步长（求导的步长可以尽可能短）
+ delta_2 = 1e-5 # 积分的步长（积分步长和计算时间相关，因此取一个合理值即可）
+ W = 0 # Winding number初始化
+ for k in np.arange(-pi, pi, delta_2):
+ H = hamiltonian(k)
+ log0 = cmath.log(H[0, 1])
+
+ H_delta = hamiltonian(k+delta_1)
+ log1 = cmath.log(H_delta[0, 1])
+
+ W = W + (log1-log0)/delta_1*delta_2 # Winding number
+ print('Winding number = ', W/2/pi/1j)
+ end_clock = time.perf_counter()
+ print('CPU执行时间(min)=', (end_clock-start_clock)/60)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/bands_of_SSH_model.py b/academic_codes/topological_invariant/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/bands_of_SSH_model.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/bands_of_SSH_model.py
rename to academic_codes/topological_invariant/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/bands_of_SSH_model.py
index 6e669c6..3ebfd19
--- a/academic_codes/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/bands_of_SSH_model.py
+++ b/academic_codes/topological_invariant/2020.07.26_Hamiltonian_bands_and_Winding_number_in_SSH_model/bands_of_SSH_model.py
@@ -1,42 +1,42 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5025
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-
-
-def hamiltonian(k): # SSH模型
- v=0.6
- w=1
- matrix = np.zeros((2, 2), dtype=complex)
- matrix[0,1] = v+w*cmath.exp(-1j*k)
- matrix[1,0] = v+w*cmath.exp(1j*k)
- return matrix
-
-
-def main():
- k = np.linspace(-pi, pi, 100)
- plot_bands_one_dimension(k, hamiltonian)
-
-
-def plot_bands_one_dimension(k, hamiltonian):
- dim = hamiltonian(0).shape[0]
- dim_k = k.shape[0]
- eigenvalue_k = np.zeros((dim_k, dim))
- i0 = 0
- for k0 in k:
- matrix0 = hamiltonian(k0)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
- i0 += 1
- for dim0 in range(dim):
- plt.plot(k, eigenvalue_k[:, dim0], '-k')
- plt.show()
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5025
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+
+
+def hamiltonian(k): # SSH模型
+ v=0.6
+ w=1
+ matrix = np.zeros((2, 2), dtype=complex)
+ matrix[0,1] = v+w*cmath.exp(-1j*k)
+ matrix[1,0] = v+w*cmath.exp(1j*k)
+ return matrix
+
+
+def main():
+ k = np.linspace(-pi, pi, 100)
+ plot_bands_one_dimension(k, hamiltonian)
+
+
+def plot_bands_one_dimension(k, hamiltonian):
+ dim = hamiltonian(0).shape[0]
+ dim_k = k.shape[0]
+ eigenvalue_k = np.zeros((dim_k, dim))
+ i0 = 0
+ for k0 in k:
+ matrix0 = hamiltonian(k0)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[i0, :] = np.sort(np.real(eigenvalue[:]))
+ i0 += 1
+ for dim0 in range(dim):
+ plt.plot(k, eigenvalue_k[:, dim0], '-k')
+ plt.show()
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.m b/academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.m
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.m
rename to academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.m
index 4ae71f4..b3b663d
--- a/academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.m
+++ b/academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.m
@@ -1,68 +1,68 @@
-% This code is supported by the website: https://www.guanjihuan.com
-% The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5778
-
-clear;clc;
-delta=0.1;
-Z2=0;
-for kx=-pi:0.1:0
- for ky=-pi:0.1:pi
- [V1,V2]=get_vector(Hamiltonian(kx,ky));
- [Vkx1,Vkx2]=get_vector(Hamiltonian(kx+delta,ky)); % 略偏离kx的波函数
- [Vky1,Vky2]=get_vector(Hamiltonian(kx,ky+delta)); % 略偏离ky的波函数
- [Vkxky1,Vkxky2]=get_vector(Hamiltonian(kx+delta,ky+delta)); % 略偏离kx，ky的波函数
-
- Ux = dot_and_det(V1, Vkx1, V2, Vkx2);
- Uy = dot_and_det(V1, Vky1, V2, Vky2);
- Ux_y = dot_and_det(Vky1, Vkxky1, Vky2, Vkxky2);
- Uy_x = dot_and_det(Vkx1, Vkxky1, Vkx2, Vkxky2);
-
- F=imag(log(Ux*Uy_x*(conj(Ux_y))*(conj(Uy))));
- A=imag(log(Ux))+imag(log(Uy_x))+imag(log(conj(Ux_y)))+imag(log(conj(Uy)));
-
- Z2 = Z2+(A-F)/(2*pi);
- end
-end
-Z2= mod(Z2, 2)
-
-
-function dd = dot_and_det(a1,b1,a2,b2) % 内积组成的矩阵对应的行列式
-x1=a1'*b1;
-x2=a2'*b2;
-x3=a1'*b2;
-x4=a2'*b1;
-dd =x1*x2-x3*x4;
-end
-
-
-function [vector_new_1, vector_new_2] = get_vector(H)
-[vector,eigenvalue] = eig(H);
-[eigenvalue, index]=sort(diag(eigenvalue), 'ascend');
-vector_new_2 = vector(:, index(2));
-vector_new_1 = vector(:, index(1));
-end
-
-
-function H=Hamiltonian(kx,ky) % BHZ模型
-A=0.3645/5;
-B=-0.686/25;
-C=0;
-D=-0.512/25;
-M=-0.01;
-
-H=zeros(4,4);
-varepsilon = C-2*D*(2-cos(kx)-cos(ky));
-d3 = -2*B*(2-(M/2/B)-cos(kx)-cos(ky));
-d1_d2 = A*(sin(kx)+1j*sin(ky));
-H(1, 1) = varepsilon+d3;
-H(2, 2) = varepsilon-d3;
-H(1, 2) = conj(d1_d2);
-H(2, 1) = d1_d2 ;
-
-varepsilon = C-2*D*(2-cos(-kx)-cos(-ky));
-d3 = -2*B*(2-(M/2/B)-cos(-kx)-cos(-ky));
-d1_d2 = A*(sin(-kx)+1j*sin(-ky));
-H(3, 3) = varepsilon+d3;
-H(4, 4) = varepsilon-d3;
-H(3, 4) = d1_d2 ;
-H(4, 3) = conj(d1_d2);
+% This code is supported by the website: https://www.guanjihuan.com
+% The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5778
+
+clear;clc;
+delta=0.1;
+Z2=0;
+for kx=-pi:0.1:0
+ for ky=-pi:0.1:pi
+ [V1,V2]=get_vector(Hamiltonian(kx,ky));
+ [Vkx1,Vkx2]=get_vector(Hamiltonian(kx+delta,ky)); % 略偏离kx的波函数
+ [Vky1,Vky2]=get_vector(Hamiltonian(kx,ky+delta)); % 略偏离ky的波函数
+ [Vkxky1,Vkxky2]=get_vector(Hamiltonian(kx+delta,ky+delta)); % 略偏离kx，ky的波函数
+
+ Ux = dot_and_det(V1, Vkx1, V2, Vkx2);
+ Uy = dot_and_det(V1, Vky1, V2, Vky2);
+ Ux_y = dot_and_det(Vky1, Vkxky1, Vky2, Vkxky2);
+ Uy_x = dot_and_det(Vkx1, Vkxky1, Vkx2, Vkxky2);
+
+ F=imag(log(Ux*Uy_x*(conj(Ux_y))*(conj(Uy))));
+ A=imag(log(Ux))+imag(log(Uy_x))+imag(log(conj(Ux_y)))+imag(log(conj(Uy)));
+
+ Z2 = Z2+(A-F)/(2*pi);
+ end
+end
+Z2= mod(Z2, 2)
+
+
+function dd = dot_and_det(a1,b1,a2,b2) % 内积组成的矩阵对应的行列式
+x1=a1'*b1;
+x2=a2'*b2;
+x3=a1'*b2;
+x4=a2'*b1;
+dd =x1*x2-x3*x4;
+end
+
+
+function [vector_new_1, vector_new_2] = get_vector(H)
+[vector,eigenvalue] = eig(H);
+[eigenvalue, index]=sort(diag(eigenvalue), 'ascend');
+vector_new_2 = vector(:, index(2));
+vector_new_1 = vector(:, index(1));
+end
+
+
+function H=Hamiltonian(kx,ky) % BHZ模型
+A=0.3645/5;
+B=-0.686/25;
+C=0;
+D=-0.512/25;
+M=-0.01;
+
+H=zeros(4,4);
+varepsilon = C-2*D*(2-cos(kx)-cos(ky));
+d3 = -2*B*(2-(M/2/B)-cos(kx)-cos(ky));
+d1_d2 = A*(sin(kx)+1j*sin(ky));
+H(1, 1) = varepsilon+d3;
+H(2, 2) = varepsilon-d3;
+H(1, 2) = conj(d1_d2);
+H(2, 1) = d1_d2 ;
+
+varepsilon = C-2*D*(2-cos(-kx)-cos(-ky));
+d3 = -2*B*(2-(M/2/B)-cos(-kx)-cos(-ky));
+d1_d2 = A*(sin(-kx)+1j*sin(-ky));
+H(3, 3) = varepsilon+d3;
+H(4, 4) = varepsilon-d3;
+H(3, 4) = d1_d2 ;
+H(4, 3) = conj(d1_d2);
end
\ No newline at end of file
diff --git a/academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.py b/academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.py
rename to academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.py
index 160bf5b..eb3e472
--- a/academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.py
+++ b/academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/Z2_invariant_in_BHZ_model.py
@@ -1,88 +1,88 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5778
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import * # 引入pi, cos等
-import cmath
-import time
-
-
-def hamiltonian(kx, ky): # BHZ模型
- A=0.3645/5
- B=-0.686/25
- C=0
- D=-0.512/25
- M=-0.01
- matrix = np.zeros((4, 4))*(1+0j)
-
- varepsilon = C-2*D*(2-cos(kx)-cos(ky))
- d3 = -2*B*(2-(M/2/B)-cos(kx)-cos(ky))
- d1_d2 = A*(sin(kx)+1j*sin(ky))
- matrix[0, 0] = varepsilon+d3
- matrix[1, 1] = varepsilon-d3
- matrix[0, 1] = np.conj(d1_d2)
- matrix[1, 0] = d1_d2
-
- varepsilon = C-2*D*(2-cos(-kx)-cos(-ky))
- d3 = -2*B*(2-(M/2/B)-cos(-kx)-cos(-ky))
- d1_d2 = A*(sin(-kx)+1j*sin(-ky))
- matrix[2, 2] = varepsilon+d3
- matrix[3, 3] = varepsilon-d3
- matrix[2, 3] = d1_d2
- matrix[3, 2] = np.conj(d1_d2)
- return matrix
-
-
-def main():
- start_clock = time.perf_counter()
- delta = 0.1
- Z2 = 0 # Z2数
- for kx in np.arange(-pi, 0, delta):
- print(kx)
- for ky in np.arange(-pi, pi, delta):
- H = hamiltonian(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数1
- vector2 = eigenvector[:, np.argsort(np.real(eigenvalue))[1]] # 价带波函数2
-
- H_delta_kx = hamiltonian(kx+delta, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数1
- vector_delta_kx2 = eigenvector[:, np.argsort(np.real(eigenvalue))[1]] # 略偏离kx的波函数2
-
- H_delta_ky = hamiltonian(kx, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数1
- vector_delta_ky2 = eigenvector[:, np.argsort(np.real(eigenvalue))[1]] # 略偏离ky的波函数2
-
- H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数1
- vector_delta_kx_ky2 = eigenvector[:, np.argsort(np.real(eigenvalue))[1]] # 略偏离kx和ky的波函数2
-
- Ux = dot_and_det(vector, vector_delta_kx, vector2, vector_delta_kx2)
- Uy = dot_and_det(vector, vector_delta_ky, vector2, vector_delta_ky2)
- Ux_y = dot_and_det(vector_delta_ky, vector_delta_kx_ky, vector_delta_ky2, vector_delta_kx_ky2)
- Uy_x = dot_and_det(vector_delta_kx, vector_delta_kx_ky, vector_delta_kx2, vector_delta_kx_ky2)
-
- F = np.imag(cmath.log(Ux*Uy_x*np.conj(Ux_y)*np.conj(Uy)))
- A = np.imag(cmath.log(Ux))+np.imag(cmath.log(Uy_x))+np.imag(cmath.log(np.conj(Ux_y)))+np.imag(cmath.log(np.conj(Uy)))
- Z2 = Z2 + (A-F)/(2*pi)
- print('Z2 = ', Z2) # Z2数
- end_clock = time.perf_counter()
- print('CPU执行时间(min)=', (end_clock-start_clock)/60)
-
-
-def dot_and_det(a1, b1, a2, b2): # 内积组成的矩阵对应的行列式
- x1 = np.dot(np.conj(a1), b1)
- x2 = np.dot(np.conj(a2), b2)
- x3 = np.dot(np.conj(a1), b2)
- x4 = np.dot(np.conj(a2), b1)
- return x1*x2-x3*x4
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5778
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import * # 引入pi, cos等
+import cmath
+import time
+
+
+def hamiltonian(kx, ky): # BHZ模型
+ A=0.3645/5
+ B=-0.686/25
+ C=0
+ D=-0.512/25
+ M=-0.01
+ matrix = np.zeros((4, 4))*(1+0j)
+
+ varepsilon = C-2*D*(2-cos(kx)-cos(ky))
+ d3 = -2*B*(2-(M/2/B)-cos(kx)-cos(ky))
+ d1_d2 = A*(sin(kx)+1j*sin(ky))
+ matrix[0, 0] = varepsilon+d3
+ matrix[1, 1] = varepsilon-d3
+ matrix[0, 1] = np.conj(d1_d2)
+ matrix[1, 0] = d1_d2
+
+ varepsilon = C-2*D*(2-cos(-kx)-cos(-ky))
+ d3 = -2*B*(2-(M/2/B)-cos(-kx)-cos(-ky))
+ d1_d2 = A*(sin(-kx)+1j*sin(-ky))
+ matrix[2, 2] = varepsilon+d3
+ matrix[3, 3] = varepsilon-d3
+ matrix[2, 3] = d1_d2
+ matrix[3, 2] = np.conj(d1_d2)
+ return matrix
+
+
+def main():
+ start_clock = time.perf_counter()
+ delta = 0.1
+ Z2 = 0 # Z2数
+ for kx in np.arange(-pi, 0, delta):
+ print(kx)
+ for ky in np.arange(-pi, pi, delta):
+ H = hamiltonian(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数1
+ vector2 = eigenvector[:, np.argsort(np.real(eigenvalue))[1]] # 价带波函数2
+
+ H_delta_kx = hamiltonian(kx+delta, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数1
+ vector_delta_kx2 = eigenvector[:, np.argsort(np.real(eigenvalue))[1]] # 略偏离kx的波函数2
+
+ H_delta_ky = hamiltonian(kx, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数1
+ vector_delta_ky2 = eigenvector[:, np.argsort(np.real(eigenvalue))[1]] # 略偏离ky的波函数2
+
+ H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数1
+ vector_delta_kx_ky2 = eigenvector[:, np.argsort(np.real(eigenvalue))[1]] # 略偏离kx和ky的波函数2
+
+ Ux = dot_and_det(vector, vector_delta_kx, vector2, vector_delta_kx2)
+ Uy = dot_and_det(vector, vector_delta_ky, vector2, vector_delta_ky2)
+ Ux_y = dot_and_det(vector_delta_ky, vector_delta_kx_ky, vector_delta_ky2, vector_delta_kx_ky2)
+ Uy_x = dot_and_det(vector_delta_kx, vector_delta_kx_ky, vector_delta_kx2, vector_delta_kx_ky2)
+
+ F = np.imag(cmath.log(Ux*Uy_x*np.conj(Ux_y)*np.conj(Uy)))
+ A = np.imag(cmath.log(Ux))+np.imag(cmath.log(Uy_x))+np.imag(cmath.log(np.conj(Ux_y)))+np.imag(cmath.log(np.conj(Uy)))
+ Z2 = Z2 + (A-F)/(2*pi)
+ print('Z2 = ', Z2) # Z2数
+ end_clock = time.perf_counter()
+ print('CPU执行时间(min)=', (end_clock-start_clock)/60)
+
+
+def dot_and_det(a1, b1, a2, b2): # 内积组成的矩阵对应的行列式
+ x1 = np.dot(np.conj(a1), b1)
+ x2 = np.dot(np.conj(a2), b2)
+ x3 = np.dot(np.conj(a1), b2)
+ x4 = np.dot(np.conj(a2), b1)
+ return x1*x2-x3*x4
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/spin_Chern_number_in_BHZ_model.py b/academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/spin_Chern_number_in_BHZ_model.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/spin_Chern_number_in_BHZ_model.py
rename to academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/spin_Chern_number_in_BHZ_model.py
index a79aa3a..ab46a24
--- a/academic_codes/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/spin_Chern_number_in_BHZ_model.py
+++ b/academic_codes/topological_invariant/2020.09.05_spin_Chern_number_and_Z2_invariant_in_BHZ_model/spin_Chern_number_in_BHZ_model.py
@@ -1,98 +1,98 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5778
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import * # 引入pi, cos等
-import cmath
-import time
-
-
-def hamiltonian1(kx, ky): # Half BHZ for spin up
- A=0.3645/5
- B=-0.686/25
- C=0
- D=-0.512/25
- M=-0.01
- matrix = np.zeros((2, 2))*(1+0j)
- varepsilon = C-2*D*(2-cos(kx)-cos(ky))
- d3 = -2*B*(2-(M/2/B)-cos(kx)-cos(ky))
- d1_d2 = A*(sin(kx)+1j*sin(ky))
-
- matrix[0, 0] = varepsilon+d3
- matrix[1, 1] = varepsilon-d3
- matrix[0, 1] = np.conj(d1_d2)
- matrix[1, 0] = d1_d2
- return matrix
-
-def hamiltonian2(kx, ky): # Half BHZ for spin down
- A=0.3645/5
- B=-0.686/25
- C=0
- D=-0.512/25
- M=-0.01
- matrix = np.zeros((2, 2))*(1+0j)
- varepsilon = C-2*D*(2-cos(-kx)-cos(-ky))
- d3 = -2*B*(2-(M/2/B)-cos(-kx)-cos(-ky))
- d1_d2 = A*(sin(-kx)+1j*sin(-ky))
-
- matrix[0, 0] = varepsilon+d3
- matrix[1, 1] = varepsilon-d3
- matrix[0, 1] = d1_d2
- matrix[1, 0] = np.conj(d1_d2)
- return matrix
-
-
-def main():
- start_clock = time.perf_counter()
- delta = 0.1
- for i0 in range(2):
- if i0 == 0:
- hamiltonian = hamiltonian1
- else:
- hamiltonian = hamiltonian2
- chern_number = 0 # 陈数初始化
- for kx in np.arange(-pi, pi, delta):
- print(kx)
- for ky in np.arange(-pi, pi, delta):
- H = hamiltonian(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
-
- H_delta_kx = hamiltonian(kx+delta, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
-
