code: import sklearn.datasets as data
  from classix import CLASSIX
  import matplotlib.pyplot as plt
  
  def rn_scale_explore():
      plt.style.use('bmh')
  
      TOL = 0.1 
      random_state = 1
      moons, _ = data.make_moons(n_samples=1000, noise=0.05, random_state=random_state)
      blobs, _ = data.make_blobs(n_samples=1500, centers=[(-0.85,2.75), (1.75,2.25)], cluster_std=0.5, random_state=random_state)
      X = np.vstack([blobs, moons])
  
      for scale in np.arange(1, 3.3, 0.1):
          clx = CLASSIX(sorting='pca', radius=TOL, group_merging='distance', verbose=0)
          clx.fit_transform(X)
          clx.visualize_linkage(scale=scale, figsize=(8,8), labelsize=24, path='img')
  
  
      for tol in np.arange(0.1, 1.3, 0.1):
          clx = CLASSIX(sorting='pca', radius=tol, group_merging='distance', verbose=0)
          clx.fit_transform(X)
          clx.visualize_linkage(scale=1.5, figsize=(8,8), labelsize=24, plot_boundary=True, path='img')
      
      
apis: matplotlib.pyplot.style.use sklearn.datasets.make_moons sklearn.datasets.make_blobs