text: import numpy as np
  from matplotlib import pyplot as plt
  
  runtime_data = np.array([8.1, 7.0, 7.3, 7.2, 6.2, 6.1, 8.3, 6.4, 7.1, 7.0, 7.5, 7.8, 7.9, 7.7, 6.4, 6.6, 8.2, 6.7, 8.1, 8.0, 6.7, 7.9, 6.7, 6.5, 5.3, 6.8, 8.3, 4.7, 6.2, 5.9, 6.3, 7.5, 7.1, 8.0, 5.6, 7.9, 8.6, 7.6, 6.9, 7.1, 6.3, 7.5, 2.7, 7.2, 6.3, 6.7, 7.3, 5.6, 7.1, 3.7, 8.1, 5.8, 5.6, 7.2, 9.0, 7.3, 7.2, 7.4, 7.0, 7.5, 6.7, 6.8, 6.5, 4.1, 8.5, 7.7, 7.4, 8.1, 7.5, 7.2, 5.9, 7.1, 7.5, 6.8, 8.1, 7.1, 8.1, 8.3, 7.3, 5.3, 8.8, 7.9, 8.2, 8.1, 7.2, 7.0, 6.4, 7.8, 7.8, 7.4, 8.1, 7.0, 8.1, 7.1, 7.4, 7.4, 8.6, 5.8, 6.3, 8.5, 7.0, 7.0, 8.0, 7.9, 7.3, 7.7, 5.4, 6.3, 5.8, 7.7, 6.3, 8.1, 6.1, 7.7, 8.1, 5.8, 6.2, 8.8, 7.2, 7.4, 6.7, 6.7, 6.0, 7.4, 8.5, 7.5, 5.7, 6.6, 6.4, 8.0, 7.3, 6.0, 6.4, 8.5, 7.1, 7.3, 8.1, 7.3, 8.1, 7.1, 8.0, 6.2, 7.8, 8.2, 8.4, 8.1, 7.4, 7.6, 7.6, 6.2, 6.4, 7.2, 5.8, 7.6, 8.1, 4.7, 7.0, 7.4, 7.5, 7.9, 6.0, 7.0, 8.0, 6.1, 8.0, 5.2, 6.5, 7.3, 7.3, 6.8, 7.9, 7.9, 5.2, 8.0, 7.5, 6.5, 7.6, 7.0, 7.4, 7.3, 6.7, 6.8, 7.0, 5.9, 8.0, 6.0, 6.3, 6.6, 7.8, 6.3, 7.2, 5.6, 8.1, 5.8, 8.2, 6.9, 6.3, 8.1, 8.1, 6.3, 7.9, 6.5, 7.3, 7.9, 5.7, 7.8, 7.5, 7.5, 6.8, 6.7, 6.1, 5.3, 7.1, 5.8, 7.0, 5.5, 7.8, 5.7, 6.1, 7.7, 6.7, 7.1, 6.9, 7.8, 7.0, 7.0, 7.1, 6.4, 7.0, 4.8, 8.2, 5.2, 7.8, 7.4, 6.1, 8.0, 6.8, 3.9, 8.1, 5.9, 7.6, 8.2, 5.8, 6.5, 5.9, 7.6, 7.9, 7.4, 7.1, 8.6, 4.9, 7.3, 7.9, 6.7, 7.5, 7.8, 5.8, 7.6, 6.4, 7.1, 7.8, 8.0, 6.2, 7.0, 6.0, 4.9, 6.0, 7.5, 6.7, 3.7, 7.8, 7.9, 7.2, 8.0, 6.8, 7.0, 7.1, 7.7, 7.0, 7.2, 7.3, 7.6, 7.1, 7.0, 6.0, 6.1, 5.8, 5.3, 5.8, 6.1, 7.5, 7.2, 5.7, 7.7, 7.1, 6.6, 5.7, 6.8, 7.1, 8.1, 7.2, 7.5, 7.0, 5.5, 6.4, 6.7, 6.2, 5.5, 6.0, 6.1, 7.7, 7.8, 6.8, 7.4, 7.5, 7.0, 5.2, 5.3, 6.2, 7.3, 6.5, 6.4, 7.3, 6.7, 7.7, 6.0, 6.0, 7.4, 7.0, 5.4, 6.9, 7.3, 8.0, 7.4, 8.1, 6.1, 7.8, 5.9, 7.8, 6.5, 6.6, 7.4, 6.4, 6.8, 6.2, 5.8, 7.7, 7.3, 5.1, 7.7, 