update_showing_all_guan_functions

academic_codes/2021.04.24_Hofstadter_butterfly_of_graphene_ribbon/Hofstadter_butterfly_of_graphene_ribbon.py

@@ -7,7 +7,6 @@ import numpy as np
 from math import *  
 import cmath  
 import functools 
-import guan
 
 def hamiltonian(B, k, N, M, t1, a):  # graphene哈密顿量（N是条带的宽度参数）
     # 初始化为零矩阵
@@ -43,9 +42,58 @@ def main():
     a = 1
     hamiltonian_function0 = functools.partial(hamiltonian, k=0, N=N, M=0, t1=1, a=a)
     B_array = np.linspace(0, 1/(3*np.sqrt(3)/2*a*a), 100)
-    eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(B_array, hamiltonian_function0)
     BS_array = B_array*(3*np.sqrt(3)/2*a*a)
-    guan.plot(BS_array, eigenvalue_array, xlabel='Flux (BS/phi_0)', ylabel='E', title='Ny=%i'%N, filename='a', show=1, save=0, style='k.', y_min=None, y_max=None, markersize=3)
+    eigenvalue_array = calculate_eigenvalue_with_one_parameter(B_array, hamiltonian_function0)
+    plot(BS_array, eigenvalue_array, xlabel='Flux (BS/phi_0)', ylabel='E', title='Ny=%i'%N, filename='a', show=1, save=0, style='k.', y_min=None, y_max=None, markersize=3)
+    
+    # import guan
+    # eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(B_array, hamiltonian_function0)
+    # guan.plot(BS_array, eigenvalue_array, xlabel='Flux (BS/phi_0)', ylabel='E', title='Ny=%i'%N, filename='a', show=1, save=0, style='k.', y_min=None, y_max=None, markersize=3)
+
+def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print_show=0):
+    dim_x = np.array(x_array).shape[0]
+    i0 = 0
+    if np.array(hamiltonian_function(0)).shape==():
+        eigenvalue_array = np.zeros((dim_x, 1))
+        for x0 in x_array:
+            hamiltonian = hamiltonian_function(x0)
+            eigenvalue_array[i0, 0] = np.real(hamiltonian)
+            i0 += 1
+    else:
+        dim = np.array(hamiltonian_function(0)).shape[0]
+        eigenvalue_array = np.zeros((dim_x, dim))
+        for x0 in x_array:
+            if print_show==1:
+                print(x0)
+            hamiltonian = hamiltonian_function(x0)
+            eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+            eigenvalue_array[i0, :] = eigenvalue
+            i0 += 1
+    return eigenvalue_array
+
+def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', format='jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2): 
+    import matplotlib.pyplot as plt
+    fig, ax = plt.subplots()
+    plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left) 
+    ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
+    ax.grid()
+    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') 
+    if y_min!=None or y_max!=None:
+        if y_min==None:
+            y_min=min(y_array)
+        if y_max==None:
+            y_max=max(y_array)
+        ax.set_ylim(y_min, y_max)
+    ax.tick_params(labelsize=labelsize) 
+    labels = ax.get_xticklabels() + ax.get_yticklabels()
+    [label.set_fontname('Times New Roman') for label in labels]
+    if save == 1:
+        plt.savefig(filename+'.'+format, dpi=dpi) 
+    if show == 1:
+        plt.show()
+    plt.close('all')
 
 if __name__ == '__main__':
     main()