- H_delta_ky = hamiltonian(kx, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
-
- H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
-
- Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
- Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
- Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
- Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
-
- F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
- # 陈数(chern number)
- chern_number = chern_number + F
- chern_number = chern_number/(2*pi*1j)
- if i0 == 0:
- chern_number_up = chern_number
- else:
- chern_number_down = chern_number
- spin_chern_number = (chern_number_up-chern_number_down)/2
- print('Chern number for spin up = ', chern_number_up)
- print('Chern number for spin down = ', chern_number_down)
- print('Spin chern number = ', spin_chern_number)
- end_clock = time.perf_counter()
- print('CPU执行时间(min)=', (end_clock-start_clock)/60)
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/5778
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import * # 引入pi, cos等
+import cmath
+import time
+
+
+def hamiltonian1(kx, ky): # Half BHZ for spin up
+ A=0.3645/5
+ B=-0.686/25
+ C=0
+ D=-0.512/25
+ M=-0.01
+ matrix = np.zeros((2, 2))*(1+0j)
+ varepsilon = C-2*D*(2-cos(kx)-cos(ky))
+ d3 = -2*B*(2-(M/2/B)-cos(kx)-cos(ky))
+ d1_d2 = A*(sin(kx)+1j*sin(ky))
+
+ matrix[0, 0] = varepsilon+d3
+ matrix[1, 1] = varepsilon-d3
+ matrix[0, 1] = np.conj(d1_d2)
+ matrix[1, 0] = d1_d2
+ return matrix
+
+def hamiltonian2(kx, ky): # Half BHZ for spin down
+ A=0.3645/5
+ B=-0.686/25
+ C=0
+ D=-0.512/25
+ M=-0.01
+ matrix = np.zeros((2, 2))*(1+0j)
+ varepsilon = C-2*D*(2-cos(-kx)-cos(-ky))
+ d3 = -2*B*(2-(M/2/B)-cos(-kx)-cos(-ky))
+ d1_d2 = A*(sin(-kx)+1j*sin(-ky))
+
+ matrix[0, 0] = varepsilon+d3
+ matrix[1, 1] = varepsilon-d3
+ matrix[0, 1] = d1_d2
+ matrix[1, 0] = np.conj(d1_d2)
+ return matrix
+
+
+def main():
+ start_clock = time.perf_counter()
+ delta = 0.1
+ for i0 in range(2):
+ if i0 == 0:
+ hamiltonian = hamiltonian1
+ else:
+ hamiltonian = hamiltonian2
+ chern_number = 0 # 陈数初始化
+ for kx in np.arange(-pi, pi, delta):
+ print(kx)
+ for ky in np.arange(-pi, pi, delta):
+ H = hamiltonian(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
+
+ H_delta_kx = hamiltonian(kx+delta, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
+
+ H_delta_ky = hamiltonian(kx, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
+
+ H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
+
+ Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
+ Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
+ Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
+ Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
+
+ F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
+ # 陈数(chern number)
+ chern_number = chern_number + F
+ chern_number = chern_number/(2*pi*1j)
+ if i0 == 0:
+ chern_number_up = chern_number
+ else:
+ chern_number_down = chern_number
+ spin_chern_number = (chern_number_up-chern_number_down)/2
+ print('Chern number for spin up = ', chern_number_up)
+ print('Chern number for spin down = ', chern_number_down)
+ print('Spin chern number = ', spin_chern_number)
+ end_clock = time.perf_counter()
+ print('CPU执行时间(min)=', (end_clock-start_clock)/60)
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals.py b/academic_codes/topological_invariant/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals.py
old mode 100755
new mode 100644
similarity index 97%
rename from academic_codes/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals.py
rename to academic_codes/topological_invariant/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals.py
index 20e470a..69786d3
--- a/academic_codes/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals.py
+++ b/academic_codes/topological_invariant/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals.py
@@ -1,83 +1,83 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6896
-"""
-
-import numpy as np
-from math import *
-import matplotlib.pyplot as plt
-import time
-import cmath
-
-
-def hamiltonian(kx,ky,kz): # Weyl semimetal
- A = 1
- M0 = 1
- M1 = 1
- H = A*(sin(kx)*sigma_x()+sin(ky)*sigma_y())+(M0-M1*(2*(1-cos(kx))+2*(1-cos(ky))+2*(1-cos(kz))))*sigma_z()
- return H
-
-
-def sigma_x():
- return np.array([[0, 1],[1, 0]])
-
-
-def sigma_y():
- return np.array([[0, -1j],[1j, 0]])
-
-
-def sigma_z():
- return np.array([[1, 0],[0, -1]])
-
-
-def main():
- start_time = time.time()
- n = 50
- delta = 2*pi/n
- kz_array = np.arange(-pi, pi, 0.1)
- chern_number_array = np.zeros(kz_array.shape[0])
- i0 = 0
- for kz in kz_array:
- print('kz=', kz)
- chern_number = 0 # 陈数初始化
- for kx in np.arange(-pi, pi, 2*pi/n):
- for ky in np.arange(-pi, pi, 2*pi/n):
- H = hamiltonian(kx, ky, kz)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
-
- H_delta_kx = hamiltonian(kx+delta, ky, kz)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
-
- H_delta_ky = hamiltonian(kx, ky+delta, kz)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
-
- H_delta_kx_ky = hamiltonian(kx+delta, ky+delta, kz)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
-
- Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
- Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
- Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
- Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
-
- F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
-
- # 陈数(chern number)
- chern_number = chern_number + F
- chern_number = chern_number/(2*pi*1j)
- print('Chern number = ', chern_number, '\n')
- chern_number_array[i0] = np.real(chern_number)
- i0 += 1
- end_time = time.time()
- print('运行时间(min)=', (end_time-start_time)/60)
- plt.plot(kz_array, chern_number_array, 'o-')
- plt.xlabel('kz')
- plt.ylabel('Chern number')
- plt.show()
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6896
+"""
+
+import numpy as np
+from math import *
+import matplotlib.pyplot as plt
+import time
+import cmath
+
+
+def hamiltonian(kx,ky,kz): # Weyl semimetal
+ A = 1
+ M0 = 1
+ M1 = 1
+ H = A*(sin(kx)*sigma_x()+sin(ky)*sigma_y())+(M0-M1*(2*(1-cos(kx))+2*(1-cos(ky))+2*(1-cos(kz))))*sigma_z()
+ return H
+
+
+def sigma_x():
+ return np.array([[0, 1],[1, 0]])
+
+
+def sigma_y():
+ return np.array([[0, -1j],[1j, 0]])
+
+
+def sigma_z():
+ return np.array([[1, 0],[0, -1]])
+
+
+def main():
+ start_time = time.time()
+ n = 50
+ delta = 2*pi/n
+ kz_array = np.arange(-pi, pi, 0.1)
+ chern_number_array = np.zeros(kz_array.shape[0])
+ i0 = 0
+ for kz in kz_array:
+ print('kz=', kz)
+ chern_number = 0 # 陈数初始化
+ for kx in np.arange(-pi, pi, 2*pi/n):
+ for ky in np.arange(-pi, pi, 2*pi/n):
+ H = hamiltonian(kx, ky, kz)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 价带波函数
+
+ H_delta_kx = hamiltonian(kx+delta, ky, kz)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx的波函数
+
+ H_delta_ky = hamiltonian(kx, ky+delta, kz)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离ky的波函数
+
+ H_delta_kx_ky = hamiltonian(kx+delta, ky+delta, kz)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[0]] # 略偏离kx和ky的波函数
+
+ Ux = np.dot(np.conj(vector), vector_delta_kx)/abs(np.dot(np.conj(vector), vector_delta_kx))
+ Uy = np.dot(np.conj(vector), vector_delta_ky)/abs(np.dot(np.conj(vector), vector_delta_ky))
+ Ux_y = np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_ky), vector_delta_kx_ky))
+ Uy_x = np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky)/abs(np.dot(np.conj(vector_delta_kx), vector_delta_kx_ky))
+
+ F = cmath.log(Ux*Uy_x*(1/Ux_y)*(1/Uy))
+
+ # 陈数(chern number)
+ chern_number = chern_number + F
+ chern_number = chern_number/(2*pi*1j)
+ print('Chern number = ', chern_number, '\n')
+ chern_number_array[i0] = np.real(chern_number)
+ i0 += 1
+ end_time = time.time()
+ print('运行时间(min)=', (end_time-start_time)/60)
+ plt.plot(kz_array, chern_number_array, 'o-')
+ plt.xlabel('kz')
+ plt.ylabel('Chern number')
+ plt.show()
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals_with_guan_package.py b/academic_codes/topological_invariant/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals_with_guan_package.py
similarity index 100%
rename from academic_codes/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals_with_guan_package.py
rename to academic_codes/topological_invariant/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/Chern_number_of_the_cross_section_plane_in_Weyl_semimetals_with_guan_package.py
diff --git a/academic_codes/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/bands_of_Weyl_semimetal_after_discretization.py b/academic_codes/topological_invariant/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/bands_of_Weyl_semimetal_after_discretization.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/bands_of_Weyl_semimetal_after_discretization.py
rename to academic_codes/topological_invariant/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/bands_of_Weyl_semimetal_after_discretization.py
index ddd31bf..bee7569
--- a/academic_codes/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/bands_of_Weyl_semimetal_after_discretization.py
+++ b/academic_codes/topological_invariant/2020.11.04_Chern_number_of_the_cross_section_plane_in_Weyl_semimetals/bands_of_Weyl_semimetal_after_discretization.py
@@ -1,66 +1,66 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6896
-"""
-
-import numpy as np
-from math import *
-import matplotlib.pyplot as plt
-
-
-def main():
- k1 = np.arange(-pi, pi, 0.05)
- k2 = np.arange(-pi, pi, 0.05)
- plot_bands_two_dimension(k1, k2, hamiltonian)
-
-
-def hamiltonian(kx,kz,ky=0): # Weyl semimetal
- A = 1
- M0 = 1
- M1 = 1
- H = A*(sin(kx)*sigma_x()+sin(ky)*sigma_y())+(M0-M1*(2*(1-cos(kx))+2*(1-cos(ky))+2*(1-cos(kz))))*sigma_z()
- return H
-
-
-def sigma_x():
- return np.array([[0, 1],[1, 0]])
-
-
-def sigma_y():
- return np.array([[0, -1j],[1j, 0]])
-
-
-def sigma_z():
- return np.array([[1, 0],[0, -1]])
-
-
-def plot_bands_two_dimension(k1, k2, hamiltonian):
- from mpl_toolkits.mplot3d import Axes3D
- from matplotlib import cm
- from matplotlib.ticker import LinearLocator, FormatStrFormatter
- dim = hamiltonian(0, 0).shape[0]
- dim1 = k1.shape[0]
- dim2 = k2.shape[0]
- eigenvalue_k = np.zeros((dim2, dim1, dim))
- i0 = 0
- for k10 in k1:
- j0 = 0
- for k20 in k2:
- matrix0 = hamiltonian(k10, k20)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- eigenvalue_k[j0, i0, :] = np.sort(np.real(eigenvalue[:]))
- j0 += 1
- i0 += 1
- fig = plt.figure()
- ax = fig.gca(projection='3d')
- k1, k2 = np.meshgrid(k1, k2)
- for dim0 in range(dim):
- ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
- plt.xlabel('kx')
- plt.ylabel('kz')
- ax.set_zlabel('E')
- plt.show()
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/6896
+"""
+
+import numpy as np
+from math import *
+import matplotlib.pyplot as plt
+
+
+def main():
+ k1 = np.arange(-pi, pi, 0.05)
+ k2 = np.arange(-pi, pi, 0.05)
+ plot_bands_two_dimension(k1, k2, hamiltonian)
+
+
+def hamiltonian(kx,kz,ky=0): # Weyl semimetal
+ A = 1
+ M0 = 1
+ M1 = 1
+ H = A*(sin(kx)*sigma_x()+sin(ky)*sigma_y())+(M0-M1*(2*(1-cos(kx))+2*(1-cos(ky))+2*(1-cos(kz))))*sigma_z()
+ return H
+
+
+def sigma_x():
+ return np.array([[0, 1],[1, 0]])
+
+
+def sigma_y():
+ return np.array([[0, -1j],[1j, 0]])
+
+
+def sigma_z():
+ return np.array([[1, 0],[0, -1]])
+
+
+def plot_bands_two_dimension(k1, k2, hamiltonian):
+ from mpl_toolkits.mplot3d import Axes3D
+ from matplotlib import cm
+ from matplotlib.ticker import LinearLocator, FormatStrFormatter
+ dim = hamiltonian(0, 0).shape[0]
+ dim1 = k1.shape[0]
+ dim2 = k2.shape[0]
+ eigenvalue_k = np.zeros((dim2, dim1, dim))
+ i0 = 0
+ for k10 in k1:
+ j0 = 0
+ for k20 in k2:
+ matrix0 = hamiltonian(k10, k20)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ eigenvalue_k[j0, i0, :] = np.sort(np.real(eigenvalue[:]))
+ j0 += 1
+ i0 += 1
+ fig = plt.figure()
+ ax = fig.gca(projection='3d')
+ k1, k2 = np.meshgrid(k1, k2)
+ for dim0 in range(dim):
+ ax.plot_surface(k1, k2, eigenvalue_k[:, :, dim0], cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ plt.xlabel('kx')
+ plt.ylabel('kz')
+ ax.set_zlabel('E')
+ plt.show()
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2020.11.27_find_the_same_gauge_numerically_with_binary_search/find_the_same_gauge_numerically_with_binary_search.py b/academic_codes/topological_invariant/2020.11.27_find_the_same_gauge_numerically_with_binary_search/find_the_same_gauge_numerically_with_binary_search.py
similarity index 100%
rename from academic_codes/2020.11.27_find_the_same_gauge_numerically_with_binary_search/find_the_same_gauge_numerically_with_binary_search.py
rename to academic_codes/topological_invariant/2020.11.27_find_the_same_gauge_numerically_with_binary_search/find_the_same_gauge_numerically_with_binary_search.py
diff --git a/academic_codes/2020.11.27_find_the_same_gauge_numerically_with_binary_search/find_the_same_gauge_numerically_with_guan.py b/academic_codes/topological_invariant/2020.11.27_find_the_same_gauge_numerically_with_binary_search/find_the_same_gauge_numerically_with_guan.py
similarity index 100%
rename from academic_codes/2020.11.27_find_the_same_gauge_numerically_with_binary_search/find_the_same_gauge_numerically_with_guan.py
rename to academic_codes/topological_invariant/2020.11.27_find_the_same_gauge_numerically_with_binary_search/find_the_same_gauge_numerically_with_guan.py
diff --git a/academic_codes/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery.py b/academic_codes/topological_invariant/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery.py
old mode 100755
new mode 100644
similarity index 98%
rename from academic_codes/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery.py
rename to academic_codes/topological_invariant/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery.py
index 7c8ac2a..b80179b
--- a/academic_codes/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery.py
