old_contents: import km
  
  data = km.np.genfromtxt('lion-reference.csv',delimiter=',')
  
  mapper = km.KeplerMapper(verbose=1)
  
  lens = mapper.fit_transform(data)
  
  graph = mapper.map(lens,
                     data,
                     clusterer=km.cluster.DBSCAN(eps=0.1, min_samples=5),
                     nr_cubes=10,
                     overlap_perc=0.2)
  
  mapper.visualize(graph,
                   path_html="lion_keplermapper_output.html")
  
  # You may want to visualize the original point cloud data in 3D scatter too
  """
  import matplotlib.pyplot as plt
  from mpl_toolkits.mplot3d import Axes3D
  
  fig = plt.figure()
  ax = fig.add_subplot(111, projection='3d')
  ax.scatter(data[:,0],data[:,1],data[:,2])
  plt.savefig("lion-reference.csv.png")
  plt.show()
  """
  
new_contents: import os
  import numpy as np
  from kmapper import KeplerMapper
  from sklearn import cluster
  
  dir_path = os.path.dirname(os.path.realpath(__file__))
  
  data = np.genfromtxt(os.path.join(dir_path,'lion-reference.csv'),delimiter=',')
  
  mapper = KeplerMapper(verbose=1)
  
  lens = mapper.fit_transform(data)
  
  graph = mapper.map(lens,
                     data,
                     clusterer=cluster.DBSCAN(eps=0.1, min_samples=5),
                     nr_cubes=10,
                     overlap_perc=0.2)
  
  mapper.visualize(graph,
                   path_html=os.path.join(dir_path, "lion_keplermapper_output.html"))
  
  # You may want to visualize the original point cloud data in 3D scatter too
  """
  import matplotlib.pyplot as plt
  from mpl_toolkits.mplot3d import Axes3D
  
  fig = plt.figure()
  ax = fig.add_subplot(111, projection='3d')
  ax.scatter(data[:,0],data[:,1],data[:,2])
  plt.savefig("lion-reference.csv.png")
  plt.show()
  """
  
subject: update example to new api