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

@@ -0,0 +1,81 @@
+"""
+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/16199
+"""
+
+import numpy as np
+from math import *
+import cmath
+
+def hamiltonian_1(k, 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 hamiltonian_2(k, v=0.6, w=1):
+    matrix = np.zeros((2, 2), dtype=complex)
+    matrix[0,1] = v*cmath.exp(1j*k/2)+w*cmath.exp(-1j*k/2)
+    matrix[1,0] = v*cmath.exp(-1j*k/2)+w*cmath.exp(1j*k/2)
+    return matrix
+
+def main():
+    k_array = np.linspace(-pi ,pi, 100)
+    E_1_array = calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_1)
+    plot(k_array, E_1_array, xlabel='k', ylabel='E_1')
+    E_2_array = calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_2)
+    plot(k_array, E_2_array, xlabel='k', ylabel='E_2')
+
+    # import guan
+    # E_1_array = guan.calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_1)
+    # guan.plot(k_array, E_1_array, xlabel='k', ylabel='E_1')
+    # E_2_array = guan.calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_2)
+    # guan.plot(k_array, E_2_array, xlabel='k', ylabel='E_2')
+
+def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print_show=0):
+    dim_x = np.array(x_array).shape[0]
+    i0 = 0
+    if np.array(hamiltonian_function(0)).shape==():
+        eigenvalue_array = np.zeros((dim_x, 1))
+        for x0 in x_array:
+            hamiltonian = hamiltonian_function(x0)
+            eigenvalue_array[i0, 0] = np.real(hamiltonian)
+            i0 += 1
+    else:
+        dim = np.array(hamiltonian_function(0)).shape[0]
+        eigenvalue_array = np.zeros((dim_x, dim))
+        for x0 in x_array:
+            if print_show==1:
+                print(x0)
+            hamiltonian = hamiltonian_function(x0)
+            eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+            eigenvalue_array[i0, :] = eigenvalue
+            i0 += 1
+    return eigenvalue_array
+
+def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', format='jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2): 
+    import matplotlib.pyplot as plt
+    fig, ax = plt.subplots()
+    plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left) 
+    ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
+    ax.grid()
+    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') 
+    if y_min!=None or y_max!=None:
+        if y_min==None:
+            y_min=min(y_array)
+        if y_max==None:
+            y_max=max(y_array)
+        ax.set_ylim(y_min, y_max)
+    ax.tick_params(labelsize=labelsize) 
+    labels = ax.get_xticklabels() + ax.get_yticklabels()
+    [label.set_fontname('Times New Roman') for label in labels]
+    if save == 1:
+        plt.savefig(filename+'.'+format, dpi=dpi) 
+    if show == 1:
+        plt.show()
+    plt.close('all')
+
+if __name__ == '__main__':
+    main()

academic_codes/2021.07.29_bands_of_SSH_model_with_two_kinds_of_fourier_transform/bands_of_SSH_model_with_two_kinds_of_fourier_transform.py

@@ -1,31 +0,0 @@
-"""
-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/16199
-"""
-
-import numpy as np
-from math import *
-import cmath
-import guan
-
-v=0.6
-w=1
-k_array = np.linspace(-pi ,pi, 100)
-
-def hamiltonian_1(k):
-    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 hamiltonian_2(k):
-    matrix = np.zeros((2, 2), dtype=complex)
-    matrix[0,1] = v*cmath.exp(1j*k/2)+w*cmath.exp(-1j*k/2)
-    matrix[1,0] = v*cmath.exp(-1j*k/2)+w*cmath.exp(1j*k/2)
-    return matrix
-
-E_1_array = guan.calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_1)
-guan.plot(k_array, E_1_array, xlabel='k', ylabel='E_1')
-
-E_2_array = guan.calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_2)
-guan.plot(k_array, E_2_array, xlabel='k', ylabel='E_2')

academic_codes/2021.08.09_flat_bands_of_kagome lattice/flat_bands_of_kagome lattice.py

@@ -5,7 +5,6 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a
 
 import numpy as np
 from math import *
-import guan
 
 def hamiltonian(kx, ky):  # kagome lattice
     k1_dot_a1 = kx
@@ -20,7 +19,85 @@ def hamiltonian(kx, ky):  # kagome lattice
     h = -t*h
     return h
 