+++ b/academic_codes/topological_invariant/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery.py
@@ -1,159 +1,159 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8536
-"""
-
-import numpy as np
-import matplotlib.pyplot as plt
-from math import *
-import cmath
-import time
-
-
-def hamiltonian(k1, k2, t1=2.82, a=1/sqrt(3)): # 石墨烯哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
- h = np.zeros((2, 2), dtype=complex)
- h[0, 0] = 0.28/2
- h[1, 1] = -0.28/2
- h[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
- h[0, 1] = h[1, 0].conj()
- return h
-
-
-def main():
- start_time = time.time()
- n = 400 # 取点密度
- delta = 1e-10 # 求导的偏离量
- kx_array = np.linspace(-2*pi, 2*pi, n)
- ky_array = np.linspace(-2*pi, 2*pi, n)
- for band in range(2):
- F_all = np.zeros((ky_array.shape[0], ky_array.shape[0])) # 贝里曲率
- j0 = 0
- for kx in kx_array:
- print(kx)
- i0 = 0
- for ky in ky_array:
- H = hamiltonian(kx, ky)
- eigenvalue, eigenvector = np.linalg.eig(H)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[band]] # 价带波函数
- # print(np.argsort(np.real(eigenvalue))[0]) # 排序索引（从小到大）
- # print(eigenvalue) # 排序前的本征值
- # print(np.sort(np.real(eigenvalue))) # 排序后的本征值（从小到大）
-
- H_delta_kx = hamiltonian(kx+delta, ky)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
- vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[band]] # 略偏离kx的波函数
- # vector_delta_kx = find_vector_with_the_same_gauge(vector_delta_kx, vector) # 如果波函数不连续需要使用这个
-
- H_delta_ky = hamiltonian(kx, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
- vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[band]] # 略偏离ky的波函数
- # vector_delta_ky = find_vector_with_the_same_gauge(vector_delta_ky, vector) # 如果波函数不连续需要使用这个
-
- H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
- eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
- vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[band]] # 略偏离kx和ky的波函数
- # vector_delta_kx_ky = find_vector_with_the_same_gauge(vector_delta_kx_ky, vector) # 如果波函数不连续需要使用这个
-
- # 价带的波函数的贝里联络(berry connection) # 求导后内积
- A_x = np.dot(vector.transpose().conj(), (vector_delta_kx-vector)/delta) # 贝里联络Ax（x分量）
- A_y = np.dot(vector.transpose().conj(), (vector_delta_ky-vector)/delta) # 贝里联络Ay（y分量）
-
- A_x_delta_ky = np.dot(vector_delta_ky.transpose().conj(), (vector_delta_kx_ky-vector_delta_ky)/delta) # 略偏离ky的贝里联络Ax
- A_y_delta_kx = np.dot(vector_delta_kx.transpose().conj(), (vector_delta_kx_ky-vector_delta_kx)/delta) # 略偏离kx的贝里联络Ay
-
- # 贝里曲率(berry curvature)
- F = ((A_y_delta_kx-A_y)/delta-(A_x_delta_ky-A_x)/delta)*1j
- # print(F)
- F_all[i0, j0] = np.real(F)
- i0 += 1
- j0 += 1
-
- if band==0:
- plot_3d_surface(kx_array/pi, ky_array/pi, F_all, xlabel='k_x (pi)', ylabel='k_y (pi)', zlabel='Berry curvature', title='Valence Band', rcount=300, ccount=300)
- else:
- plot_3d_surface(kx_array/pi, ky_array/pi, F_all, xlabel='k_x (pi)', ylabel='k_y (pi)', zlabel='Berry curvature', title='Conductance Band', rcount=300, ccount=300)
- # import guan
- # if band==0:
- # guan.plot_3d_surface(kx_array/pi, ky_array/pi, F_all, xlabel='kx', ylabel='ky', zlabel='Berry curvature', title='Valence Band', rcount=300, ccount=300)
- # else:
- # guan.plot_3d_surface(kx_array/pi, ky_array/pi, F_all, xlabel='kx', ylabel='ky', zlabel='Berry curvature', title='Conductance Band', rcount=300, ccount=300)
- end_time = time.time()
- print('运行时间(min)=', (end_time-start_time)/60)
-
-def find_vector_with_the_same_gauge(vector_1, vector_0):
- # 寻找近似的同一的规范
- phase_1_pre = 0
- phase_2_pre = pi
- n_test = 10001
- for i0 in range(n_test):
- test_1 = np.sum(np.abs(vector_1*cmath.exp(1j*phase_1_pre) - vector_0))
- test_2 = np.sum(np.abs(vector_1*cmath.exp(1j*phase_2_pre) - vector_0))
- if test_1 < 1e-9:
- phase = phase_1_pre
- # print('Done with i0=', i0)
- break
- if i0 == n_test-1:
- phase = phase_1_pre
- print('Gauge Not Found with i0=', i0)
- if test_1 < test_2:
- if i0 == 0:
- phase_1 = phase_1_pre-(phase_2_pre-phase_1_pre)/2
- phase_2 = phase_1_pre+(phase_2_pre-phase_1_pre)/2
- else:
- phase_1 = phase_1_pre
- phase_2 = phase_1_pre+(phase_2_pre-phase_1_pre)/2
- else:
- if i0 == 0:
- phase_1 = phase_2_pre-(phase_2_pre-phase_1_pre)/2
- phase_2 = phase_2_pre+(phase_2_pre-phase_1_pre)/2
- else:
- phase_1 = phase_2_pre-(phase_2_pre-phase_1_pre)/2
- phase_2 = phase_2_pre
- phase_1_pre = phase_1
- phase_2_pre = phase_2
-
- vector_1 = vector_1*cmath.exp(1j*phase)
- # print('二分查找找到的规范=', phase)
- return vector_1
-
-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 matplotlib.pyplot as plt
- from matplotlib import cm
- from matplotlib.ticker import LinearLocator
- matrix = np.array(matrix)
- fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
- plt.subplots_adjust(bottom=0.1, right=0.65)
- x_array, y_array = np.meshgrid(x_array, y_array)
- if len(matrix.shape) == 2:
- surf = ax.plot_surface(x_array, y_array, matrix, rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
- elif len(matrix.shape) == 3:
- for i0 in range(matrix.shape[2]):
- surf = ax.plot_surface(x_array, y_array, matrix[:,:,i0], rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
- ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
- ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
- ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
- ax.set_zlabel(zlabel, fontsize=fontsize, fontfamily='Times New Roman')
- ax.zaxis.set_major_locator(LinearLocator(5))
- ax.zaxis.set_major_formatter('{x:.2f}')
- if z_min!=None or z_max!=None:
- if z_min==None:
- z_min=matrix.min()
- if z_max==None:
- z_max=matrix.max()
- ax.set_zlim(z_min, z_max)
- ax.tick_params(labelsize=labelsize)
- labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
- [label.set_fontname('Times New Roman') for label in labels]
- cax = plt.axes([0.8, 0.1, 0.05, 0.8])
- cbar = fig.colorbar(surf, cax=cax)
- cbar.ax.tick_params(labelsize=labelsize)
- for l in cbar.ax.yaxis.get_ticklabels():
- l.set_family('Times New Roman')
- if save == 1:
- plt.savefig(filename+file_format, dpi=dpi)
- if show == 1:
- plt.show()
- plt.close('all')
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/8536
+"""
+
+import numpy as np
+import matplotlib.pyplot as plt
+from math import *
+import cmath
+import time
+
+
+def hamiltonian(k1, k2, t1=2.82, a=1/sqrt(3)): # 石墨烯哈密顿量（a为原子间距，不赋值的话默认为1/sqrt(3)）
+ h = np.zeros((2, 2), dtype=complex)
+ h[0, 0] = 0.28/2
+ h[1, 1] = -0.28/2
+ h[1, 0] = t1*(cmath.exp(1j*k2*a)+cmath.exp(1j*sqrt(3)/2*k1*a-1j/2*k2*a)+cmath.exp(-1j*sqrt(3)/2*k1*a-1j/2*k2*a))
+ h[0, 1] = h[1, 0].conj()
+ return h
+
+
+def main():
+ start_time = time.time()
+ n = 400 # 取点密度
+ delta = 1e-10 # 求导的偏离量
+ kx_array = np.linspace(-2*pi, 2*pi, n)
+ ky_array = np.linspace(-2*pi, 2*pi, n)
+ for band in range(2):
+ F_all = np.zeros((ky_array.shape[0], ky_array.shape[0])) # 贝里曲率
+ j0 = 0
+ for kx in kx_array:
+ print(kx)
+ i0 = 0
+ for ky in ky_array:
+ H = hamiltonian(kx, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[band]] # 价带波函数
+ # print(np.argsort(np.real(eigenvalue))[0]) # 排序索引（从小到大）
+ # print(eigenvalue) # 排序前的本征值
+ # print(np.sort(np.real(eigenvalue))) # 排序后的本征值（从小到大）
+
+ H_delta_kx = hamiltonian(kx+delta, ky)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx)
+ vector_delta_kx = eigenvector[:, np.argsort(np.real(eigenvalue))[band]] # 略偏离kx的波函数
+ # vector_delta_kx = find_vector_with_the_same_gauge(vector_delta_kx, vector) # 如果波函数不连续需要使用这个
+
+ H_delta_ky = hamiltonian(kx, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_ky)
+ vector_delta_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[band]] # 略偏离ky的波函数
+ # vector_delta_ky = find_vector_with_the_same_gauge(vector_delta_ky, vector) # 如果波函数不连续需要使用这个
+
+ H_delta_kx_ky = hamiltonian(kx+delta, ky+delta)
+ eigenvalue, eigenvector = np.linalg.eig(H_delta_kx_ky)
+ vector_delta_kx_ky = eigenvector[:, np.argsort(np.real(eigenvalue))[band]] # 略偏离kx和ky的波函数
+ # vector_delta_kx_ky = find_vector_with_the_same_gauge(vector_delta_kx_ky, vector) # 如果波函数不连续需要使用这个
+
+ # 价带的波函数的贝里联络(berry connection) # 求导后内积
+ A_x = np.dot(vector.transpose().conj(), (vector_delta_kx-vector)/delta) # 贝里联络Ax（x分量）
+ A_y = np.dot(vector.transpose().conj(), (vector_delta_ky-vector)/delta) # 贝里联络Ay（y分量）
+
+ A_x_delta_ky = np.dot(vector_delta_ky.transpose().conj(), (vector_delta_kx_ky-vector_delta_ky)/delta) # 略偏离ky的贝里联络Ax
+ A_y_delta_kx = np.dot(vector_delta_kx.transpose().conj(), (vector_delta_kx_ky-vector_delta_kx)/delta) # 略偏离kx的贝里联络Ay
+
+ # 贝里曲率(berry curvature)
+ F = ((A_y_delta_kx-A_y)/delta-(A_x_delta_ky-A_x)/delta)*1j
+ # print(F)
+ F_all[i0, j0] = np.real(F)
+ i0 += 1
+ j0 += 1
+
+ if band==0:
+ plot_3d_surface(kx_array/pi, ky_array/pi, F_all, xlabel='k_x (pi)', ylabel='k_y (pi)', zlabel='Berry curvature', title='Valence Band', rcount=300, ccount=300)
+ else:
+ plot_3d_surface(kx_array/pi, ky_array/pi, F_all, xlabel='k_x (pi)', ylabel='k_y (pi)', zlabel='Berry curvature', title='Conductance Band', rcount=300, ccount=300)
+ # import guan
+ # if band==0:
+ # guan.plot_3d_surface(kx_array/pi, ky_array/pi, F_all, xlabel='kx', ylabel='ky', zlabel='Berry curvature', title='Valence Band', rcount=300, ccount=300)
+ # else:
+ # guan.plot_3d_surface(kx_array/pi, ky_array/pi, F_all, xlabel='kx', ylabel='ky', zlabel='Berry curvature', title='Conductance Band', rcount=300, ccount=300)
+ end_time = time.time()
+ print('运行时间(min)=', (end_time-start_time)/60)
+
+def find_vector_with_the_same_gauge(vector_1, vector_0):
+ # 寻找近似的同一的规范
+ phase_1_pre = 0
+ phase_2_pre = pi
+ n_test = 10001
+ for i0 in range(n_test):
+ test_1 = np.sum(np.abs(vector_1*cmath.exp(1j*phase_1_pre) - vector_0))
+ test_2 = np.sum(np.abs(vector_1*cmath.exp(1j*phase_2_pre) - vector_0))
+ if test_1 < 1e-9:
+ phase = phase_1_pre
+ # print('Done with i0=', i0)
+ break
+ if i0 == n_test-1:
+ phase = phase_1_pre
+ print('Gauge Not Found with i0=', i0)
+ if test_1 < test_2:
+ if i0 == 0:
+ phase_1 = phase_1_pre-(phase_2_pre-phase_1_pre)/2
+ phase_2 = phase_1_pre+(phase_2_pre-phase_1_pre)/2
+ else:
+ phase_1 = phase_1_pre
+ phase_2 = phase_1_pre+(phase_2_pre-phase_1_pre)/2
+ else:
+ if i0 == 0:
+ phase_1 = phase_2_pre-(phase_2_pre-phase_1_pre)/2
+ phase_2 = phase_2_pre+(phase_2_pre-phase_1_pre)/2
+ else:
+ phase_1 = phase_2_pre-(phase_2_pre-phase_1_pre)/2
+ phase_2 = phase_2_pre
+ phase_1_pre = phase_1
+ phase_2_pre = phase_2
+
+ vector_1 = vector_1*cmath.exp(1j*phase)
+ # print('二分查找找到的规范=', phase)
+ return vector_1
+
+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 matplotlib.pyplot as plt
+ from matplotlib import cm
+ from matplotlib.ticker import LinearLocator
+ matrix = np.array(matrix)
+ fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
+ plt.subplots_adjust(bottom=0.1, right=0.65)
+ x_array, y_array = np.meshgrid(x_array, y_array)
+ if len(matrix.shape) == 2:
+ surf = ax.plot_surface(x_array, y_array, matrix, rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ elif len(matrix.shape) == 3:
+ for i0 in range(matrix.shape[2]):
+ surf = ax.plot_surface(x_array, y_array, matrix[:,:,i0], rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
+ ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
+ ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
+ ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
+ ax.set_zlabel(zlabel, fontsize=fontsize, fontfamily='Times New Roman')
+ ax.zaxis.set_major_locator(LinearLocator(5))
+ ax.zaxis.set_major_formatter('{x:.2f}')
+ if z_min!=None or z_max!=None:
+ if z_min==None:
+ z_min=matrix.min()
+ if z_max==None:
+ z_max=matrix.max()
+ ax.set_zlim(z_min, z_max)
+ ax.tick_params(labelsize=labelsize)
+ labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
+ [label.set_fontname('Times New Roman') for label in labels]
+ cax = plt.axes([0.8, 0.1, 0.05, 0.8])
+ cbar = fig.colorbar(surf, cax=cax)
+ cbar.ax.tick_params(labelsize=labelsize)
+ for l in cbar.ax.yaxis.get_ticklabels():
+ l.set_family('Times New Roman')
+ if save == 1:
+ plt.savefig(filename+file_format, dpi=dpi)
+ if show == 1:
+ plt.show()
+ plt.close('all')
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_with_ky=0.py b/academic_codes/topological_invariant/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_with_ky=0.py
similarity index 100%
rename from academic_codes/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_with_ky=0.py
rename to academic_codes/topological_invariant/2021.01.10_Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery/Berry_curvature_distribution_with_ky=0.py
diff --git a/academic_codes/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model.py b/academic_codes/topological_invariant/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model.py
rename to academic_codes/topological_invariant/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model.py
index 0177fbc..64afdc6
--- a/academic_codes/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model.py
+++ b/academic_codes/topological_invariant/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model.py
@@ -1,77 +1,77 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10064
-"""
-
-import numpy as np
-import cmath
-from math import *
-
-
-def hamiltonian(k): # SSH模型哈密顿量
- gamma = 0.5
- lambda0 = 1
- h = np.zeros((2, 2), dtype=complex)
- h[0,0] = 0
- h[1,1] = 0
- h[0,1] = gamma+lambda0*cmath.exp(-1j*k)
- h[1,0] = gamma+lambda0*cmath.exp(1j*k)
- return h
-
-
-def main():
- Num_k = 100
- k_array = np.linspace(-pi, pi, Num_k)
- vector_array = []
- for k in k_array:
- vector = get_occupied_bands_vectors(k, hamiltonian)
- vector_array.append(vector)
- # vector_array.append(vector*cmath.exp(1j*np.random.uniform(0, pi))) # 随机相位测试
-
- # 波函数固定一个规范
- vector_sum = 0
- for i0 in range(Num_k):
- vector_sum += np.abs(vector_array[i0])
- index = np.argmax(np.abs(vector_sum))
- for i0 in range(Num_k):
- vector_array[i0] = find_vector_with_fixed_gauge_by_making_one_component_real(vector_array[i0], index=index)
-
- # # 波函数固定一个规范