7.3, 6.6, 7.1, 6.7, 6.3, 5.5, 7.4, 7.7, 6.6, 7.8, 6.9, 5.7, 7.8, 7.7, 6.3, 8.0, 5.5, 6.9, 7.0, 5.7, 6.0, 6.8, 6.3, 6.7, 6.9, 5.7, 6.9, 7.6, 7.1, 6.1, 7.6, 7.4, 6.6, 7.6, 7.8, 7.1, 5.6, 6.7, 6.7, 6.6, 6.3, 5.8, 7.2, 5.0, 5.4, 7.2, 6.8, 5.5, 6.0, 6.1, 6.4, 3.9, 7.1, 7.7, 6.7, 6.7, 7.4, 7.8, 6.6, 6.1, 7.8, 6.5, 7.3, 7.2, 5.6, 5.4, 6.9, 7.8, 7.7, 7.2, 6.8, 5.7, 5.8, 6.2, 5.9, 7.8, 6.5, 8.1, 5.2, 6.0, 8.4, 4.7, 7.0, 7.4, 6.4, 7.1, 7.1, 7.6, 6.6, 5.6, 6.3, 7.5, 7.7, 7.4, 6.0, 6.6, 7.1, 7.9, 7.8, 5.9, 7.0, 7.0, 6.8, 6.5, 6.1, 8.3, 6.7, 6.0, 6.4, 7.3, 7.6, 6.0, 6.6, 7.5, 6.3, 7.5, 6.4, 6.9, 8.0, 6.7, 7.8, 6.4, 5.8, 7.5, 7.7, 7.4, 8.5, 5.7, 8.3, 6.7, 7.2, 6.5, 6.3, 7.7, 6.3, 7.8, 6.7, 6.7, 6.6, 8.0, 6.5, 6.9, 7.0, 5.3, 6.3, 7.2, 6.8, 7.1, 7.4, 8.3, 6.3, 7.2, 6.5, 7.3, 7.9, 5.7, 6.5, 7.7, 4.3, 7.8, 7.8, 7.2, 5.0, 7.1, 5.7, 7.1, 6.0, 6.9, 7.9, 6.2, 7.2, 5.3, 4.7, 6.6, 7.0, 3.9, 6.6, 5.4, 6.4, 6.7, 6.9, 5.4, 7.0, 6.4, 7.2, 6.5, 7.0, 5.7, 7.3, 6.1, 7.2, 7.4, 6.3, 7.1, 5.7, 6.7, 6.8, 6.5, 6.8, 7.9, 5.8, 7.1, 4.3, 6.3, 7.1, 4.6, 7.1, 6.3, 6.9, 6.6, 6.5, 6.5, 6.8, 7.8, 6.1, 5.8, 6.3, 7.5, 6.1, 6.5, 6.0, 7.1, 7.1, 7.8, 6.8, 5.8, 6.8, 6.8, 7.6, 6.3, 4.9, 4.2, 5.1, 5.7, 7.6, 5.2, 7.2, 6.0, 7.3, 7.2, 7.8, 6.2, 7.1, 6.4, 6.1, 7.2, 6.6, 6.2, 7.9, 7.3, 6.7, 6.4, 6.4, 7.2, 5.1, 7.4, 7.2, 6.9, 8.1, 7.0, 6.2, 7.6, 6.7, 7.5, 6.6, 6.3, 4.0, 6.9, 6.3, 7.3, 7.3, 6.4, 6.6, 5.6, 6.0, 6.3, 6.7, 6.0, 6.1, 6.2, 6.7, 6.6, 7.0, 4.9, 8.4, 7.0, 7.5, 7.3, 5.6, 6.7, 8.0, 8.1, 4.8, 7.5, 5.5, 8.2, 6.6, 3.2, 5.3, 5.6, 7.4, 6.4, 6.8, 6.7, 6.4, 7.0, 7.9, 5.9, 7.7, 6.7, 7.0, 6.9, 7.7, 6.6, 7.1, 6.6, 5.7, 6.3, 6.5, 8.0, 6.1, 6.5, 7.6, 5.6, 5.9, 7.2, 6.7, 7.2, 6.5, 7.2, 6.7, 7.5, 6.5, 5.9, 7.7, 8.0, 7.6, 6.1, 8.3, 7.1, 5.4, 7.8, 6.5, 5.5, 7.9, 8.1, 6.1, 7.3, 7.2, 5.5, 6.5, 7.0, 7.1, 6.6, 6.5, 5.8, 7.1, 6.5, 7.4, 6.2, 6.0, 7.6, 7.3, 8.2, 5.8, 6.5, 6.6, 6.2, 