-kx_array = np.linspace(-pi ,pi, 500)
+def main():
-ky_array = np.linspace(-pi ,pi, 500)
+    kx_array = np.linspace(-pi ,pi, 500)
-eigenvalue_array = guan.calculate_eigenvalue_with_two_parameters(kx_array, ky_array, hamiltonian)
+    ky_array = np.linspace(-pi ,pi, 500)
-guan.plot_3d_surface(kx_array, ky_array, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', rcount=200, ccount=200)
+    eigenvalue_array = calculate_eigenvalue_with_two_parameters(kx_array, ky_array, hamiltonian)
+    plot_3d_surface(kx_array, ky_array, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', rcount=200, ccount=200)
+    
+    # import guan
+    # eigenvalue_array = guan.calculate_eigenvalue_with_two_parameters(kx_array, ky_array, hamiltonian)
+    # guan.plot_3d_surface(kx_array, ky_array, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', rcount=200, ccount=200)
+
+def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_function, print_show=0, print_show_more=0):  
+    dim_x = np.array(x_array).shape[0]
+    dim_y = np.array(y_array).shape[0]
+    if np.array(hamiltonian_function(0,0)).shape==():
+        eigenvalue_array = np.zeros((dim_y, dim_x, 1))
+        i0 = 0
+        for y0 in y_array:
+            j0 = 0
+            for x0 in x_array:
+                hamiltonian = hamiltonian_function(x0, y0)
+                eigenvalue_array[i0, j0, 0] = np.real(hamiltonian)
+                j0 += 1
+            i0 += 1
+    else:
+        dim = np.array(hamiltonian_function(0, 0)).shape[0]
+        eigenvalue_array = np.zeros((dim_y, dim_x, dim))
+        i0 = 0
+        for y0 in y_array:
+            j0 = 0
+            if print_show==1:
+                print(y0)
+            for x0 in x_array:
+                if print_show_more==1:
+                    print(x0)
+                hamiltonian = hamiltonian_function(x0, y0)
+                eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+                eigenvalue_array[i0, j0, :] = eigenvalue
+                j0 += 1
+            i0 += 1
+    return eigenvalue_array
+
+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', 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+'.'+format, dpi=dpi) 
+    if show == 1:
+        plt.show()
+    plt.close('all')
+
+if __name__ == '__main__':
+    main()

academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_Wilson_loop.py

@@ -6,7 +6,6 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a
 import numpy as np
 import cmath
 from math import *
-import guan
 
 def hamiltonian(kx, ky):  # BBH model
     # label of atoms in a unit cell
@@ -61,7 +60,33 @@ def main():
                 nu_x[i0] += 1
         nu_x = np.sort(nu_x)
         nu_x_array.append(nu_x.real)
-    guan.plot(ky_array, nu_x_array, xlabel='ky', ylabel='nu_x', style='-', y_min=0, y_max=1)
+    plot(ky_array, nu_x_array, xlabel='ky', ylabel='nu_x', style='-', y_min=0, y_max=1)
+    # import guan
+    # guan.plot(ky_array, nu_x_array, xlabel='ky', ylabel='nu_x', style='-', y_min=0, y_max=1)
+
+def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', format='jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2): 
+    import matplotlib.pyplot as plt
+    fig, ax = plt.subplots()
+    plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left) 
+    ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
+    ax.grid()
+    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') 
+    if y_min!=None or y_max!=None:
+        if y_min==None:
+            y_min=min(y_array)
+        if y_max==None:
+            y_max=max(y_array)
+        ax.set_ylim(y_min, y_max)
+    ax.tick_params(labelsize=labelsize) 
+    labels = ax.get_xticklabels() + ax.get_yticklabels()
+    [label.set_fontname('Times New Roman') for label in labels]
+    if save == 1:
+        plt.savefig(filename+'.'+format, dpi=dpi) 
+    if show == 1:
+        plt.show()
+    plt.close('all')
 
 if __name__ == '__main__':
     main()

academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_bands.py

@@ -7,7 +7,6 @@ import numpy as np
 import cmath
 from math import *
 import functools
-import guan
 
