old_contents: """
  =============================================
  A demo of the mean-shift clustering algorithm
  =============================================
  
  Reference:
  
  Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
  feature space analysis". IEEE Transactions on Pattern Analysis and
  Machine Intelligence. 2002. pp. 603-619.
  
  """
  
  import numpy as np
  from sklearn.cluster import MeanShift, estimate_bandwidth
  from sklearn.datasets import make_blobs
  
  # #############################################################################
  # Generate sample data
  centers = [[1, 1], [-1, -1], [1, -1]]
  X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)
  
  # #############################################################################
  # Compute clustering with MeanShift
  
  # The following bandwidth can be automatically detected using
  bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)
  
  ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
  ms.fit(X)
  labels = ms.labels_
  cluster_centers = ms.cluster_centers_
  
  labels_unique = np.unique(labels)
  n_clusters_ = len(labels_unique)
  
  print("number of estimated clusters : %d" % n_clusters_)
  
  # #############################################################################
  # Plot result
  import matplotlib.pyplot as plt
  from itertools import cycle
  
  plt.figure(1)
  plt.clf()
  
  colors = cycle("bgrcmykbgrcmykbgrcmykbgrcmyk")
  for k, col in zip(range(n_clusters_), colors):
      my_members = labels == k
      cluster_center = cluster_centers[k]
      plt.plot(X[my_members, 0], X[my_members, 1], col + ".")
      plt.plot(
          cluster_center[0],
          cluster_center[1],
          "o",
          markerfacecolor=col,
          markeredgecolor="k",
          markersize=14,
      )
  plt.title("Estimated number of clusters: %d" % n_clusters_)
  plt.show()
  
new_contents: """
  =============================================
  A demo of the mean-shift clustering algorithm
  =============================================
  
  Reference:
  
  Dorin Comaniciu and Peter Meer, "Mean Shift: A robust approach toward
  feature space analysis". IEEE Transactions on Pattern Analysis and
  Machine Intelligence. 2002. pp. 603-619.
  
  """
  
  import numpy as np
  from sklearn.cluster import MeanShift, estimate_bandwidth
  from sklearn.datasets import make_blobs
  
  # %%
  # Generate sample data
  # --------------------
  centers = [[1, 1], [-1, -1], [1, -1]]
  X, _ = make_blobs(n_samples=10000, centers=centers, cluster_std=0.6)
  
  # %%
  # Compute clustering with MeanShift
  # ---------------------------------
  
  # The following bandwidth can be automatically detected using
  bandwidth = estimate_bandwidth(X, quantile=0.2, n_samples=500)
  
  ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
  ms.fit(X)
  labels = ms.labels_
  cluster_centers = ms.cluster_centers_
  
  labels_unique = np.unique(labels)
  n_clusters_ = len(labels_unique)
  
  print("number of estimated clusters : %d" % n_clusters_)
  
  # %%
  # Plot result
  # -----------
  import matplotlib.pyplot as plt
  from itertools import cycle
  
  plt.figure(1)
  plt.clf()
  
  colors = cycle("bgrcmykbgrcmykbgrcmykbgrcmyk")
  for k, col in zip(range(n_clusters_), colors):
      my_members = labels == k
      cluster_center = cluster_centers[k]
      plt.plot(X[my_members, 0], X[my_members, 1], col + ".")
      plt.plot(
          cluster_center[0],
          cluster_center[1],
          "o",
          markerfacecolor=col,
          markeredgecolor="k",
          markersize=14,
      )
  plt.title("Estimated number of clusters: %d" % n_clusters_)
  plt.show()
  
subject: use notebook-style for plot_mean_shift.py (#22713)