- # import guan
- # vector_array = guan.find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array, precision=0.005)
-
- # 计算Wilson loop
- W_k = 1
- for i0 in range(Num_k-1):
- F = np.dot(vector_array[i0+1].transpose().conj(), vector_array[i0])
- W_k = np.dot(F, W_k)
- nu = np.log(W_k)/2/pi/1j
- # if np.real(nu) < 0:
- # nu += 1
- print('p=', nu, '\n')
-
-
-def get_occupied_bands_vectors(x, matrix):
- matrix0 = matrix(x)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]]
- return vector
-
-
-def find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=0.005, index=None):
- if index == None:
- index = np.argmax(np.abs(vector))
- sign_pre = np.sign(np.imag(vector[index]))
- for phase in np.arange(0, 2*np.pi, precision):
- sign = np.sign(np.imag(vector[index]*cmath.exp(1j*phase)))
- if np.abs(np.imag(vector[index]*cmath.exp(1j*phase))) < 1e-9 or sign == -sign_pre:
- break
- sign_pre = sign
- vector = vector*cmath.exp(1j*phase)
- if np.real(vector[index]) < 0:
- vector = -vector
- return vector
-
-
-if __name__ == '__main__':
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10064
+"""
+
+import numpy as np
+import cmath
+from math import *
+
+
+def hamiltonian(k): # SSH模型哈密顿量
+ gamma = 0.5
+ lambda0 = 1
+ h = np.zeros((2, 2), dtype=complex)
+ h[0,0] = 0
+ h[1,1] = 0
+ h[0,1] = gamma+lambda0*cmath.exp(-1j*k)
+ h[1,0] = gamma+lambda0*cmath.exp(1j*k)
+ return h
+
+
+def main():
+ Num_k = 100
+ k_array = np.linspace(-pi, pi, Num_k)
+ vector_array = []
+ for k in k_array:
+ vector = get_occupied_bands_vectors(k, hamiltonian)
+ vector_array.append(vector)
+ # vector_array.append(vector*cmath.exp(1j*np.random.uniform(0, pi))) # 随机相位测试
+
+ # 波函数固定一个规范
+ vector_sum = 0
+ for i0 in range(Num_k):
+ vector_sum += np.abs(vector_array[i0])
+ index = np.argmax(np.abs(vector_sum))
+ for i0 in range(Num_k):
+ vector_array[i0] = find_vector_with_fixed_gauge_by_making_one_component_real(vector_array[i0], index=index)
+
+ # # 波函数固定一个规范
+ # import guan
+ # vector_array = guan.find_vector_array_with_fixed_gauge_by_making_one_component_real(vector_array, precision=0.005)
+
+ # 计算Wilson loop
+ W_k = 1
+ for i0 in range(Num_k-1):
+ F = np.dot(vector_array[i0+1].transpose().conj(), vector_array[i0])
+ W_k = np.dot(F, W_k)
+ nu = np.log(W_k)/2/pi/1j
+ # if np.real(nu) < 0:
+ # nu += 1
+ print('p=', nu, '\n')
+
+
+def get_occupied_bands_vectors(x, matrix):
+ matrix0 = matrix(x)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]]
+ return vector
+
+
+def find_vector_with_fixed_gauge_by_making_one_component_real(vector, precision=0.005, index=None):
+ if index == None:
+ index = np.argmax(np.abs(vector))
+ sign_pre = np.sign(np.imag(vector[index]))
+ for phase in np.arange(0, 2*np.pi, precision):
+ sign = np.sign(np.imag(vector[index]*cmath.exp(1j*phase)))
+ if np.abs(np.imag(vector[index]*cmath.exp(1j*phase))) < 1e-9 or sign == -sign_pre:
+ break
+ sign_pre = sign
+ vector = vector*cmath.exp(1j*phase)
+ if np.real(vector[index]) < 0:
+ vector = -vector
+ return vector
+
+
+if __name__ == '__main__':
main()
\ No newline at end of file
diff --git a/academic_codes/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model_with_better_code.py b/academic_codes/topological_invariant/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model_with_better_code.py
old mode 100755
new mode 100644
similarity index 96%
rename from academic_codes/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model_with_better_code.py
rename to academic_codes/topological_invariant/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model_with_better_code.py
index adfc149..c840f01
--- a/academic_codes/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model_with_better_code.py
+++ b/academic_codes/topological_invariant/2021.03.01_Wilson_loop_in_SSH_model/Wilson_loop_in_SSH_model_with_better_code.py
@@ -1,53 +1,53 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10064
-"""
-
-import numpy as np
-import cmath
-from math import *
-
-
-def hamiltonian(k):
- gamma = 0.5
- lambda0 = 1
- delta = 0
- h = np.zeros((2, 2), dtype=complex)
- h[0,0] = delta
- h[1,1] = -delta
- h[0,1] = gamma+lambda0*cmath.exp(-1j*k)
- h[1,0] = gamma+lambda0*cmath.exp(1j*k)
- return h
-
-
-def main():
- Num_k = 100
- k_array = np.linspace(-pi, pi, Num_k)
- vector_array = []
- for k in k_array:
- vector = get_occupied_bands_vectors(k, hamiltonian)
- if k != pi:
- vector_array.append(vector)
- else:
- vector_array.append(vector_array[0])
-
- # 计算Wilson loop
- W_k = 1
- for i0 in range(Num_k-1):
- F = np.dot(vector_array[i0+1].transpose().conj(), vector_array[i0])
- W_k = np.dot(F, W_k)
- nu = np.log(W_k)/2/pi/1j
- # if np.real(nu) < 0:
- # nu += 1
- print('p=', nu, '\n')
-
-
-def get_occupied_bands_vectors(x, matrix):
- matrix0 = matrix(x)
- eigenvalue, eigenvector = np.linalg.eig(matrix0)
- vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]]
- return vector
-
-
-if __name__ == '__main__':
- main()
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/10064
+"""
+
+import numpy as np
+import cmath
+from math import *
+
+
+def hamiltonian(k):
+ gamma = 0.5
+ lambda0 = 1
+ delta = 0
+ h = np.zeros((2, 2), dtype=complex)
+ h[0,0] = delta
+ h[1,1] = -delta
+ h[0,1] = gamma+lambda0*cmath.exp(-1j*k)
+ h[1,0] = gamma+lambda0*cmath.exp(1j*k)
+ return h
+
+
+def main():
+ Num_k = 100
+ k_array = np.linspace(-pi, pi, Num_k)
+ vector_array = []
+ for k in k_array:
+ vector = get_occupied_bands_vectors(k, hamiltonian)
+ if k != pi:
+ vector_array.append(vector)
+ else:
+ vector_array.append(vector_array[0])
+
+ # 计算Wilson loop
+ W_k = 1
+ for i0 in range(Num_k-1):
+ F = np.dot(vector_array[i0+1].transpose().conj(), vector_array[i0])
+ W_k = np.dot(F, W_k)
+ nu = np.log(W_k)/2/pi/1j
+ # if np.real(nu) < 0:
+ # nu += 1
+ print('p=', nu, '\n')
+
+
+def get_occupied_bands_vectors(x, matrix):
+ matrix0 = matrix(x)
+ eigenvalue, eigenvector = np.linalg.eig(matrix0)
+ vector = eigenvector[:, np.argsort(np.real(eigenvalue))[0]]
+ return vector
+
+
+if __name__ == '__main__':
+ main()
diff --git a/academic_codes/2021.07.26_calculation_of_chern_number_with_kubo_formula/calculation_of_chern_number_with_kubo_formula.py b/academic_codes/topological_invariant/2021.07.26_calculation_of_chern_number_with_kubo_formula/calculation_of_chern_number_with_kubo_formula.py
similarity index 100%
rename from academic_codes/2021.07.26_calculation_of_chern_number_with_kubo_formula/calculation_of_chern_number_with_kubo_formula.py
rename to academic_codes/topological_invariant/2021.07.26_calculation_of_chern_number_with_kubo_formula/calculation_of_chern_number_with_kubo_formula.py
diff --git a/academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_Wilson_loop.py b/academic_codes/topological_invariant/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_Wilson_loop.py
similarity index 100%
rename from academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_Wilson_loop.py
rename to academic_codes/topological_invariant/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_Wilson_loop.py
diff --git a/academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_bands.py b/academic_codes/topological_invariant/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_bands.py
similarity index 100%
rename from academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_bands.py
rename to academic_codes/topological_invariant/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_bands.py
diff --git a/academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_nested_Wilson_loop.py b/academic_codes/topological_invariant/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_nested_Wilson_loop.py
similarity index 100%
rename from academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_nested_Wilson_loop.py
rename to academic_codes/topological_invariant/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_nested_Wilson_loop.py
diff --git a/academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/simple_method_for_degenerate_bands.py b/academic_codes/topological_invariant/2021.12.09_nested_Wilson_loop_of_BBH_model/simple_method_for_degenerate_bands.py
similarity index 100%
rename from academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/simple_method_for_degenerate_bands.py
rename to academic_codes/topological_invariant/2021.12.09_nested_Wilson_loop_of_BBH_model/simple_method_for_degenerate_bands.py
diff --git a/academic_codes/2021.12.27_Chern_numbers_of_Landau_levels/Chern_numbers_of_Landau_levels_in_square_lattice.py b/academic_codes/topological_invariant/2021.12.27_Chern_numbers_of_Landau_levels/Chern_numbers_of_Landau_levels_in_square_lattice.py
similarity index 100%
rename from academic_codes/2021.12.27_Chern_numbers_of_Landau_levels/Chern_numbers_of_Landau_levels_in_square_lattice.py
rename to academic_codes/topological_invariant/2021.12.27_Chern_numbers_of_Landau_levels/Chern_numbers_of_Landau_levels_in_square_lattice.py
diff --git a/academic_codes/2021.12.27_Chern_numbers_of_Landau_levels/quantum_transport_of_square_lattice_under_magnetic_fields_in_multi_lead_systems_with_guan.py b/academic_codes/topological_invariant/2021.12.27_Chern_numbers_of_Landau_levels/quantum_transport_of_square_lattice_under_magnetic_fields_in_multi_lead_systems_with_guan.py
similarity index 100%
rename from academic_codes/2021.12.27_Chern_numbers_of_Landau_levels/quantum_transport_of_square_lattice_under_magnetic_fields_in_multi_lead_systems_with_guan.py
rename to academic_codes/topological_invariant/2021.12.27_Chern_numbers_of_Landau_levels/quantum_transport_of_square_lattice_under_magnetic_fields_in_multi_lead_systems_with_guan.py
diff --git a/academic_codes/2021.12.28_calculation_of_Chern_number_by_Wilson_loop/calculation_of_Chern_number_by_Wilson_loop.py b/academic_codes/topological_invariant/2021.12.28_calculation_of_Chern_number_by_Wilson_loop/calculation_of_Chern_number_by_Wilson_loop.py
similarity index 100%
rename from academic_codes/2021.12.28_calculation_of_Chern_number_by_Wilson_loop/calculation_of_Chern_number_by_Wilson_loop.py
rename to academic_codes/topological_invariant/2021.12.28_calculation_of_Chern_number_by_Wilson_loop/calculation_of_Chern_number_by_Wilson_loop.py
diff --git a/academic_codes/2021.12.28_calculation_of_Chern_number_by_Wilson_loop/calculation_of_Chern_number_by_Wilson_loop_(wrong).py b/academic_codes/topological_invariant/2021.12.28_calculation_of_Chern_number_by_Wilson_loop/calculation_of_Chern_number_by_Wilson_loop_(wrong).py
similarity index 100%
rename from academic_codes/2021.12.28_calculation_of_Chern_number_by_Wilson_loop/calculation_of_Chern_number_by_Wilson_loop_(wrong).py
rename to academic_codes/topological_invariant/2021.12.28_calculation_of_Chern_number_by_Wilson_loop/calculation_of_Chern_number_by_Wilson_loop_(wrong).py
diff --git a/academic_codes/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_Kubo_formula.py b/academic_codes/topological_invariant/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_Kubo_formula.py
similarity index 100%
rename from academic_codes/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_Kubo_formula.py
rename to academic_codes/topological_invariant/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_Kubo_formula.py
diff --git a/academic_codes/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_Wilson_loop.py b/academic_codes/topological_invariant/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_Wilson_loop.py
similarity index 100%
rename from academic_codes/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_Wilson_loop.py
rename to academic_codes/topological_invariant/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_Wilson_loop.py
diff --git a/academic_codes/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_the_efficient_method.py b/academic_codes/topological_invariant/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_the_efficient_method.py
similarity index 100%
rename from academic_codes/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_the_efficient_method.py
rename to academic_codes/topological_invariant/2022.04.21_Berry_curvature/Berry_curvature_distribution_of_graphene_under_broken_inversion_symmery_with_the_efficient_method.py
diff --git a/academic_codes/2022.07.12_gauge_fixing/example_find_vector_with_fixed_gauge_by_making_one_component_real.py b/academic_codes/topological_invariant/2022.07.12_gauge_fixing/example_find_vector_with_fixed_gauge_by_making_one_component_real.py
similarity index 100%
rename from academic_codes/2022.07.12_gauge_fixing/example_find_vector_with_fixed_gauge_by_making_one_component_real.py
rename to academic_codes/topological_invariant/2022.07.12_gauge_fixing/example_find_vector_with_fixed_gauge_by_making_one_component_real.py
diff --git a/academic_codes/2022.07.12_gauge_fixing/example_rotation_of_degenerate_vectors.py b/academic_codes/topological_invariant/2022.07.12_gauge_fixing/example_rotation_of_degenerate_vectors.py
similarity index 100%
rename from academic_codes/2022.07.12_gauge_fixing/example_rotation_of_degenerate_vectors.py
rename to academic_codes/topological_invariant/2022.07.12_gauge_fixing/example_rotation_of_degenerate_vectors.py
diff --git a/academic_codes/2022.08.12_calculation_of_Chern_number_by_Wilson_loop_for_degenerate_case/calculation_of_Chern_number_by_Wilson_loop_for_degenerate_case.py b/academic_codes/topological_invariant/2022.08.12_calculation_of_Chern_number_by_Wilson_loop_for_degenerate_case/calculation_of_Chern_number_by_Wilson_loop_for_degenerate_case.py
similarity index 100%
rename from academic_codes/2022.08.12_calculation_of_Chern_number_by_Wilson_loop_for_degenerate_case/calculation_of_Chern_number_by_Wilson_loop_for_degenerate_case.py
rename to academic_codes/topological_invariant/2022.08.12_calculation_of_Chern_number_by_Wilson_loop_for_degenerate_case/calculation_of_Chern_number_by_Wilson_loop_for_degenerate_case.py
