update_showing_all_guan_functions
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@ -7,7 +7,6 @@ import numpy as np
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from math import *
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import cmath
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import functools
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import guan
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def hamiltonian(B, k, N, M, t1, a): # graphene哈密顿量（N是条带的宽度参数）
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# 初始化为零矩阵
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@ -43,9 +42,58 @@ def main():
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a = 1
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hamiltonian_function0 = functools.partial(hamiltonian, k=0, N=N, M=0, t1=1, a=a)
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B_array = np.linspace(0, 1/(3*np.sqrt(3)/2*a*a), 100)
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eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(B_array, hamiltonian_function0)
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BS_array = B_array*(3*np.sqrt(3)/2*a*a)
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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)
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eigenvalue_array = calculate_eigenvalue_with_one_parameter(B_array, hamiltonian_function0)
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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)
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# import guan
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# eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(B_array, hamiltonian_function0)
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# 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)
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def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print_show=0):
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dim_x = np.array(x_array).shape[0]
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i0 = 0
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if np.array(hamiltonian_function(0)).shape==():
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eigenvalue_array = np.zeros((dim_x, 1))
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for x0 in x_array:
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hamiltonian = hamiltonian_function(x0)
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eigenvalue_array[i0, 0] = np.real(hamiltonian)
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i0 += 1
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else:
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dim = np.array(hamiltonian_function(0)).shape[0]
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eigenvalue_array = np.zeros((dim_x, dim))
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for x0 in x_array:
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if print_show==1:
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print(x0)
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hamiltonian = hamiltonian_function(x0)
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eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
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eigenvalue_array[i0, :] = eigenvalue
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i0 += 1
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return eigenvalue_array
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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):
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots()
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plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left)
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ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
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ax.grid()
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y_min=min(y_array)
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if y_max==None:
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y_max=max(y_array)
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ax.set_ylim(y_min, y_max)
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ax.tick_params(labelsize=labelsize)
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labels = ax.get_xticklabels() + ax.get_yticklabels()
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[label.set_fontname('Times New Roman') for label in labels]
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if save == 1:
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plt.savefig(filename+'.'+format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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if __name__ == '__main__':
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main()
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@ -0,0 +1,81 @@
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"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/16199
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"""
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import numpy as np
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from math import *
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import cmath
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def hamiltonian_1(k, v=0.6, w=1):
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matrix = np.zeros((2, 2), dtype=complex)
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matrix[0,1] = v+w*cmath.exp(-1j*k)
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matrix[1,0] = v+w*cmath.exp(1j*k)
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return matrix
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def hamiltonian_2(k, v=0.6, w=1):
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matrix = np.zeros((2, 2), dtype=complex)
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matrix[0,1] = v*cmath.exp(1j*k/2)+w*cmath.exp(-1j*k/2)
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matrix[1,0] = v*cmath.exp(-1j*k/2)+w*cmath.exp(1j*k/2)
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return matrix
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def main():
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k_array = np.linspace(-pi ,pi, 100)
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E_1_array = calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_1)
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plot(k_array, E_1_array, xlabel='k', ylabel='E_1')
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E_2_array = calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_2)
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plot(k_array, E_2_array, xlabel='k', ylabel='E_2')
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# import guan
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# E_1_array = guan.calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_1)
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# guan.plot(k_array, E_1_array, xlabel='k', ylabel='E_1')
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# E_2_array = guan.calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_2)
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# guan.plot(k_array, E_2_array, xlabel='k', ylabel='E_2')
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def calculate_eigenvalue_with_one_parameter(x_array, hamiltonian_function, print_show=0):
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dim_x = np.array(x_array).shape[0]
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i0 = 0
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if np.array(hamiltonian_function(0)).shape==():
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eigenvalue_array = np.zeros((dim_x, 1))
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for x0 in x_array:
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hamiltonian = hamiltonian_function(x0)
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eigenvalue_array[i0, 0] = np.real(hamiltonian)
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i0 += 1
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else:
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dim = np.array(hamiltonian_function(0)).shape[0]
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eigenvalue_array = np.zeros((dim_x, dim))
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for x0 in x_array:
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if print_show==1:
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print(x0)
