old_contents: from planet4 import io, clustering, plotting
  import numpy as np
  import matplotlib.pyplot as plt
  
  db = io.DBManager()
  
  def cluster_and_plot(image_id, hdbscan_min_samples=None, scaler='robust', normalize=False,
                       min_samples=4, proba_cut=0.9, dir_ext='ms4_pc0.9'):
      from planet4 import plotting, clustering
      cm = clustering.ClusteringManager(do_dynamic_min_samples=False,
                                        include_angle=True,
                                        quiet=True,
                                        output_dir='new_clustering_'+dir_ext,
                                        normalize=normalize,
                                        scaler=scaler,
                                        min_samples_factor=0.1,
                                        use_DBSCAN=False,
                                        hdbscan_min_samples=hdbscan_min_samples,
                                        min_samples=min_samples,
                                        proba_cut=proba_cut)
      cm.cluster_image_id(image_id)
      plotting.plot_image_id_pipeline(image_id,
                                      save=True,
                                      savetitle='hdbscan_',
                                      cm=cm,
                                     )
      plt.close('all')
      return dict(id_=image_id)
  
  
  myids = np.load('../notebooks/myids.npy')
  
  for id_ in myids:
      print(id_)
      cluster_and_plot(id_, min_samples=4, proba_cut=0.9, dir_ext='ms4_pc0.9_',
                       normalize=True, scaler=)
  
new_contents: from planet4 import io
  import numpy as np
  import matplotlib.pyplot as plt
  
  db = io.DBManager()
  
  
  def cluster_and_plot(image_id, hdbscan_min_samples=None, scaler='robust', normalize=False,
                       min_samples=4, proba_cut=0.9, dir_ext=''):
      from planet4 import plotting, clustering
      cm = clustering.ClusteringManager(do_dynamic_min_samples=False,
                                        include_angle=True,
                                        quiet=True,
                                        output_dir='new_clustering_'+dir_ext,
                                        normalize=normalize,
                                        scaler=scaler,
                                        min_samples_factor=0.1,
                                        use_DBSCAN=False,
                                        hdbscan_min_samples=hdbscan_min_samples,
                                        min_samples=min_samples,
                                        proba_cut=proba_cut)
      cm.cluster_image_id(image_id)
      plotting.plot_image_id_pipeline(image_id,
                                      save=True,
                                      savetitle='hdbscan_',
                                      cm=cm,
                                      )
      plt.close('all')
      return dict(id_=image_id)
  
  
  myids = np.load('../notebooks/myids.npy')
  
  for id_ in myids:
      print(id_)
      cluster_and_plot(id_, min_samples=4, proba_cut=0.9, dir_ext='ms4_pc0.9_scale',
                       normalize=True, scaler='scale')
  
subject: copy out code from notebook