diff --git a/academic_codes/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_(function_form).py b/academic_codes/topological_invariant/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_(function_form).py
similarity index 100%
rename from academic_codes/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_(function_form).py
rename to academic_codes/topological_invariant/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_(function_form).py
diff --git a/academic_codes/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_for_degenerate_case_(function_form).py b/academic_codes/topological_invariant/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_for_degenerate_case_(function_form).py
similarity index 100%
rename from academic_codes/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_for_degenerate_case_(function_form).py
rename to academic_codes/topological_invariant/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_for_degenerate_case_(function_form).py
diff --git a/academic_codes/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_(function_form).py b/academic_codes/topological_invariant/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_(function_form).py
similarity index 100%
rename from academic_codes/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_(function_form).py
rename to academic_codes/topological_invariant/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_(function_form).py
diff --git a/academic_codes/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_for_degenerate_case_(function_form).py b/academic_codes/topological_invariant/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_for_degenerate_case_(function_form).py
similarity index 100%
rename from academic_codes/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_for_degenerate_case_(function_form).py
rename to academic_codes/topological_invariant/2022.08.13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_for_degenerate_case_(function_form).py
diff --git a/academic_codes/2022.08.28_calculation_of_Chern_number_by_efficient_method_for_degenerate_case/calculation_of_Chern_number_by_efficient_method_for_degenerate_case.py b/academic_codes/topological_invariant/2022.08.28_calculation_of_Chern_number_by_efficient_method_for_degenerate_case/calculation_of_Chern_number_by_efficient_method_for_degenerate_case.py
similarity index 100%
rename from academic_codes/2022.08.28_calculation_of_Chern_number_by_efficient_method_for_degenerate_case/calculation_of_Chern_number_by_efficient_method_for_degenerate_case.py
rename to academic_codes/topological_invariant/2022.08.28_calculation_of_Chern_number_by_efficient_method_for_degenerate_case/calculation_of_Chern_number_by_efficient_method_for_degenerate_case.py
diff --git a/language_learning/2019.10.31_fortran_example/Console1.f90 b/language_learning/fortran/2019.10.31_fortran_example/Console1.f90
similarity index 100%
rename from language_learning/2019.10.31_fortran_example/Console1.f90
rename to language_learning/fortran/2019.10.31_fortran_example/Console1.f90
diff --git a/language_learning/2019.10.31_parallel_calculations_with_OpenMP/Console1.f90 b/language_learning/fortran/2019.10.31_parallel_calculations_with_OpenMP/Console1.f90
similarity index 100%
rename from language_learning/2019.10.31_parallel_calculations_with_OpenMP/Console1.f90
rename to language_learning/fortran/2019.10.31_parallel_calculations_with_OpenMP/Console1.f90
diff --git a/language_learning/2023.07.19_include_and_use_in_fortran/example.f90 b/language_learning/fortran/2023.07.19_include_and_use_in_fortran/example.f90
similarity index 100%
rename from language_learning/2023.07.19_include_and_use_in_fortran/example.f90
rename to language_learning/fortran/2023.07.19_include_and_use_in_fortran/example.f90
diff --git a/language_learning/2023.07.19_include_and_use_in_fortran/example_include_1.f90 b/language_learning/fortran/2023.07.19_include_and_use_in_fortran/example_include_1.f90
similarity index 100%
rename from language_learning/2023.07.19_include_and_use_in_fortran/example_include_1.f90
rename to language_learning/fortran/2023.07.19_include_and_use_in_fortran/example_include_1.f90
diff --git a/language_learning/2023.07.19_include_and_use_in_fortran/example_include_2.f90 b/language_learning/fortran/2023.07.19_include_and_use_in_fortran/example_include_2.f90
similarity index 100%
rename from language_learning/2023.07.19_include_and_use_in_fortran/example_include_2.f90
rename to language_learning/fortran/2023.07.19_include_and_use_in_fortran/example_include_2.f90
diff --git a/language_learning/2019.12.04_latex_example/basic_structure.tex b/language_learning/latex/2019.12.04_latex_example/basic_structure.tex
old mode 100755
new mode 100644
similarity index 97%
rename from language_learning/2019.12.04_latex_example/basic_structure.tex
rename to language_learning/latex/2019.12.04_latex_example/basic_structure.tex
index ab06ec9..632c65c
--- a/language_learning/2019.12.04_latex_example/basic_structure.tex
+++ b/language_learning/latex/2019.12.04_latex_example/basic_structure.tex
@@ -1,5 +1,5 @@
-\documentclass{article} %文档类声明
-%导言区（文档类声明和正文间的是导言区）
-\begin{document} %正文开始
- Hello, world! %正文
-\end{document} %正文结束
+\documentclass{article} %文档类声明
+%导言区（文档类声明和正文间的是导言区）
+\begin{document} %正文开始
+ Hello, world! %正文
+\end{document} %正文结束
diff --git a/language_learning/2019.12.04_latex_example/simple_example.tex b/language_learning/latex/2019.12.04_latex_example/simple_example.tex
old mode 100755
new mode 100644
similarity index 97%
rename from language_learning/2019.12.04_latex_example/simple_example.tex
rename to language_learning/latex/2019.12.04_latex_example/simple_example.tex
index 5e1f4cd..107054d
--- a/language_learning/2019.12.04_latex_example/simple_example.tex
+++ b/language_learning/latex/2019.12.04_latex_example/simple_example.tex
@@ -1,18 +1,18 @@
-\documentclass{article} %文档类声明
-\usepackage{ctex} %一个支持中文宏包，如果不用中文无法显示
-
-
-\begin{document} %正文
- \title{这是一个标题} %标题
- \author{作者名字} %作者
- \date{} %\maketitle默认会加上当前时间，用\date{}，空着内容可以取消时间的显示
- \maketitle %加了这个，标题、作者等信息才会显示
- \section{节}
- \subsection{小节}
- \subsubsection{子小节}
- Hello, world! %下面空一行代表换行
- \textbf{用\textbackslash textbf\{\}可以加粗文本} %\textbf{}可以加粗文本
- \section{节}
- \part{}
- \part{}
+\documentclass{article} %文档类声明
+\usepackage{ctex} %一个支持中文宏包，如果不用中文无法显示
+
+
+\begin{document} %正文
+ \title{这是一个标题} %标题
+ \author{作者名字} %作者
+ \date{} %\maketitle默认会加上当前时间，用\date{}，空着内容可以取消时间的显示
+ \maketitle %加了这个，标题、作者等信息才会显示
+ \section{节}
+ \subsection{小节}
+ \subsubsection{子小节}
+ Hello, world! %下面空一行代表换行
+ \textbf{用\textbackslash textbf\{\}可以加粗文本} %\textbf{}可以加粗文本
+ \section{节}
+ \part{}
+ \part{}
\end{document}
\ No newline at end of file
diff --git a/language_learning/2019.12.05_beamer_as_slides/1.jpg b/language_learning/latex/2019.12.05_beamer_as_slides/1.jpg
old mode 100755
new mode 100644
similarity index 100%
rename from language_learning/2019.12.05_beamer_as_slides/1.jpg
rename to language_learning/latex/2019.12.05_beamer_as_slides/1.jpg
diff --git a/language_learning/2019.12.05_beamer_as_slides/beamer.tex b/language_learning/latex/2019.12.05_beamer_as_slides/beamer.tex
old mode 100755
new mode 100644
similarity index 97%
rename from language_learning/2019.12.05_beamer_as_slides/beamer.tex
rename to language_learning/latex/2019.12.05_beamer_as_slides/beamer.tex
index eeda189..7e2169e
--- a/language_learning/2019.12.05_beamer_as_slides/beamer.tex
+++ b/language_learning/latex/2019.12.05_beamer_as_slides/beamer.tex
@@ -1,50 +1,50 @@
-\documentclass{beamer}
-\usepackage{ctex} %一个支持中文宏包，如果不用中文无法显示
-\usepackage{graphicx} %这个包提供\includegraphics命令来插入图片
-
-\usetheme{Boadilla} %主题
-\usecolortheme{default} %主题的颜色
-
-\title{这是PPT标题} %标题
-\author{作者名字\inst{1}，作者名字\inst{2}} %作者
-\institute{\inst{1}第一个单位\and\inst{2}第二个单位} %这里的\and有换行的效果
-\date{\today} %时间（默认也会显示）
-\logo{\includegraphics[height=1.0cm]{1.jpg}} %右下角的小log
-
-\begin{document} %正文开始
- \begin{frame} %相当于ppt里的一页
- \titlepage %标题页
- \end{frame}
- \begin{frame}
- \frametitle{目录} %当前页的标题
- \tableofcontents %制作目录，需要\section{}配合
- \end{frame}
-
- \section{第一节} %用来做目录
- \begin{frame}
- \frametitle{当前页的标题1}
- 这是第一节第一页的内容。This is a text in the first frame. This is a text in the first frame. This is a text in the first frame.
- \end{frame}
-
- \section{第二节}
- \begin{frame}
- \frametitle{当前页的标题2}
- 这是第二节第一页的内容。这里使用了\textbackslash pause。\pause This is a text in the second frame. This is a text in the second frame. This is a text in the second frame. %\pause是暂停，前后会分成两页。
- \end{frame}
- \begin{frame}
- \frametitle{当前页的标题3：Two-column slide}
- \begin{columns} %分成列
- \column{0.5\textwidth} %占用一半
- This is a text in first column.
- $$E=mc^2$$
- \begin{itemize} %制作列表
- \item First item
- \item Second item
- \end{itemize}
- \column{0.5\textwidth} %占用一半
- This text will be in the second column
- and on a second tought this is a nice looking
- layout in some cases.
- \end{columns}
- \end{frame}
+\documentclass{beamer}
+\usepackage{ctex} %一个支持中文宏包，如果不用中文无法显示
+\usepackage{graphicx} %这个包提供\includegraphics命令来插入图片
+
+\usetheme{Boadilla} %主题
+\usecolortheme{default} %主题的颜色
+
+\title{这是PPT标题} %标题
+\author{作者名字\inst{1}，作者名字\inst{2}} %作者
+\institute{\inst{1}第一个单位\and\inst{2}第二个单位} %这里的\and有换行的效果
+\date{\today} %时间（默认也会显示）
+\logo{\includegraphics[height=1.0cm]{1.jpg}} %右下角的小log
+
+\begin{document} %正文开始
+ \begin{frame} %相当于ppt里的一页
+ \titlepage %标题页
+ \end{frame}
+ \begin{frame}
+ \frametitle{目录} %当前页的标题
+ \tableofcontents %制作目录，需要\section{}配合
+ \end{frame}
+
+ \section{第一节} %用来做目录
+ \begin{frame}
+ \frametitle{当前页的标题1}
+ 这是第一节第一页的内容。This is a text in the first frame. This is a text in the first frame. This is a text in the first frame.
+ \end{frame}
+
+ \section{第二节}
+ \begin{frame}
+ \frametitle{当前页的标题2}
+ 这是第二节第一页的内容。这里使用了\textbackslash pause。\pause This is a text in the second frame. This is a text in the second frame. This is a text in the second frame. %\pause是暂停，前后会分成两页。
+ \end{frame}
+ \begin{frame}
+ \frametitle{当前页的标题3：Two-column slide}
+ \begin{columns} %分成列
+ \column{0.5\textwidth} %占用一半
+ This is a text in first column.
+ $$E=mc^2$$
+ \begin{itemize} %制作列表
+ \item First item
+ \item Second item
+ \end{itemize}
+ \column{0.5\textwidth} %占用一半
+ This text will be in the second column
+ and on a second tought this is a nice looking
+ layout in some cases.
+ \end{columns}
+ \end{frame}
\end{document}
\ No newline at end of file
diff --git a/language_learning/2023.07.25_S1_in_Eq_and_Fig/S1_in_Eq_and_Fig.tex b/language_learning/latex/2023.07.25_S1_in_Eq_and_Fig/S1_in_Eq_and_Fig.tex
similarity index 100%
rename from language_learning/2023.07.25_S1_in_Eq_and_Fig/S1_in_Eq_and_Fig.tex
rename to language_learning/latex/2023.07.25_S1_in_Eq_and_Fig/S1_in_Eq_and_Fig.tex
diff --git a/language_learning/2019.10.29_matlab_example/matlab_example.m b/language_learning/others/2019.10.29_matlab_example/matlab_example.m
old mode 100755
new mode 100644
similarity index 97%
rename from language_learning/2019.10.29_matlab_example/matlab_example.m
rename to language_learning/others/2019.10.29_matlab_example/matlab_example.m
index 9d4d173..6dfd5da
--- a/language_learning/2019.10.29_matlab_example/matlab_example.m
+++ b/language_learning/others/2019.10.29_matlab_example/matlab_example.m
@@ -1,41 +1,41 @@
-% This code is supported by the website: https://www.guanjihuan.com
-% The newest version of this code is on the web page: https://www.guanjihuan.com/archives/766
-
-
-%在matlab里加上百分号“%”是注释。
-%快捷键：选中按ctrl+R为注释，选中按ctrl+T为取消注释，
-clc; %clc有窗口清空的效果，一般都用上
-clear all; %clear all可以清空所有变量，一般都用上
-clf; %clf为清空输出的图片内容，在画图的时候最好添加上
-
-aa=1 %没加分号“;”,默认打印输出
-bb=2; %加了分号“;”，即不打印输出
-cc1=zeros(2,3) %零矩阵用zeros()
-cc2=eye(3,3) %单位矩阵
-
-%矩阵乘积
-matrix1=[3,3;3,3] %里面分号代表矩阵换一行。下标是从1开始记。
-matrix2=[2,0;0,2]
-matrix_product_1=matrix1*matrix2 % *是正常的矩阵乘积
-matrix_product_2=matrix1.*matrix2 % .*是矩阵每个元素对应相乘
-
-%循环
-for i0=1:0.5:2 %循环内容为for到end。a:b:c代表最小为a，最大为c，步长为b
- for_result=i0+1i %i在matlab中代表虚数，所以起变量名最好不要用i。要输出内容，后面不加分号即可
-end
-
-%判断
-if aa~=1 %在matlab中，~=代表不等于，==代表等于
- dd=100
-else
- dd=300
-end
-
-matrix=[2,3;5,7]
-%求本征矢和本征值
-[V,D]=eig(matrix) %在matlab中，V的列向量是本征矢，注意是列。D的对角上是对应本征值。
-%求逆
-inv1=inv(matrix) %求逆
-inv2=matrix^-1 %求逆也可以这样写
-%画图
+% This code is supported by the website: https://www.guanjihuan.com
+% The newest version of this code is on the web page: https://www.guanjihuan.com/archives/766
+
+
+%在matlab里加上百分号“%”是注释。
+%快捷键：选中按ctrl+R为注释，选中按ctrl+T为取消注释，
+clc; %clc有窗口清空的效果，一般都用上
+clear all; %clear all可以清空所有变量，一般都用上
+clf; %clf为清空输出的图片内容，在画图的时候最好添加上
+
+aa=1 %没加分号“;”,默认打印输出
+bb=2; %加了分号“;”，即不打印输出
+cc1=zeros(2,3) %零矩阵用zeros()
+cc2=eye(3,3) %单位矩阵
+
+%矩阵乘积
+matrix1=[3,3;3,3] %里面分号代表矩阵换一行。下标是从1开始记。
+matrix2=[2,0;0,2]
+matrix_product_1=matrix1*matrix2 % *是正常的矩阵乘积
+matrix_product_2=matrix1.*matrix2 % .*是矩阵每个元素对应相乘
+
+%循环
+for i0=1:0.5:2 %循环内容为for到end。a:b:c代表最小为a，最大为c，步长为b
+ for_result=i0+1i %i在matlab中代表虚数，所以起变量名最好不要用i。要输出内容，后面不加分号即可
+end
+
+%判断
+if aa~=1 %在matlab中，~=代表不等于，==代表等于
+ dd=100
+else
+ dd=300
+end
+
+matrix=[2,3;5,7]
+%求本征矢和本征值
+[V,D]=eig(matrix) %在matlab中，V的列向量是本征矢，注意是列。D的对角上是对应本征值。
+%求逆
+inv1=inv(matrix) %求逆
+inv2=matrix^-1 %求逆也可以这样写
+%画图
plot([0:20],[10:-1:-10],'-o') %更多画图技巧可参考官方文档或网上资料
\ No newline at end of file
diff --git a/language_learning/2019.11.16_markdown_example/markdown_example.md b/language_learning/others/2019.11.16_markdown_example/markdown_example.md
old mode 100755
new mode 100644
similarity index 96%
rename from language_learning/2019.11.16_markdown_example/markdown_example.md
rename to language_learning/others/2019.11.16_markdown_example/markdown_example.md
index 114b040..b6f65bb
--- a/language_learning/2019.11.16_markdown_example/markdown_example.md
+++ b/language_learning/others/2019.11.16_markdown_example/markdown_example.md
@@ -1,47 +1,47 @@
-# 一级标题
-## 二级标题
-### 三级标题
-#### 四级标题
-
-有序列表：数字加一个点
-
-1. 列表内容
-2. 列表内容
-3. 列表内容
-