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hamiltonian = hamiltonian_function(x0)
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eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
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eigenvalue_array[i0, :] = eigenvalue
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i0 += 1
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return eigenvalue_array
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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):
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots()
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plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left)
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ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
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ax.grid()
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y_min=min(y_array)
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if y_max==None:
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y_max=max(y_array)
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ax.set_ylim(y_min, y_max)
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ax.tick_params(labelsize=labelsize)
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labels = ax.get_xticklabels() + ax.get_yticklabels()
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[label.set_fontname('Times New Roman') for label in labels]
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if save == 1:
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plt.savefig(filename+'.'+format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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if __name__ == '__main__':
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main()
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@ -1,31 +0,0 @@
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"""
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This code is supported by the website: https://www.guanjihuan.com
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The newest version of this code is on the web page: https://www.guanjihuan.com/archives/16199
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"""
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import numpy as np
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from math import *
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import cmath
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import guan
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v=0.6
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w=1
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k_array = np.linspace(-pi ,pi, 100)
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def hamiltonian_1(k):
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matrix = np.zeros((2, 2), dtype=complex)
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matrix[0,1] = v+w*cmath.exp(-1j*k)
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matrix[1,0] = v+w*cmath.exp(1j*k)
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return matrix
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def hamiltonian_2(k):
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matrix = np.zeros((2, 2), dtype=complex)
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matrix[0,1] = v*cmath.exp(1j*k/2)+w*cmath.exp(-1j*k/2)
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matrix[1,0] = v*cmath.exp(-1j*k/2)+w*cmath.exp(1j*k/2)
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return matrix
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E_1_array = guan.calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_1)
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guan.plot(k_array, E_1_array, xlabel='k', ylabel='E_1')
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E_2_array = guan.calculate_eigenvalue_with_one_parameter(k_array, hamiltonian_2)
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guan.plot(k_array, E_2_array, xlabel='k', ylabel='E_2')
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@ -5,7 +5,6 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a
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import numpy as np
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from math import *
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import guan
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def hamiltonian(kx, ky): # kagome lattice
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k1_dot_a1 = kx
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@ -20,7 +19,85 @@ def hamiltonian(kx, ky): # kagome lattice
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h = -t*h
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return h
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kx_array = np.linspace(-pi ,pi, 500)
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ky_array = np.linspace(-pi ,pi, 500)
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eigenvalue_array = guan.calculate_eigenvalue_with_two_parameters(kx_array, ky_array, hamiltonian)
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guan.plot_3d_surface(kx_array, ky_array, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', rcount=200, ccount=200)
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def main():
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kx_array = np.linspace(-pi ,pi, 500)
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ky_array = np.linspace(-pi ,pi, 500)
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eigenvalue_array = calculate_eigenvalue_with_two_parameters(kx_array, ky_array, hamiltonian)
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plot_3d_surface(kx_array, ky_array, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', rcount=200, ccount=200)
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# import guan
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# eigenvalue_array = guan.calculate_eigenvalue_with_two_parameters(kx_array, ky_array, hamiltonian)
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# guan.plot_3d_surface(kx_array, ky_array, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', rcount=200, ccount=200)
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def calculate_eigenvalue_with_two_parameters(x_array, y_array, hamiltonian_function, print_show=0, print_show_more=0):
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dim_x = np.array(x_array).shape[0]
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dim_y = np.array(y_array).shape[0]
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if np.array(hamiltonian_function(0,0)).shape==():
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eigenvalue_array = np.zeros((dim_y, dim_x, 1))
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i0 = 0
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for y0 in y_array:
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j0 = 0
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for x0 in x_array:
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hamiltonian = hamiltonian_function(x0, y0)
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eigenvalue_array[i0, j0, 0] = np.real(hamiltonian)
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j0 += 1
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i0 += 1
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else:
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dim = np.array(hamiltonian_function(0, 0)).shape[0]
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eigenvalue_array = np.zeros((dim_y, dim_x, dim))
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i0 = 0
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for y0 in y_array:
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j0 = 0
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if print_show==1:
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print(y0)
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for x0 in x_array:
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if print_show_more==1:
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print(x0)
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hamiltonian = hamiltonian_function(x0, y0)
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eigenvalue, eigenvector = np.linalg.eigh(hamiltonian)
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eigenvalue_array[i0, j0, :] = eigenvalue
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j0 += 1
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i0 += 1
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return eigenvalue_array
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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):