 def hamiltonian(kx, ky):  # BBH model
     # label of atoms in a unit cell
@@ -32,13 +31,133 @@ def hamiltonian(kx, ky):  # BBH model
 def main():
     kx = np.arange(-pi, pi, 0.05)
     ky = np.arange(-pi, pi, 0.05)
-
+    eigenvalue_array = calculate_eigenvalue_with_two_parameters(kx, ky, hamiltonian)
-    eigenvalue_array = guan.calculate_eigenvalue_with_two_parameters(kx, ky, hamiltonian)
+    plot_3d_surface(kx, ky, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', title='BBH bands')
-    guan.plot_3d_surface(kx, ky, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', title='BBH bands')
-
     hamiltonian0 = functools.partial(hamiltonian, ky=0) 
-    eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(kx, hamiltonian0)
+    eigenvalue_array = calculate_eigenvalue_with_one_parameter(kx, hamiltonian0)
-    guan.plot(kx, eigenvalue_array, xlabel='kx', ylabel='E', title='BBH bands ky=0')
+    plot(kx, eigenvalue_array, xlabel='kx', ylabel='E', title='BBH bands ky=0')
+
+    # import guan
+    # eigenvalue_array = guan.calculate_eigenvalue_with_two_parameters(kx, ky, hamiltonian)
+    # guan.plot_3d_surface(kx, ky, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', title='BBH bands')
+    # hamiltonian0 = functools.partial(hamiltonian, ky=0) 
+    # eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(kx, hamiltonian0)
+    # guan.plot(kx, eigenvalue_array, xlabel='kx', ylabel='E', title='BBH bands ky=0')
+
+def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print_show=0):
+    dim_x = np.array(x_array).shape[0]
+    i0 = 0
+    if np.array(hamiltonian_function(0)).shape==():
+        eigenvalue_array = np.zeros((dim_x, 1))
+        for x0 in x_array:
+            hamiltonian = hamiltonian_function(x0)
+            eigenvalue_array[i0, 0] = np.real(hamiltonian)
+            i0 += 1
+    else:
+        dim = np.array(hamiltonian_function(0)).shape[0]
+        eigenvalue_array = np.zeros((dim_x, dim))
+        for x0 in x_array:
+            if print_show==1:
+                print(x0)
+            hamiltonian = hamiltonian_function(x0)
+            eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+            eigenvalue_array[i0, :] = eigenvalue
+            i0 += 1
+    return eigenvalue_array
+
+def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_function, print_show=0, print_show_more=0):  
+    dim_x = np.array(x_array).shape[0]
+    dim_y = np.array(y_array).shape[0]
+    if np.array(hamiltonian_function(0,0)).shape==():
+        eigenvalue_array = np.zeros((dim_y, dim_x, 1))
+        i0 = 0
+        for y0 in y_array:
+            j0 = 0
+            for x0 in x_array:
+                hamiltonian = hamiltonian_function(x0, y0)
+                eigenvalue_array[i0, j0, 0] = np.real(hamiltonian)
+                j0 += 1
+            i0 += 1
+    else:
+        dim = np.array(hamiltonian_function(0, 0)).shape[0]
+        eigenvalue_array = np.zeros((dim_y, dim_x, dim))
+        i0 = 0
+        for y0 in y_array:
+            j0 = 0
+            if print_show==1:
+                print(y0)
+            for x0 in x_array:
+                if print_show_more==1:
+                    print(x0)
+                hamiltonian = hamiltonian_function(x0, y0)
+                eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
+                eigenvalue_array[i0, j0, :] = eigenvalue
+                j0 += 1
+            i0 += 1
+    return eigenvalue_array
+
+def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', format='jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2): 
+    import matplotlib.pyplot as plt
+    fig, ax = plt.subplots()
+    plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left) 
+    ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
+    ax.grid()
+    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') 
+    if y_min!=None or y_max!=None:
+        if y_min==None:
+            y_min=min(y_array)
+        if y_max==None:
+            y_max=max(y_array)
+        ax.set_ylim(y_min, y_max)
+    ax.tick_params(labelsize=labelsize) 
+    labels = ax.get_xticklabels() + ax.get_yticklabels()
+    [label.set_fontname('Times New Roman') for label in labels]
+    if save == 1:
+        plt.savefig(filename+'.'+format, dpi=dpi) 
+    if show == 1:
+        plt.show()
+    plt.close('all')
+
+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', 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+'.'+format, dpi=dpi) 
+    if show == 1:
+        plt.show()
+    plt.close('all')
 