-无序列表：用 + - * 任何一种都可以。为了不和其他记号重复，个人倾向于用 + 。
-
-+ 列表内容
- + 嵌套前面加几个空格。为了保险起见，个人倾向于用四个空格或一个Tab。
-+ 列表内容
- + 列表嵌套
- + 列表嵌套
- + 列表嵌套
-
-*倾斜：前后一个星号*
-
-**加粗：前后两个星号**
-
-***斜体加粗：前后三个星号***
-
-代码：用三个反引号。如下所示。
-
-```
-print('hello world')
-```
-
-分割线：三个或者三个以上的 - 或 * 。为了不和其他记号重复，个人倾向于用 --- 。
-
----
-
-在Markdown中空一行可采用以下符号。该符号为HTML中的符号，在Markdown中也是支持的。
-
-
-
-以下是表格的书写形式。其中，第二行用一个横杆也是可以。为了保险起见，个人倾向于用三个横杆。
-
-| 右对齐 | 居中对齐 | 左对齐 |
-| ---: | :---: | :--- |
-| 单元格 123 | 单元格 456 | 单元格 759 |
-| 单元格 | 单元格 | 单元格 |
+# 一级标题
+## 二级标题
+### 三级标题
+#### 四级标题
+
+有序列表：数字加一个点
+
+1. 列表内容
+2. 列表内容
+3. 列表内容
+
+无序列表：用 + - * 任何一种都可以。为了不和其他记号重复，个人倾向于用 + 。
+
++ 列表内容
+ + 嵌套前面加几个空格。为了保险起见，个人倾向于用四个空格或一个Tab。
++ 列表内容
+ + 列表嵌套
+ + 列表嵌套
+ + 列表嵌套
+
+*倾斜：前后一个星号*
+
+**加粗：前后两个星号**
+
+***斜体加粗：前后三个星号***
+
+代码：用三个反引号。如下所示。
+
+```
+print('hello world')
+```
+
+分割线：三个或者三个以上的 - 或 * 。为了不和其他记号重复，个人倾向于用 --- 。
+
+---
+
+在Markdown中空一行可采用以下符号。该符号为HTML中的符号，在Markdown中也是支持的。
+
+
+
+以下是表格的书写形式。其中，第二行用一个横杆也是可以。为了保险起见，个人倾向于用三个横杆。
+
+| 右对齐 | 居中对齐 | 左对齐 |
+| ---: | :---: | :--- |
+| 单元格 123 | 单元格 456 | 单元格 759 |
+| 单元格 | 单元格 | 单元格 |
| 单元格 | 单元格 | 单元格 |
\ No newline at end of file
diff --git a/language_learning/2022.04.24_html_learning/html_learning.html b/language_learning/others/2022.04.24_html_learning/html_learning.html
similarity index 100%
rename from language_learning/2022.04.24_html_learning/html_learning.html
rename to language_learning/others/2022.04.24_html_learning/html_learning.html
diff --git a/language_learning/2019.10.10_python_example/python_example.py b/language_learning/python/2019.10.10_python_example/python_example.py
old mode 100755
new mode 100644
similarity index 97%
rename from language_learning/2019.10.10_python_example/python_example.py
rename to language_learning/python/2019.10.10_python_example/python_example.py
index 0c1b49e..323ff38
--- a/language_learning/2019.10.10_python_example/python_example.py
+++ b/language_learning/python/2019.10.10_python_example/python_example.py
@@ -1,118 +1,118 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/417
-"""
-
-
-# 第一部分：Python基本操作（循环，判断，函数，文件写入）
-print('\n第一部分：Python基本操作（循环，判断，函数，文件写入）\n') # \n代表换行
-
-for i in range(5): # 循环（这里只举例for循环，还有其他循环）
- print('我是循环产生的数：', i) # Python中没有end，因此每个语句的缩进很重要
- if i == 2: # 判断
- print('判断：我是第三个数 2')
- else:
- pass # pass代表不执行任何语句，用于占位，可以之后再补充，不然空着会报错
-print() # 输出空一行
-
-def fun0(arg): # 定义函数
- print('我是函数中的内容，参数值为：', arg)
- return arg*2 # 返回值
-
-print('函数返回值：', fun0(5)) # 调用函数
-print()
-
-def main(): # “主函数”，其实也是一个普通的函数，也可以起其他名字
- print('我是主函数中的内容。')
- print()
-
-if __name__ == '__main__': # 如果直接运行本文件，那么执行以下内容。如果是import本文件，那么不执行。
- main()
-
-# 关于类class，这里不举例了。科学计算中主要还是面向过程，面向对象用的比较少。
-
-# 文件写入
-# 第一种方式
-with open('test1.txt', 'w') as f1: # 其中'w'为重新写入，改为'a'是补充内容
- f1.write(str(100)+'\n这是第一种方式写入文件') # str()为转换成字符串
-# 第二种方式
-f2 = open('test2.txt', 'w') # 打开文件
-f2.write(str(200)+'\n这是第二种方式写入文件') # 写入文件
-f2.close() # 关闭文件
-print('已写入文件！')
-print()
-
-
-
-
-# 第二部分：Numpy库中常用的语句
-print('\n\n\n第二部分：Numpy库中常用的语句\n')
-import numpy as np
-
-print('零矩阵：\n', np.zeros((2, 3))) # 注意np.zeros()里需要填元组，因此显示的是两个括号
-print('单位矩阵：\n', np.identity(3)) # 3行3列的单位矩阵,或者可以用np.eye()
-print('把一维数组按对角矩阵排列：\n', np.diag([1, 3, 5]))
-print()
-
-print('指定步长的等差数列：\n', np.arange(1, 5, .5)) # 区间是左闭右开[1, 5)，步长为0.5
-print('指定个数的等差数列：\n', np.linspace(-2, 2, 5)) # 区间是左闭右闭[-2, 2], 数量是5
-print()
-
-print('随机数：\n', np.random.uniform(-2, 2)) # 随机浮点数
-print('随机整数：\n', np.random.randint(-10, 10)) # 区间是左闭右开[-10, 10)
-print()
-
-# 随机数除了使用numpy库，也使用random生成
-import random
-print('使用random库的随机数：\n', random.uniform(-2,2)) # 随机浮点数
-print('使用random库的随机整数：\n', random.randint(-10, 10)) # 区间是左闭右闭[-10, 10]
-print()
-
-print('数组从小到大排列：\n', np.sort([1, 7, 0, 3]))
-print('数组从小到大排列对应的索引：\n', np.argsort([1, 7, 0, 3])) # 注意Python中下标是从0开始的
-print()
-
-matrix0 = np.array([[1, 2+9j, 3], [2, 5, 7]]) # numpy数组
-print('矩阵0：\n', matrix0)
-print('矩阵的维度：\n', matrix0.shape) # 查看矩阵的维度
-print('矩阵的行数：\n', matrix0.shape[0]) # 查看矩阵的行数
-print('矩阵的列数：\n', matrix0.shape[1]) # 查看矩阵的列数
-print('矩阵转置：\n', matrix0.transpose()) # 矩阵转置
-print('矩阵转置共轭：\n', matrix0.transpose().conj()) # 矩阵转置共轭
-print()
-
-matrix1 = np.array([[3, 5], [2, 7]])
-eigenvalue, eigenvector = np.linalg.eig(matrix1) # 求本征值，本征向量
-print('矩阵1：\n', matrix1)
-print('本征值：\n', eigenvalue)
-print('本征向量：\n', eigenvector) # 列向量为本征向量
-print('逆矩阵：\n', np.linalg.inv(matrix1)) # 求逆
-print('计算行列式：\n', np.linalg.det(matrix1)) # 行列式
-print()
-
-matrix2 = np.array([[1, 2], [3, 4]])
-print('矩阵2：\n', matrix2)
-print('矩阵1和矩阵2相乘：\n', np.matmul(matrix1, matrix2)) # 矩阵乘积，或者可以用np.dot()
-print()
-
-a = np.array([1, 2])
-print('numpy数组a=', a)
-b = np.array([3, 4])
-print('numpy数组b=', b)
-c = np.append(a, b, axis=0) # 增加元素
-print('numpy数组增加元素：\n', c)
-d = np.append([a], [b], axis=0) # 增加行（列数要相同），或者用np.row_stack(([a], [b]))
-print('numpy数组增加行：\n', d)
-e = np.append([a], [b], axis=1) # 增加列（行数要相同），或者用np.column_stack(([a], [b]))
-print('numpy数组增加列：\n', e)
-print('重新观察：a=', a)
-print('重新观察：b=', b)
-print()
-
-
-# 如果不是numpy数组，原python数组可以直接用以下方法增加元素
-c = [100, 200]
-print('python数组c=', c)
-c.append(300)
-print('增加元素后，c=', c)
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/417
+"""
+
+
+# 第一部分：Python基本操作（循环，判断，函数，文件写入）
+print('\n第一部分：Python基本操作（循环，判断，函数，文件写入）\n') # \n代表换行
+
+for i in range(5): # 循环（这里只举例for循环，还有其他循环）
+ print('我是循环产生的数：', i) # Python中没有end，因此每个语句的缩进很重要
+ if i == 2: # 判断
+ print('判断：我是第三个数 2')
+ else:
+ pass # pass代表不执行任何语句，用于占位，可以之后再补充，不然空着会报错
+print() # 输出空一行
+
+def fun0(arg): # 定义函数
+ print('我是函数中的内容，参数值为：', arg)
+ return arg*2 # 返回值
+
+print('函数返回值：', fun0(5)) # 调用函数
+print()
+
+def main(): # “主函数”，其实也是一个普通的函数，也可以起其他名字
+ print('我是主函数中的内容。')
+ print()
+
+if __name__ == '__main__': # 如果直接运行本文件，那么执行以下内容。如果是import本文件，那么不执行。
+ main()
+
+# 关于类class，这里不举例了。科学计算中主要还是面向过程，面向对象用的比较少。
+
+# 文件写入
+# 第一种方式
+with open('test1.txt', 'w') as f1: # 其中'w'为重新写入，改为'a'是补充内容
+ f1.write(str(100)+'\n这是第一种方式写入文件') # str()为转换成字符串
+# 第二种方式
+f2 = open('test2.txt', 'w') # 打开文件
+f2.write(str(200)+'\n这是第二种方式写入文件') # 写入文件
+f2.close() # 关闭文件
+print('已写入文件！')
+print()
+
+
+
+
+# 第二部分：Numpy库中常用的语句
+print('\n\n\n第二部分：Numpy库中常用的语句\n')
+import numpy as np
+
+print('零矩阵：\n', np.zeros((2, 3))) # 注意np.zeros()里需要填元组，因此显示的是两个括号
+print('单位矩阵：\n', np.identity(3)) # 3行3列的单位矩阵,或者可以用np.eye()
+print('把一维数组按对角矩阵排列：\n', np.diag([1, 3, 5]))
+print()
+
+print('指定步长的等差数列：\n', np.arange(1, 5, .5)) # 区间是左闭右开[1, 5)，步长为0.5
+print('指定个数的等差数列：\n', np.linspace(-2, 2, 5)) # 区间是左闭右闭[-2, 2], 数量是5
+print()
+
+print('随机数：\n', np.random.uniform(-2, 2)) # 随机浮点数
+print('随机整数：\n', np.random.randint(-10, 10)) # 区间是左闭右开[-10, 10)
+print()
+
+# 随机数除了使用numpy库，也使用random生成
+import random
+print('使用random库的随机数：\n', random.uniform(-2,2)) # 随机浮点数
+print('使用random库的随机整数：\n', random.randint(-10, 10)) # 区间是左闭右闭[-10, 10]
+print()
+
+print('数组从小到大排列：\n', np.sort([1, 7, 0, 3]))
+print('数组从小到大排列对应的索引：\n', np.argsort([1, 7, 0, 3])) # 注意Python中下标是从0开始的
+print()
+
+matrix0 = np.array([[1, 2+9j, 3], [2, 5, 7]]) # numpy数组
+print('矩阵0：\n', matrix0)
+print('矩阵的维度：\n', matrix0.shape) # 查看矩阵的维度
+print('矩阵的行数：\n', matrix0.shape[0]) # 查看矩阵的行数
+print('矩阵的列数：\n', matrix0.shape[1]) # 查看矩阵的列数
+print('矩阵转置：\n', matrix0.transpose()) # 矩阵转置
+print('矩阵转置共轭：\n', matrix0.transpose().conj()) # 矩阵转置共轭
+print()
+
+matrix1 = np.array([[3, 5], [2, 7]])
+eigenvalue, eigenvector = np.linalg.eig(matrix1) # 求本征值，本征向量
+print('矩阵1：\n', matrix1)
+print('本征值：\n', eigenvalue)
+print('本征向量：\n', eigenvector) # 列向量为本征向量
+print('逆矩阵：\n', np.linalg.inv(matrix1)) # 求逆
+print('计算行列式：\n', np.linalg.det(matrix1)) # 行列式
+print()
+
+matrix2 = np.array([[1, 2], [3, 4]])
+print('矩阵2：\n', matrix2)
+print('矩阵1和矩阵2相乘：\n', np.matmul(matrix1, matrix2)) # 矩阵乘积，或者可以用np.dot()
+print()
+
+a = np.array([1, 2])
+print('numpy数组a=', a)
+b = np.array([3, 4])
+print('numpy数组b=', b)
+c = np.append(a, b, axis=0) # 增加元素
+print('numpy数组增加元素：\n', c)
+d = np.append([a], [b], axis=0) # 增加行（列数要相同），或者用np.row_stack(([a], [b]))
+print('numpy数组增加行：\n', d)
+e = np.append([a], [b], axis=1) # 增加列（行数要相同），或者用np.column_stack(([a], [b]))
+print('numpy数组增加列：\n', e)
+print('重新观察：a=', a)
+print('重新观察：b=', b)
+print()
+
+
+# 如果不是numpy数组，原python数组可以直接用以下方法增加元素
+c = [100, 200]
+print('python数组c=', c)
+c.append(300)
+print('增加元素后，c=', c)
print()
\ No newline at end of file
diff --git a/language_learning/2019.10.11_neutral_network_with_tensorflow/neutral_network_with_tensorflow.py b/language_learning/python/2019.10.11_neutral_network_with_tensorflow/neutral_network_with_tensorflow.py
similarity index 100%
rename from language_learning/2019.10.11_neutral_network_with_tensorflow/neutral_network_with_tensorflow.py
rename to language_learning/python/2019.10.11_neutral_network_with_tensorflow/neutral_network_with_tensorflow.py
diff --git a/language_learning/2019.10.11_tensorflow_example/tensorflow.py b/language_learning/python/2019.10.11_tensorflow_example/tensorflow.py
similarity index 100%
rename from language_learning/2019.10.11_tensorflow_example/tensorflow.py
rename to language_learning/python/2019.10.11_tensorflow_example/tensorflow.py
diff --git a/language_learning/2019.10.27_ball_games_with_pygame/ball_games_with_pygame.py b/language_learning/python/2019.10.27_ball_games_with_pygame/ball_games_with_pygame.py
similarity index 100%
rename from language_learning/2019.10.27_ball_games_with_pygame/ball_games_with_pygame.py
rename to language_learning/python/2019.10.27_ball_games_with_pygame/ball_games_with_pygame.py
diff --git a/language_learning/2019.10.27_stock_date_from_tushare/stock_date_from_tushare.py b/language_learning/python/2019.10.27_stock_date_from_tushare/stock_date_from_tushare.py
similarity index 100%
rename from language_learning/2019.10.27_stock_date_from_tushare/stock_date_from_tushare.py
rename to language_learning/python/2019.10.27_stock_date_from_tushare/stock_date_from_tushare.py
diff --git a/language_learning/2019.12.03_create_GIF_with_python/create_GIF_with_python.py b/language_learning/python/2019.12.03_create_GIF_with_python/create_GIF_with_python.py
similarity index 100%
rename from language_learning/2019.12.03_create_GIF_with_python/create_GIF_with_python.py
rename to language_learning/python/2019.12.03_create_GIF_with_python/create_GIF_with_python.py
diff --git a/language_learning/2020.06.01_parallel_calculations_with_python/application_of_parallel_calculations_with_python.py b/language_learning/python/2020.06.01_parallel_calculations_with_python/application_of_parallel_calculations_with_python.py
similarity index 100%
rename from language_learning/2020.06.01_parallel_calculations_with_python/application_of_parallel_calculations_with_python.py
rename to language_learning/python/2020.06.01_parallel_calculations_with_python/application_of_parallel_calculations_with_python.py
diff --git a/language_learning/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python.py b/language_learning/python/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python.py
old mode 100755
new mode 100644
similarity index 96%
rename from language_learning/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python.py
rename to language_learning/python/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python.py
index c37e6a7..15ee46b
--- a/language_learning/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python.py
+++ b/language_learning/python/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python.py
@@ -1,42 +1,42 @@
-from multiprocessing import Process
-import os
-import time
-
-def run_proc(name): # 要执行的代码
- start_time = time.perf_counter()
- time.sleep(2)
- end_time = time.perf_counter()
- print ('Process id running on %s = %s' % (name, os.getpid()), '; running time = %s' % (end_time-start_time))
-
-
-if __name__ == '__main__':
-
- # 串行
- print('串行程序')
- print('Process id = %s.' % os.getpid())
- start_time = time.perf_counter()
- run_proc('job1')
- run_proc('job2')
- run_proc('job3')
- run_proc('job4')
- end_time = time.perf_counter()
- print('CPU执行时间(s)=', (end_time-start_time), '\n')
-
- # 并行
- print('并行程序')
- print('Process id = %s.' % os.getpid())
- start_time = time.perf_counter()
- p1 = Process(target=run_proc, args=('job1',))
- p2 = Process(target=run_proc, args=('job2',))
- p3 = Process(target=run_proc, args=('job3',))
- p4 = Process(target=run_proc, args=('job4',))
- p1.start()
- p2.start()
- p3.start()
- p4.start()
- p1.join() # join()方法可以等待子进程结束后再继续往下运行
- p2.join()
- p3.join()
- p4.join()
- end_time = time.perf_counter()
+from multiprocessing import Process
+import os
+import time
+
+def run_proc(name): # 要执行的代码
+ start_time = time.perf_counter()
+ time.sleep(2)
+ end_time = time.perf_counter()
+ print ('Process id running on %s = %s' % (name, os.getpid()), '; running time = %s' % (end_time-start_time))
+
+
+if __name__ == '__main__':
+
+ # 串行
+ print('串行程序')
+ print('Process id = %s.' % os.getpid())
+ start_time = time.perf_counter()
+ run_proc('job1')
+ run_proc('job2')
+ run_proc('job3')
+ run_proc('job4')
+ end_time = time.perf_counter()
+ print('CPU执行时间(s)=', (end_time-start_time), '\n')
+
+ # 并行
+ print('并行程序')
+ print('Process id = %s.' % os.getpid())
+ start_time = time.perf_counter()
+ p1 = Process(target=run_proc, args=('job1',))
+ p2 = Process(target=run_proc, args=('job2',))
+ p3 = Process(target=run_proc, args=('job3',))
+ p4 = Process(target=run_proc, args=('job4',))
+ p1.start()
+ p2.start()
+ p3.start()
+ p4.start()
+ p1.join() # join()方法可以等待子进程结束后再继续往下运行
+ p2.join()
+ p3.join()
+ p4.join()
+ end_time = time.perf_counter()
print('运行时间(s)=', (end_time-start_time))
\ No newline at end of file
diff --git a/language_learning/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python_using_Value.py b/language_learning/python/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python_using_Value.py
similarity index 100%
rename from language_learning/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python_using_Value.py
