text: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import numpy as np
  import pandas as pd
  import matplotlib.pyplot as plt
  import seaborn as sns
  from brokenaxes import brokenaxes
  import matplotlib
  
  
  # plt.style.use('bmh')
  # fig = plt.figure(figsize=(6,5))#
  
  
  # plt.plot([0.1, 0.5, 1, 2, 3], [4.26, 4.40, 4.38, 4.54, 4.54], marker='o', color='blue')
  # plt.ylim(ymin = 4.2)
  # plt.ylim(ymax = 4.6)
  # # plt.xlim(xmin = 0.935)
  # # plt.xlim(xmax = 0.9655)
  # plt.xlabel("Lambda",fontsize=13,fontweight='bold')
  # plt.ylabel("Error (%)",fontsize=13,fontweight='bold')
  # fig.suptitle('CIFAR-10')
  # for a, b in zip([0.1, 0.5, 1, 2, 3], [4.26, 4.40, 4.38, 4.54, 4.54]):
  #     plt.text(a, b + 0.01, '%.2f' % b, ha='center', va='bottom', fontsize=12)
  #     # plt.text(a, c + 0.0, '%.5f' % c, ha='center', va='bottom', fontsize=7)
  # # plt.tight_layout()
  # plt.savefig('./plot_cifar10.pdf', dpi=250)
  # plt.show()
  
  plt.style.use('bmh')
  fig = plt.figure(figsize=(6,5))#
  
  
  plt.plot([0.1, 0.5, 1, 2, 3], [21.52, 21.62, 21.04, 22.50, 22.02], marker='o', color='blue')
  plt.ylim(ymin = 21.00)
  plt.ylim(ymax = 22.60)
  # plt.xlim(xmin = 0.935)
  # plt.xlim(xmax = 0.9655)
  plt.xlabel("Lambda",fontsize=13,fontweight='bold')
  plt.ylabel("Error (%)",fontsize=13,fontweight='bold')
  fig.suptitle('CIFAR-100')
  for a, b in zip([0.1, 0.5, 1, 2, 3], [21.52, 21.62, 21.04, 22.50, 22.02]):
      plt.text(a, b + 0.05, '%.2f' % b, ha='center', va='bottom', fontsize=12)
      # plt.text(a, c + 0.0, '%.5f' % c, ha='center', va='bottom', fontsize=7)
  # plt.tight_layout()
  plt.savefig('./plot_cifar100.pdf', dpi=250)
  plt.show()
  
  
  # plt.scatter([0.9504], [0.4993], marker='+', color='red', label='DenseNet-121', s=80)
  # plt.scatter([0.9646], [0.4872], marker='*', color='red', label='WRN-28-10', s=80)
  # plt.scatter([0.9403], [0.5016], marker='o', color='blue', label='AdaRKNet-62', s=80)
  # plt.scatter([0.9596], [0.4855], marker='v', color='blue', label='ResNet-62', s=80)
  # plt.scatter([0.9362], [0.4807], marker='s', color='green', label='ResNet-50', s=80)
  # plt.scatter([0.9443], [0.4732], marker='d', color='green', label='MobileNetV2', s=80)
  # plt.legend()
  # plt.ylim(ymin = 0.471)
  # plt.ylim(ymax = 0.505)
  # plt.xlim(xmin = 0.935)
  # plt.xlim(xmax = 0.9655)
  # y_major_locator=MultipleLocator(0.01)
  # x_major_locator=MultipleLocator(0.01)
  # ax=plt.gca()
  # ax.yaxis.set_major_locator(y_major_locator)
  # ax.xaxis.set_major_locator(x_major_locator)
  # # plt.tight_layout()
  # plt.xlabel("Natural Accuracy",fontsize=13,fontweight='bold')
  # plt.ylabel("Adversarial Robustness (PGD-20)",fontsize=13,fontweight='bold')
  # fig.suptitle('Performance Misalignment for Differnet Architectures')
  # plt.savefig('./plot_performance.pdf', dpi=250)
  # plt.show()