5.8, 6.4, 6.7, 7.1, 6.0, 5.1, 6.2, 6.2, 6.6, 7.6, 6.8, 6.7, 6.3, 7.0, 6.9, 6.6, 7.7, 7.5, 5.6, 7.1, 5.7, 5.2, 5.4, 6.6, 8.2, 7.6, 6.2, 6.1, 4.6, 5.7, 6.1, 5.9, 7.2, 6.5, 7.9, 6.3, 5.0, 7.3, 5.2, 6.6, 5.2, 7.8, 7.5, 7.3, 7.3, 6.6, 5.7, 8.2, 6.7, 6.2, 6.3, 5.7, 6.6, 4.5, 8.1, 5.6, 7.3, 6.2, 5.1, 4.7, 4.8, 7.2, 6.9, 6.5, 7.3, 6.5, 6.9, 7.8, 6.8, 4.6, 6.7, 6.4, 6.0, 6.3, 6.6, 7.8, 6.6, 6.2, 7.3, 7.4, 6.5, 7.0, 4.3, 7.2, 6.2, 6.2, 6.8, 6.0, 6.6, 7.1, 6.8, 5.2, 6.7, 6.2, 7.0, 6.3, 7.8, 7.6, 5.4, 7.6, 5.4, 4.6, 6.9, 6.8, 5.8, 7.0, 5.8, 5.3, 4.6, 5.3, 7.6, 1.9, 7.2, 6.4, 7.4, 5.7, 6.4, 6.3, 7.5, 5.5, 4.2, 7.8, 6.3, 6.4, 7.1, 7.1, 6.8, 7.3, 6.7, 7.8, 6.3, 7.5, 6.8, 7.4, 6.8, 7.1, 7.6, 5.9, 6.6, 7.5, 6.4, 7.8, 7.2, 8.4, 6.2, 7.1, 6.3, 6.5, 6.9, 6.9, 6.6, 6.9, 7.7, 2.7, 5.4, 7.0, 6.6, 7.0, 6.9, 7.3, 5.8, 5.8, 6.9, 7.5, 6.3, 6.9, 6.1, 7.5, 6.8, 6.5, 5.5, 7.7, 3.5, 6.2, 7.1, 5.5, 7.1, 7.1, 7.1, 7.9, 6.5, 5.5, 6.5, 5.6, 6.8, 7.9, 6.2, 6.2, 6.7, 6.9, 6.5, 6.6, 6.4, 4.7, 7.2, 7.2, 6.7, 7.5, 6.6, 6.7, 7.5, 6.1, 6.4, 6.3, 6.4, 6.8, 6.1, 4.9, 7.3, 5.9, 6.1, 7.1, 5.9, 6.8, 5.4, 6.3, 6.2, 6.6, 4.4, 6.8, 7.3, 7.4, 6.1, 4.9, 5.8, 6.1, 6.4, 6.9, 7.2, 5.6, 4.9, 6.1, 7.8, 7.3, 4.3, 7.2, 6.4, 6.2, 5.2, 7.7, 6.2, 7.8, 7.0, 5.9, 6.7, 6.3, 6.9, 7.0, 6.7, 7.3, 3.5, 6.5, 4.8, 6.9, 5.9, 6.2, 7.4, 6.0, 6.2, 5.0, 7.0, 7.6, 7.0, 5.3, 7.4, 6.5, 6.8, 5.6, 5.9, 6.3, 7.1, 7.5, 6.6, 8.5, 6.3, 5.9, 6.7, 6.2, 5.5, 6.2, 5.6, 5.3])
  max_runtime = runtime_data.max()
  min_runtime = runtime_data.min()
  print(min_runtime,max_runtime)
  
  #设置不等宽的组距，hist方法中取到的会是一个左闭右开的去见[1.9,3.5)
  num_bin_list = [1.9,3.5]
  i=3.5
  while i<=max_runtime:
      i += 0.5
      num_bin_list.append(i)
  print(num_bin_list)
  
  #设置图形的大小
  plt.figure(figsize=(20,8),dpi=80)
  plt.hist(runtime_data,num_bin_list)
  
  #xticks让之前的组距能够对应上
  plt.xticks(num_bin_list)
  
  plt.show()
  