From 6ef48f5f7620c1f8f2703ced373cec66bc525ed5 Mon Sep 17 00:00:00 2001 From: guanjihuan Date: Mon, 15 Aug 2022 02:56:03 +0800 Subject: [PATCH] update --- ...bution_with_Wilson_loop_(function_form).py | 83 +++++++++++++++-- ...oop_for_degenerate_case_(function_form).py | 89 ++++++++++++++++-- ...th_the_efficient_method_(function_form).py | 92 ++++++++++++++++--- 3 files changed, 240 insertions(+), 24 deletions(-) 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/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_(function_form).py index 1aaea88..bcd5ea1 100644 --- a/academic_codes/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_(function_form).py +++ b/academic_codes/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_(function_form).py @@ -7,7 +7,6 @@ import numpy as np from math import * import cmath import math -import guan 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)） @@ -21,12 +20,19 @@ def hamiltonian(k1, k2, t1=2.82, a=1/sqrt(3)): # 石墨烯哈密顿量（a为 def main(): k_array, berry_curvature_array = calculate_berry_curvature_with_wilson_loop(hamiltonian_function=hamiltonian, k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) - # k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop(hamiltonian_function=hamiltonian, k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) - guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 0]), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') - guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 1]), title='Conductance Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 0]), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 1]), title='Conductance Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') dim = berry_curvature_array.shape - guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 0]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 - guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 1]), title='Conductance Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 0]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 1]), title='Conductance Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + + # import guan + # k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop(hamiltonian_function=hamiltonian, k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) + # guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 0]), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + # guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 1]), title='Conductance Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + # dim = berry_curvature_array.shape + # guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 0]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + # guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 1]), title='Conductance Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0): @@ -79,5 +85,70 @@ def calculate_berry_curvature_with_wilson_loop(hamiltonian_function, k_min=-math return k_array, berry_curvature_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') + + +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.grid() + ax.tick_params(labelsize=labelsize) + labels = ax.get_xticklabels() + ax.get_yticklabels() + [label.set_fontname('Times New Roman') for label in labels] + ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize) + 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) + if save == 1: + plt.savefig(filename+'.'+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/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_for_degenerate_case_(function_form).py b/academic_codes/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_for_degenerate_case_(function_form).py index a283928..7f53b1d 100644 --- 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/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_Wilson_loop_for_degenerate_case_(function_form).py @@ -7,7 +7,6 @@ import numpy as np from math import * import cmath import math -import guan 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)） @@ -21,16 +20,27 @@ def hamiltonian(k1, k2, t1=2.82, a=1/sqrt(3)): # 石墨烯哈密顿量（a为 def main(): k_array, berry_curvature_array = calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function=hamiltonian, index_of_bands=[0], k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) - # k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function=hamiltonian, index_of_bands=[0], k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) dim = berry_curvature_array.shape - guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') - guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + plot_3d_surface(k_array, k_array, np.real(berry_curvature_array), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 k_array, berry_curvature_array = calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function=hamiltonian, index_of_bands=[0, 1], k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) - # k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function=hamiltonian, index_of_bands=[0, 1], k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) dim = berry_curvature_array.shape - guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array), title='All Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') - guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :]), title='All Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + plot_3d_surface(k_array, k_array, np.real(berry_curvature_array), title='All Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :]), title='All Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + + + # import guan + # k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function=hamiltonian, index_of_bands=[0], k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) + # dim = berry_curvature_array.shape + # guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + # guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + + # k_array, berry_curvature_array = guan.calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function=hamiltonian, index_of_bands=[0, 1], k_min=-2*math.pi, k_max=2*math.pi, precision_of_plaquettes=500, precision_of_wilson_loop=1) + # dim = berry_curvature_array.shape + # guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array), title='All Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + # guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :]), title='All Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_function, index_of_bands=[0, 1], k_min=-math.pi, k_max=math.pi, precision_of_plaquettes=20, precision_of_wilson_loop=5, print_show=0): @@ -98,5 +108,70 @@ def calculate_berry_curvature_with_wilson_loop_for_degenerate_case(hamiltonian_f return k_array, berry_curvature_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') + + +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.grid() + ax.tick_params(labelsize=labelsize) + labels = ax.get_xticklabels() + ax.get_yticklabels() + [label.set_fontname('Times New Roman') for label in labels] + ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize) + 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) + if save == 1: + plt.savefig(filename+'.'+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/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_(function_form).py b/academic_codes/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_(function_form).py index afedb1f..5ced048 100644 --- 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/2022_08_13_Berry_curvature_distribution_in_function_form/Berry_curvature_distribution_with_the_efficient_method_(function_form).py @@ -6,10 +6,8 @@ The newest version of this code is on the web page: https://www.guanjihuan.com/a import numpy as np from math import * import cmath -import guan import math - 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))*(1+0j) h[0, 0] = 0.28/2 @@ -21,12 +19,19 @@ def hamiltonian(k1, k2, t1=2.82, a=1/sqrt(3)): # 石墨烯哈密顿量（a为 def main(): k_array, berry_curvature_array = calculate_berry_curvature_with_efficient_method(hamiltonian_function=hamiltonian, k_min=-2*math.pi, k_max=2*math.pi, precision=500, print_show=0) - # k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method(hamiltonian_function=hamiltonian, k_min=-2*math.pi, k_max=2*math.pi, precision=500, print_show=0) - guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 0]), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') - guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 1]), title='Conductance Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 0]), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 1]), title='Conductance Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') dim = berry_curvature_array.shape - guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 0]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 - guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 1]), title='Conductance Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 0]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 1]), title='Conductance Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + + # import guan + # k_array, berry_curvature_array = guan.calculate_berry_curvature_with_efficient_method(hamiltonian_function=hamiltonian, k_min=-2*math.pi, k_max=2*math.pi, precision=500, print_show=0) + # guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 0]), title='Valence Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + # guan.plot_3d_surface(k_array, k_array, np.real(berry_curvature_array[:, :, 1]), title='Conductance Band', xlabel='kx', ylabel='ky', zlabel='Berry curvature') + # dim = berry_curvature_array.shape + # guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 0]), title='Valence Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 + # guan.plot(k_array, np.real(berry_curvature_array[int(dim[0]/2), :, 1]), title='Conductance Band ky=0', xlabel='kx', ylabel='Berry curvature') # ky=0 def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min=-math.pi, k_max=math.pi, precision=100, print_show=0): @@ -44,13 +49,13 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min= j0 = 0 for ky in k_array: H = hamiltonian_function(kx, ky) - vector = guan.calculate_eigenvector(H) + eigenvalue, vector = np.linalg.eigh(H) H_delta_kx = hamiltonian_function(kx+delta, ky) - vector_delta_kx = guan.calculate_eigenvector(H_delta_kx) + eigenvalue, vector_delta_kx = np.linalg.eigh(H_delta_kx) H_delta_ky = hamiltonian_function(kx, ky+delta) - vector_delta_ky = guan.calculate_eigenvector(H_delta_ky) + eigenvalue, vector_delta_ky = np.linalg.eigh(H_delta_ky) H_delta_kx_ky = hamiltonian_function(kx+delta, ky+delta) - vector_delta_kx_ky = guan.calculate_eigenvector(H_delta_kx_ky) + eigenvalue, vector_delta_kx_ky = np.linalg.eigh(H_delta_kx_ky) for i in range(dim): vector_i = vector[:, i] vector_delta_kx_i = vector_delta_kx[:, i] @@ -67,5 +72,70 @@ def calculate_berry_curvature_with_efficient_method(hamiltonian_function, k_min= return k_array, berry_curvature_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') + + +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.grid() + ax.tick_params(labelsize=labelsize) + labels = ax.get_xticklabels() + ax.get_yticklabels() + [label.set_fontname('Times New Roman') for label in labels] + ax.plot(x_array, y_array, style, linewidth=linewidth, markersize=markersize) + 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) + if save == 1: + plt.savefig(filename+'.'+format, dpi=dpi) + if show == 1: + plt.show() + plt.close('all') + + if __name__ == '__main__': main() \ No newline at end of file