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import matplotlib.pyplot as plt
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from matplotlib import cm
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from matplotlib.ticker import LinearLocator
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matrix = np.array(matrix)
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fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
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plt.subplots_adjust(bottom=0.1, right=0.65)
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x_array, y_array = np.meshgrid(x_array, y_array)
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if len(matrix.shape) == 2:
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surf = ax.plot_surface(x_array, y_array, matrix, rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
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elif len(matrix.shape) == 3:
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for i0 in range(matrix.shape[2]):
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surf = ax.plot_surface(x_array, y_array, matrix[:,:,i0], rcount=rcount, ccount=ccount, cmap=cm.coolwarm, linewidth=0, antialiased=False)
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_zlabel(zlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.zaxis.set_major_locator(LinearLocator(5))
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ax.zaxis.set_major_formatter('{x:.2f}')
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if z_min!=None or z_max!=None:
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if z_min==None:
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z_min=matrix.min()
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if z_max==None:
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z_max=matrix.max()
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ax.set_zlim(z_min, z_max)
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ax.tick_params(labelsize=labelsize)
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labels = ax.get_xticklabels() + ax.get_yticklabels() + ax.get_zticklabels()
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[label.set_fontname('Times New Roman') for label in labels]
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cax = plt.axes([0.8, 0.1, 0.05, 0.8])
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cbar = fig.colorbar(surf, cax=cax)
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cbar.ax.tick_params(labelsize=labelsize)
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for l in cbar.ax.yaxis.get_ticklabels():
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l.set_family('Times New Roman')
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if save == 1:
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plt.savefig(filename+'.'+format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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if __name__ == '__main__':
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main()
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@ -6,7 +6,6 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a
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import numpy as np
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import cmath
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from math import *
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import guan
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def hamiltonian(kx, ky): # BBH model
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# label of atoms in a unit cell
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@ -61,7 +60,33 @@ def main():
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nu_x[i0] += 1
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nu_x = np.sort(nu_x)
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nu_x_array.append(nu_x.real)
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guan.plot(ky_array, nu_x_array, xlabel='ky', ylabel='nu_x', style='-', y_min=0, y_max=1)
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plot(ky_array, nu_x_array, xlabel='ky', ylabel='nu_x', style='-', y_min=0, y_max=1)
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# import guan
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# guan.plot(ky_array, nu_x_array, xlabel='ky', ylabel='nu_x', style='-', y_min=0, y_max=1)
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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):
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import matplotlib.pyplot as plt
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fig, ax = plt.subplots()
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plt.subplots_adjust(bottom=adjust_bottom, left=adjust_left)
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ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize)
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ax.grid()
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ax.set_title(title, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_xlabel(xlabel, fontsize=fontsize, fontfamily='Times New Roman')
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ax.set_ylabel(ylabel, fontsize=fontsize, fontfamily='Times New Roman')
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if y_min!=None or y_max!=None:
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if y_min==None:
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y_min=min(y_array)
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if y_max==None:
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y_max=max(y_array)
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ax.set_ylim(y_min, y_max)
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ax.tick_params(labelsize=labelsize)
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labels = ax.get_xticklabels() + ax.get_yticklabels()
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[label.set_fontname('Times New Roman') for label in labels]
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if save == 1:
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plt.savefig(filename+'.'+format, dpi=dpi)
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if show == 1:
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plt.show()
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plt.close('all')
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if __name__ == '__main__':
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main()
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@ -7,7 +7,6 @@ import numpy as np
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import cmath
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from math import *
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import functools
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import guan
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def hamiltonian(kx, ky): # BBH model
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# label of atoms in a unit cell
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@ -32,13 +31,133 @@ def hamiltonian(kx, ky): # BBH model
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def main():
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kx = np.arange(-pi, pi, 0.05)
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ky = np.arange(-pi, pi, 0.05)
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eigenvalue_array = guan.calculate_eigenvalue_with_two_parameters(kx, ky, hamiltonian)
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guan.plot_3d_surface(kx, ky, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', title='BBH bands')
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eigenvalue_array = calculate_eigenvalue_with_two_parameters(kx, ky, hamiltonian)
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plot_3d_surface(kx, ky, eigenvalue_array, xlabel='kx', ylabel='ky', zlabel='E', title='BBH bands')
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hamiltonian0 = functools.partial(hamiltonian, ky=0)
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eigenvalue_array = guan.calculate_eigenvalue_with_one_parameter(kx, hamiltonian0)
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guan.plot(kx, eigenvalue_array, xlabel='kx', ylabel='E', title='BBH bands ky=0')
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eigenvalue_array = calculate_eigenvalue_with_one_parameter(kx, hamiltonian0)
|
||||
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()
|
||||
@ -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()
|
||||
main()
|
||||
Loading…
x
Reference in New Issue
Block a user