 if __name__ == '__main__':
     main()

academic_codes/2021.12.09_nested_Wilson_loop_of_BBH_model/BBH_nested_Wilson_loop.py

@@ -6,7 +6,6 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a
 import numpy as np
 import cmath
 from math import *
-import guan
 
 def hamiltonian(kx, ky):  # BBH model
     # label of atoms in a unit cell
@@ -81,7 +80,9 @@ def main():
             p_y_for_nu_x += 1
         p_y_for_nu_x_array.append(p_y_for_nu_x.real) 
         print('p_y_for_nu_x=', p_y_for_nu_x)
-    guan.plot(kx2_array, p_y_for_nu_x_array, xlabel='kx', ylabel='p_y_for_nu_x', style='-o', y_min=0, y_max=1)
+    plot(kx2_array, p_y_for_nu_x_array, xlabel='kx', ylabel='p_y_for_nu_x', style='-o', y_min=0, y_max=1)
+    # import guan
+    # guan.plot(kx2_array, p_y_for_nu_x_array, xlabel='kx', ylabel='p_y_for_nu_x', style='-o', y_min=0, y_max=1)
 
     # Part II: calculate p_x_for_nu_y
     p_x_for_nu_y_array = []
@@ -117,7 +118,9 @@ def main():
         p_x_for_nu_y_array.append(p_x_for_nu_y.real)
         print('p_x_for_nu_y=', p_x_for_nu_y)
     # print(sum(p_x_for_nu_y_array)/len(p_x_for_nu_y_array))
-    guan.plot(ky2_array, p_x_for_nu_y_array, xlabel='ky', ylabel='p_x_for_nu_y', style='-o', y_min=0, y_max=1)
+    plot(ky2_array, p_x_for_nu_y_array, xlabel='ky', ylabel='p_x_for_nu_y', style='-o', y_min=0, y_max=1)
+    # import guan
+    # guan.plot(ky2_array, p_x_for_nu_y_array, xlabel='ky', ylabel='p_x_for_nu_y', style='-o', y_min=0, y_max=1)
 
 def get_nu_x_vector(kx_array, ky):
     Num_kx = len(kx_array)
@@ -171,5 +174,29 @@ def get_nu_y_vector(kx, ky_array):
     nu_y_vector_2 = eigenvector[:, np.argsort(np.real(nu_y))[1]]
     return nu_y_vector_1, nu_y_vector_2
 
+def plot(x_array, y_array, xlabel='x', ylabel='y', title='', fontsize=20, labelsize=20, show=1, save=0, filename='a', format='jpg', dpi=300, style='', y_min=None, y_max=None, linewidth=None, markersize=None, adjust_bottom=0.2, adjust_left=0.2): 
+    import matplotlib.pyplot as plt
+    fig, ax = plt.subplots()
+    plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left) 
+    ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
+    ax.grid()
+    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') 
+    if y_min!=None or y_max!=None:
+        if y_min==None:
+            y_min=min(y_array)
+        if y_max==None:
+            y_max=max(y_array)
+        ax.set_ylim(y_min, y_max)
+    ax.tick_params(labelsize=labelsize) 
+    labels = ax.get_xticklabels() + ax.get_yticklabels()
+    [label.set_fontname('Times New Roman') for label in labels]
+    if save == 1:
+        plt.savefig(filename+'.'+format, dpi=dpi) 
+    if show == 1:
+        plt.show()
+    plt.close('all')
+
 if __name__ == '__main__':
     main()