rename to language_learning/python/2020.06.01_parallel_calculations_with_python/parallel_calculations_with_python_using_Value.py
diff --git a/language_learning/2020.08.29_several_python_functions/2D_data_plot_and_write/plot_with_2D_data.py b/language_learning/python/2020.08.29_several_python_functions/2D_data_plot_and_write/plot_with_2D_data.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/2D_data_plot_and_write/plot_with_2D_data.py
rename to language_learning/python/2020.08.29_several_python_functions/2D_data_plot_and_write/plot_with_2D_data.py
diff --git a/language_learning/2020.08.29_several_python_functions/2D_data_plot_and_write/write_2D_data_into_txt_file.py b/language_learning/python/2020.08.29_several_python_functions/2D_data_plot_and_write/write_2D_data_into_txt_file.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/2D_data_plot_and_write/write_2D_data_into_txt_file.py
rename to language_learning/python/2020.08.29_several_python_functions/2D_data_plot_and_write/write_2D_data_into_txt_file.py
diff --git a/language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_plot_1D_bands.py b/language_learning/python/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_plot_1D_bands.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_plot_1D_bands.py
rename to language_learning/python/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_plot_1D_bands.py
diff --git a/language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_plot_2D_bands.py b/language_learning/python/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_plot_2D_bands.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_plot_2D_bands.py
rename to language_learning/python/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_plot_2D_bands.py
diff --git a/language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_1D_data_into_txt_file.py b/language_learning/python/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_1D_data_into_txt_file.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_1D_data_into_txt_file.py
rename to language_learning/python/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_1D_data_into_txt_file.py
diff --git a/language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_2D_data_into_txt_file.py b/language_learning/python/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_2D_data_into_txt_file.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_2D_data_into_txt_file.py
rename to language_learning/python/2020.08.29_several_python_functions/get_eigenvalues_plot_and_write/get_eigenvalues_and_write_2D_data_into_txt_file.py
diff --git a/language_learning/2020.08.29_several_python_functions/python_structure.py b/language_learning/python/2020.08.29_several_python_functions/python_structure.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/python_structure.py
rename to language_learning/python/2020.08.29_several_python_functions/python_structure.py
diff --git a/language_learning/2020.08.29_several_python_functions/read_txt_file/1D_data.txt b/language_learning/python/2020.08.29_several_python_functions/read_txt_file/1D_data.txt
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/read_txt_file/1D_data.txt
rename to language_learning/python/2020.08.29_several_python_functions/read_txt_file/1D_data.txt
diff --git a/language_learning/2020.08.29_several_python_functions/read_txt_file/2D_data.txt b/language_learning/python/2020.08.29_several_python_functions/read_txt_file/2D_data.txt
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/read_txt_file/2D_data.txt
rename to language_learning/python/2020.08.29_several_python_functions/read_txt_file/2D_data.txt
diff --git a/language_learning/2020.08.29_several_python_functions/read_txt_file/read_1D_data.py b/language_learning/python/2020.08.29_several_python_functions/read_txt_file/read_1D_data.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/read_txt_file/read_1D_data.py
rename to language_learning/python/2020.08.29_several_python_functions/read_txt_file/read_1D_data.py
diff --git a/language_learning/2020.08.29_several_python_functions/read_txt_file/read_2D_data.py b/language_learning/python/2020.08.29_several_python_functions/read_txt_file/read_2D_data.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/read_txt_file/read_2D_data.py
rename to language_learning/python/2020.08.29_several_python_functions/read_txt_file/read_2D_data.py
diff --git a/language_learning/2020.08.29_several_python_functions/running_and_write_2D_data_into_txt_file.py b/language_learning/python/2020.08.29_several_python_functions/running_and_write_2D_data_into_txt_file.py
similarity index 100%
rename from language_learning/2020.08.29_several_python_functions/running_and_write_2D_data_into_txt_file.py
rename to language_learning/python/2020.08.29_several_python_functions/running_and_write_2D_data_into_txt_file.py
diff --git a/language_learning/2020.10.17_web_scraping_with_BeautifulSoup/BeautifulSoup.py b/language_learning/python/2020.10.17_web_scraping_with_BeautifulSoup/BeautifulSoup.py
similarity index 100%
rename from language_learning/2020.10.17_web_scraping_with_BeautifulSoup/BeautifulSoup.py
rename to language_learning/python/2020.10.17_web_scraping_with_BeautifulSoup/BeautifulSoup.py
diff --git a/language_learning/2020.10.31_download_references_in_a_pdf_file_with_python/download_references_in_a_pdf_file_with_python.py b/language_learning/python/2020.10.31_download_references_in_a_pdf_file_with_python/download_references_in_a_pdf_file_with_python.py
similarity index 100%
rename from language_learning/2020.10.31_download_references_in_a_pdf_file_with_python/download_references_in_a_pdf_file_with_python.py
rename to language_learning/python/2020.10.31_download_references_in_a_pdf_file_with_python/download_references_in_a_pdf_file_with_python.py
diff --git a/language_learning/2020.10.31_download_references_in_a_pdf_file_with_python/get_links_from_a_pdf_file.py b/language_learning/python/2020.10.31_download_references_in_a_pdf_file_with_python/get_links_from_a_pdf_file.py
similarity index 100%
rename from language_learning/2020.10.31_download_references_in_a_pdf_file_with_python/get_links_from_a_pdf_file.py
rename to language_learning/python/2020.10.31_download_references_in_a_pdf_file_with_python/get_links_from_a_pdf_file.py
diff --git a/language_learning/2020.11.25_academic_words/download_academic_word_mp3.py b/language_learning/python/2020.11.25_academic_words/download_academic_word_mp3.py
similarity index 100%
rename from language_learning/2020.11.25_academic_words/download_academic_word_mp3.py
rename to language_learning/python/2020.11.25_academic_words/download_academic_word_mp3.py
diff --git a/language_learning/2020.11.25_academic_words/play_academic_words_with_guan.py b/language_learning/python/2020.11.25_academic_words/play_academic_words_with_guan.py
similarity index 100%
rename from language_learning/2020.11.25_academic_words/play_academic_words_with_guan.py
rename to language_learning/python/2020.11.25_academic_words/play_academic_words_with_guan.py
diff --git a/language_learning/2020.12.23_plot_with_matplotlib/plot_2D_scatter.py b/language_learning/python/2020.12.23_plot_with_matplotlib/plot_2D_scatter.py
similarity index 100%
rename from language_learning/2020.12.23_plot_with_matplotlib/plot_2D_scatter.py
rename to language_learning/python/2020.12.23_plot_with_matplotlib/plot_2D_scatter.py
diff --git a/language_learning/2020.12.23_plot_with_matplotlib/plot_3D_scatter.py b/language_learning/python/2020.12.23_plot_with_matplotlib/plot_3D_scatter.py
similarity index 100%
rename from language_learning/2020.12.23_plot_with_matplotlib/plot_3D_scatter.py
rename to language_learning/python/2020.12.23_plot_with_matplotlib/plot_3D_scatter.py
diff --git a/language_learning/2020.12.23_plot_with_matplotlib/plot_3D_surface.py b/language_learning/python/2020.12.23_plot_with_matplotlib/plot_3D_surface.py
similarity index 100%
rename from language_learning/2020.12.23_plot_with_matplotlib/plot_3D_surface.py
rename to language_learning/python/2020.12.23_plot_with_matplotlib/plot_3D_surface.py
diff --git a/language_learning/2020.12.23_plot_with_matplotlib/plot_contour.py b/language_learning/python/2020.12.23_plot_with_matplotlib/plot_contour.py
similarity index 100%
rename from language_learning/2020.12.23_plot_with_matplotlib/plot_contour.py
rename to language_learning/python/2020.12.23_plot_with_matplotlib/plot_contour.py
diff --git a/language_learning/2020.12.23_plot_with_matplotlib/plot_line.py b/language_learning/python/2020.12.23_plot_with_matplotlib/plot_line.py
similarity index 100%
rename from language_learning/2020.12.23_plot_with_matplotlib/plot_line.py
rename to language_learning/python/2020.12.23_plot_with_matplotlib/plot_line.py
diff --git a/language_learning/2021.01.13_python_code_for_data_processing/python_code_for_data_processing.py b/language_learning/python/2021.01.13_python_code_for_data_processing/python_code_for_data_processing.py
similarity index 100%
rename from language_learning/2021.01.13_python_code_for_data_processing/python_code_for_data_processing.py
rename to language_learning/python/2021.01.13_python_code_for_data_processing/python_code_for_data_processing.py
diff --git a/language_learning/2021.01.18_run_programs_sequentially/run_programs_sequentially.py b/language_learning/python/2021.01.18_run_programs_sequentially/run_programs_sequentially.py
old mode 100755
new mode 100644
similarity index 96%
rename from language_learning/2021.01.18_run_programs_sequentially/run_programs_sequentially.py
rename to language_learning/python/2021.01.18_run_programs_sequentially/run_programs_sequentially.py
index f00514d..ae2f566
--- a/language_learning/2021.01.18_run_programs_sequentially/run_programs_sequentially.py
+++ b/language_learning/python/2021.01.18_run_programs_sequentially/run_programs_sequentially.py
@@ -1,21 +1,21 @@
-import os
-import time
-
-start = time.time()
-
-print('程序1开始的时间：', time.ctime())
-start1 = time.time()
-os.chdir('D:') # 代码位置
-os.system('python a.py') # 运行a.py
-end1 = time.time()
-print('程序1运行时间(min)=', (end1-start1)/60,'\n')
-
-print('程序2开始的时间：', time.ctime())
-start2 = time.time()
-os.chdir('E:') # 代码位置
-os.system('python b.py') # 运行b.py
-end2 = time.time()
-print('程序2运行时间(min)=', (end2-start2)/60, '\n')
-
-end = time.time()
+import os
+import time
+
+start = time.time()
+
+print('程序1开始的时间：', time.ctime())
+start1 = time.time()
+os.chdir('D:') # 代码位置
+os.system('python a.py') # 运行a.py
+end1 = time.time()
+print('程序1运行时间(min)=', (end1-start1)/60,'\n')
+
+print('程序2开始的时间：', time.ctime())
+start2 = time.time()
+os.chdir('E:') # 代码位置
+os.system('python b.py') # 运行b.py
+end2 = time.time()
+print('程序2运行时间(min)=', (end2-start2)/60, '\n')
+
+end = time.time()
print('总运行时间(min)=', (end-start)/60)
\ No newline at end of file
diff --git a/language_learning/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/find_key_words_in_pdf_files.py b/language_learning/python/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/find_key_words_in_pdf_files.py
old mode 100755
new mode 100644
similarity index 97%
rename from language_learning/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/find_key_words_in_pdf_files.py
rename to language_learning/python/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/find_key_words_in_pdf_files.py
index 4018d44..affae8f
--- a/language_learning/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/find_key_words_in_pdf_files.py
+++ b/language_learning/python/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/find_key_words_in_pdf_files.py
@@ -1,137 +1,137 @@
-"""
-This code is supported by the website: https://www.guanjihuan.com
-The newest version of this code is on the web page: https://www.guanjihuan.com/archives/9129
-"""
-
-import os
-import re
-import time
-import logging
-logging.Logger.propagate = False
-logging.getLogger().setLevel(logging.ERROR) # 只显示error级别的通知
-
-
-def main():
- # 参数
- key_word_array = ['photonic', 'Berry phase']
- original_path = 'D:\\文献'
-
- # 查找所有的PDF文件路径
- pdf_file_all = find_files_pdf(original_path)
- print('\n该文件夹下总共有', len(pdf_file_all), '个PDF文件。\n')
-
- f = open('error.txt','w',encoding='utf-8')
- f.close()
- for key_word in key_word_array:
- f = open(str(key_word)+'.txt','w',encoding='utf-8')
- f.write('该文件夹下总共有'+str(len(pdf_file_all))+'个PDF文件。\n')
- f.close()
-
- # 查找包含关键词的PDF文件
- i0 = 1
- begin = time.time()
- for pdf_file in pdf_file_all:
- print('查找第', i0, '个文件，', end='')
- begin0 = time.time()
- try:
- content = get_text_from_pdf(pdf_file)
- for key_word in key_word_array:
- if re.search(re.compile(key_word),content):
- print('发现文件！关键词', key_word, '对应的文件位置在：\n\n', pdf_file, '\n')
- with open(str(key_word)+'.txt','a',encoding='utf-8') as f:
- f.write('\n查找第'+str(i0)+'个文件时发现文件！位置在：\n'+pdf_file+'\n')
- except:
- print('出现异常！位置在：\n\n', pdf_file, '\n')
- with open('error.txt','a',encoding='utf-8') as f:
- f.write('\n解析第'+str(i0)+'个文件时出现异常！位置在：\n'+pdf_file+'\n')
- end0 = time.time()
- print('用时', end0-begin0, '秒')
- i0 += 1
- print('\n全部搜索结束！')
- end = time.time()
- print('\n总共用时：', (end-begin)/60, '分')
-
-
-def find_files_pdf(path): # 查找所有PDF文件
- file_all = find_files(path)
- pdf_file_all = []
- for file0 in file_all:
- if re.search(re.compile('^fdp.'),file0[::-1]): # 如果文件是以.pdf结尾
- pdf_file_all.append(file0)
- return pdf_file_all
-
-
-def find_files(path): # 查找所有文件
- file_all = []
- path_next_loop = [path]
- for i in range(10000): # i为文件在文件夹中的深度
- file_all_in_one_loop, path_next_loop = find_files_loop_module(path_next_loop)
- for file_in_one_loop in file_all_in_one_loop:
- file_all.append(file_in_one_loop)
- if path_next_loop == []:
- break
- return file_all
-
-
-def find_files_loop_module(path_all): # 查找文件的一个循环模块
- file_all_in_one_loop = []
- path_next_loop = []
- for path in path_all:
- filenames = os.listdir(path)
- for filename in filenames:
- filename = os.path.join(path,filename)
- if os.path.isfile(filename): # 如果是文件
- file_all_in_one_loop.append(filename)
- else: # 如果是文件夹
- path_next_loop.append(filename)
- return file_all_in_one_loop, path_next_loop
-
-
-def get_text_from_pdf(file_path): # 从PDF中获取文本
- from pdfminer.pdfparser import PDFParser, PDFDocument
- from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
- from pdfminer.converter import PDFPageAggregator
- from pdfminer.layout import LAParams, LTTextBox
- from pdfminer.pdfinterp import PDFTextExtractionNotAllowed
-
- # 用文件对象来创建一个pdf文档分析器
- praser = PDFParser(open(file_path, 'rb'))
- # 创建一个PDF文档
- doc = PDFDocument()
- # 连接分析器 与文档对象
- praser.set_document(doc)
- doc.set_parser(praser)
-
- # 提供初始化密码
- # 如果没有密码 就创建一个空的字符串
- doc.initialize()
-
- # 检测文档是否提供txt转换，不提供就忽略
- if not doc.is_extractable:
- raise PDFTextExtractionNotAllowed
- else:
- # 创建PDf 资源管理器 来管理共享资源
- rsrcmgr = PDFResourceManager()
- # 创建一个PDF设备对象
- laparams = LAParams()
- device = PDFPageAggregator(rsrcmgr, laparams=laparams)
- # 创建一个PDF解释器对象
- interpreter = PDFPageInterpreter(rsrcmgr, device)
-
- # 循环遍历列表，每次处理一个page的内容
- content = ''
- for page in doc.get_pages():
- interpreter.process_page(page)
- # 接受该页面的LTPage对象
- layout = device.get_result()
- # 这里layout是一个LTPage对象，里面存放着这个 page 解析出的各种对象
- # 包括 LTTextBox, LTFigure, LTImage, LTTextBoxHorizontal 等
- for x in layout:
- if isinstance(x, LTTextBox):
- # print(x.get_text().strip())
- content = content + x.get_text().strip()
- return content
-
-
-if __name__ == "__main__":
+"""
+This code is supported by the website: https://www.guanjihuan.com
+The newest version of this code is on the web page: https://www.guanjihuan.com/archives/9129
+"""
+
+import os
+import re
+import time
+import logging
+logging.Logger.propagate = False
+logging.getLogger().setLevel(logging.ERROR) # 只显示error级别的通知
+
+
+def main():
+ # 参数
+ key_word_array = ['photonic', 'Berry phase']
+ original_path = 'D:\\文献'
+
+ # 查找所有的PDF文件路径
+ pdf_file_all = find_files_pdf(original_path)
+ print('\n该文件夹下总共有', len(pdf_file_all), '个PDF文件。\n')
+
+ f = open('error.txt','w',encoding='utf-8')
+ f.close()
+ for key_word in key_word_array:
+ f = open(str(key_word)+'.txt','w',encoding='utf-8')
+ f.write('该文件夹下总共有'+str(len(pdf_file_all))+'个PDF文件。\n')
+ f.close()
+
+ # 查找包含关键词的PDF文件
+ i0 = 1
+ begin = time.time()
+ for pdf_file in pdf_file_all:
+ print('查找第', i0, '个文件，', end='')
+ begin0 = time.time()
+ try:
+ content = get_text_from_pdf(pdf_file)
+ for key_word in key_word_array:
+ if re.search(re.compile(key_word),content):
+ print('发现文件！关键词', key_word, '对应的文件位置在：\n\n', pdf_file, '\n')
+ with open(str(key_word)+'.txt','a',encoding='utf-8') as f:
+ f.write('\n查找第'+str(i0)+'个文件时发现文件！位置在：\n'+pdf_file+'\n')
+ except:
+ print('出现异常！位置在：\n\n', pdf_file, '\n')
+ with open('error.txt','a',encoding='utf-8') as f:
+ f.write('\n解析第'+str(i0)+'个文件时出现异常！位置在：\n'+pdf_file+'\n')
+ end0 = time.time()
+ print('用时', end0-begin0, '秒')
+ i0 += 1
+ print('\n全部搜索结束！')
+ end = time.time()
+ print('\n总共用时：', (end-begin)/60, '分')
+
+
+def find_files_pdf(path): # 查找所有PDF文件
+ file_all = find_files(path)
+ pdf_file_all = []
+ for file0 in file_all:
+ if re.search(re.compile('^fdp.'),file0[::-1]): # 如果文件是以.pdf结尾
+ pdf_file_all.append(file0)
+ return pdf_file_all
+
+
+def find_files(path): # 查找所有文件
+ file_all = []
+ path_next_loop = [path]
+ for i in range(10000): # i为文件在文件夹中的深度
+ file_all_in_one_loop, path_next_loop = find_files_loop_module(path_next_loop)
+ for file_in_one_loop in file_all_in_one_loop:
+ file_all.append(file_in_one_loop)
+ if path_next_loop == []:
+ break
+ return file_all
+
+
+def find_files_loop_module(path_all): # 查找文件的一个循环模块
+ file_all_in_one_loop = []
+ path_next_loop = []
+ for path in path_all:
+ filenames = os.listdir(path)
+ for filename in filenames:
+ filename = os.path.join(path,filename)
+ if os.path.isfile(filename): # 如果是文件
+ file_all_in_one_loop.append(filename)
+ else: # 如果是文件夹
+ path_next_loop.append(filename)
+ return file_all_in_one_loop, path_next_loop
+
+
+def get_text_from_pdf(file_path): # 从PDF中获取文本
+ from pdfminer.pdfparser import PDFParser, PDFDocument
+ from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
+ from pdfminer.converter import PDFPageAggregator
+ from pdfminer.layout import LAParams, LTTextBox
+ from pdfminer.pdfinterp import PDFTextExtractionNotAllowed
+
+ # 用文件对象来创建一个pdf文档分析器
+ praser = PDFParser(open(file_path, 'rb'))
+ # 创建一个PDF文档
+ doc = PDFDocument()
+ # 连接分析器 与文档对象
+ praser.set_document(doc)
+ doc.set_parser(praser)
+
+ # 提供初始化密码
+ # 如果没有密码 就创建一个空的字符串
+ doc.initialize()
+
+ # 检测文档是否提供txt转换，不提供就忽略
+ if not doc.is_extractable:
+ raise PDFTextExtractionNotAllowed
+ else:
+ # 创建PDf 资源管理器 来管理共享资源
+ rsrcmgr = PDFResourceManager()
+ # 创建一个PDF设备对象
+ laparams = LAParams()
+ device = PDFPageAggregator(rsrcmgr, laparams=laparams)
+ # 创建一个PDF解释器对象
+ interpreter = PDFPageInterpreter(rsrcmgr, device)
+
+ # 循环遍历列表，每次处理一个page的内容
+ content = ''
+ for page in doc.get_pages():
+ interpreter.process_page(page)
+ # 接受该页面的LTPage对象
+ layout = device.get_result()
+ # 这里layout是一个LTPage对象，里面存放着这个 page 解析出的各种对象
+ # 包括 LTTextBox, LTFigure, LTImage, LTTextBoxHorizontal 等
+ for x in layout:
+ if isinstance(x, LTTextBox):
+ # print(x.get_text().strip())
+ content = content + x.get_text().strip()
+ return content
+
+
+if __name__ == "__main__":
main()
\ No newline at end of file
diff --git a/language_learning/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/get_content_in_a_pdf_file.py b/language_learning/python/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/get_content_in_a_pdf_file.py
old mode 100755
new mode 100644
similarity index 97%
rename from language_learning/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/get_content_in_a_pdf_file.py
rename to language_learning/python/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/get_content_in_a_pdf_file.py
index 9508c55..5ff8ad4
--- a/language_learning/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/get_content_in_a_pdf_file.py
+++ b/language_learning/python/2021.01.23_find_key_words_in_pdf_files_with_pdfminer/get_content_in_a_pdf_file.py
@@ -1,63 +1,63 @@
-import os
-os.chdir('D:/') # PDF文件存放的位置
-import logging
-logging.Logger.propagate = False
-logging.getLogger().setLevel(logging.ERROR) # 只显示error级别的通知
-
-
-def main():
- content = get_text_from_pdf('a')
- with open('a.txt', 'w', encoding='utf-8') as f:
- f.write(content)
-
-
-def get_text_from_pdf(filename):
- from pdfminer.pdfparser import PDFParser, PDFDocument
- from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
- from pdfminer.converter import PDFPageAggregator
- from pdfminer.layout import LAParams, LTTextBox
- from pdfminer.pdfinterp import PDFTextExtractionNotAllowed
-
- path = filename+".pdf"
-
- # 用文件对象来创建一个pdf文档分析器
- praser = PDFParser(open(path, 'rb'))
- # 创建一个PDF文档
- doc = PDFDocument()
- # 连接分析器 与文档对象
- praser.set_document(doc)
- doc.set_parser(praser)
-
- # 提供初始化密码
- # 如果没有密码 就创建一个空的字符串
- doc.initialize()
-
- # 检测文档是否提供txt转换，不提供就忽略
- if not doc.is_extractable:
- raise PDFTextExtractionNotAllowed
- else:
- # 创建PDf 资源管理器 来管理共享资源
- rsrcmgr = PDFResourceManager()
- # 创建一个PDF设备对象
- laparams = LAParams()
- device = PDFPageAggregator(rsrcmgr, laparams=laparams)
- # 创建一个PDF解释器对象
- interpreter = PDFPageInterpreter(rsrcmgr, device)
-
- # 循环遍历列表，每次处理一个page的内容
- content = ''
- for page in doc.get_pages():
- interpreter.process_page(page)
- # 接受该页面的LTPage对象
- layout = device.get_result()
- # 这里layout是一个LTPage对象，里面存放着这个 page 解析出的各种对象
- # 包括 LTTextBox, LTFigure, LTImage, LTTextBoxHorizontal 等
- for x in layout:
- if isinstance(x, LTTextBox):
- # print(x.get_text().strip())
- content = content + x.get_text().strip()
- return content
-
-
-if __name__ == "__main__":
+import os
+os.chdir('D:/') # PDF文件存放的位置
+import logging
+logging.Logger.propagate = False
+logging.getLogger().setLevel(logging.ERROR) # 只显示error级别的通知
+
+
+def main():
+ content = get_text_from_pdf('a')
+ with open('a.txt', 'w', encoding='utf-8') as f:
+ f.write(content)
+
+
+def get_text_from_pdf(filename):
+ from pdfminer.pdfparser import PDFParser, PDFDocument
+ from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter
+ from pdfminer.converter import PDFPageAggregator
+ from pdfminer.layout import LAParams, LTTextBox
+ from pdfminer.pdfinterp import PDFTextExtractionNotAllowed
+
+ path = filename+".pdf"
+
+ # 用文件对象来创建一个pdf文档分析器
+ praser = PDFParser(open(path, 'rb'))
+ # 创建一个PDF文档
+ doc = PDFDocument()
+ # 连接分析器 与文档对象
+ praser.set_document(doc)
+ doc.set_parser(praser)
+
+ # 提供初始化密码
+ # 如果没有密码 就创建一个空的字符串
+ doc.initialize()
+
+ # 检测文档是否提供txt转换，不提供就忽略
+ if not doc.is_extractable:
+ raise PDFTextExtractionNotAllowed
+ else:
+ # 创建PDf 资源管理器 来管理共享资源
+ rsrcmgr = PDFResourceManager()
+ # 创建一个PDF设备对象
+ laparams = LAParams()
+ device = PDFPageAggregator(rsrcmgr, laparams=laparams)
+ # 创建一个PDF解释器对象
+ interpreter = PDFPageInterpreter(rsrcmgr, device)
+
+ # 循环遍历列表，每次处理一个page的内容
+ content = ''
+ for page in doc.get_pages():
+ interpreter.process_page(page)
+ # 接受该页面的LTPage对象
+ layout = device.get_result()
+ # 这里layout是一个LTPage对象，里面存放着这个 page 解析出的各种对象
+ # 包括 LTTextBox, LTFigure, LTImage, LTTextBoxHorizontal 等
+ for x in layout:
+ if isinstance(x, LTTextBox):
+ # print(x.get_text().strip())
+ content = content + x.get_text().strip()
+ return content
+
+
+if __name__ == "__main__":
main()
\ No newline at end of file
diff --git a/language_learning/2021.03.18_element_words/download_element_word_mp3.py b/language_learning/python/2021.03.18_element_words/download_element_word_mp3.py
similarity index 100%
rename from language_learning/2021.03.18_element_words/download_element_word_mp3.py
rename to language_learning/python/2021.03.18_element_words/download_element_word_mp3.py
diff --git a/language_learning/2021.03.18_element_words/play_element_words_with_guan.py b/language_learning/python/2021.03.18_element_words/play_element_words_with_guan.py
similarity index 100%
rename from language_learning/2021.03.18_element_words/play_element_words_with_guan.py
rename to language_learning/python/2021.03.18_element_words/play_element_words_with_guan.py
diff --git a/language_learning/2021.05.10_parallel_calculations_using_sh_files/a.py b/language_learning/python/2021.05.10_parallel_calculations_using_sh_files/a.py
old mode 100755
new mode 100644
similarity index 100%
rename from language_learning/2021.05.10_parallel_calculations_using_sh_files/a.py
rename to language_learning/python/2021.05.10_parallel_calculations_using_sh_files/a.py
diff --git a/language_learning/2021.05.10_parallel_calculations_using_sh_files/a.sh b/language_learning/python/2021.05.10_parallel_calculations_using_sh_files/a.sh
old mode 100755
new mode 100644
similarity index 100%
rename from language_learning/2021.05.10_parallel_calculations_using_sh_files/a.sh
rename to language_learning/python/2021.05.10_parallel_calculations_using_sh_files/a.sh
diff --git a/language_learning/2021.05.10_parallel_calculations_using_sh_files/combine.py b/language_learning/python/2021.05.10_parallel_calculations_using_sh_files/combine.py
old mode 100755
new mode 100644
similarity index 96%
rename from language_learning/2021.05.10_parallel_calculations_using_sh_files/combine.py
rename to language_learning/python/2021.05.10_parallel_calculations_using_sh_files/combine.py
index 75a185c..bce8497
--- a/language_learning/2021.05.10_parallel_calculations_using_sh_files/combine.py
+++ b/language_learning/python/2021.05.10_parallel_calculations_using_sh_files/combine.py
@@ -1,6 +1,6 @@
-f = open('combine.txt', 'w')
-for job_index in range(7):
- with open('a'+str(job_index)+'.txt', 'r') as f0:
- text = f0.read()
- f.write(text)
+f = open('combine.txt', 'w')
+for job_index in range(7):
+ with open('a'+str(job_index)+'.txt', 'r') as f0:
+ text = f0.read()
+ f.write(text)
f.close()
\ No newline at end of file
diff --git a/language_learning/2021.05.10_parallel_calculations_using_sh_files/task.sh b/language_learning/python/2021.05.10_parallel_calculations_using_sh_files/task.sh
old mode 100755
new mode 100644
similarity index 100%
rename from language_learning/2021.05.10_parallel_calculations_using_sh_files/task.sh
rename to language_learning/python/2021.05.10_parallel_calculations_using_sh_files/task.sh
diff --git a/language_learning/2021.06.07_find_common_words_in_APS_abstracts/collect_prb_abstracts.py b/language_learning/python/2021.06.07_find_common_words_in_APS_abstracts/collect_prb_abstracts.py
similarity index 100%
rename from language_learning/2021.06.07_find_common_words_in_APS_abstracts/collect_prb_abstracts.py
rename to language_learning/python/2021.06.07_find_common_words_in_APS_abstracts/collect_prb_abstracts.py
diff --git a/language_learning/2021.06.07_find_common_words_in_APS_abstracts/collect_prl_abstracts.py b/language_learning/python/2021.06.07_find_common_words_in_APS_abstracts/collect_prl_abstracts.py
similarity index 100%
rename from language_learning/2021.06.07_find_common_words_in_APS_abstracts/collect_prl_abstracts.py
rename to language_learning/python/2021.06.07_find_common_words_in_APS_abstracts/collect_prl_abstracts.py
diff --git a/language_learning/2021.06.07_find_common_words_in_APS_abstracts/count_words.py b/language_learning/python/2021.06.07_find_common_words_in_APS_abstracts/count_words.py
similarity index 100%
rename from language_learning/2021.06.07_find_common_words_in_APS_abstracts/count_words.py
rename to language_learning/python/2021.06.07_find_common_words_in_APS_abstracts/count_words.py
diff --git a/language_learning/2021.06.24_sympy_example/sympy_example.py b/language_learning/python/2021.06.24_sympy_example/sympy_example.py
similarity index 100%
rename from language_learning/2021.06.24_sympy_example/sympy_example.py
rename to language_learning/python/2021.06.24_sympy_example/sympy_example.py
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