old_contents: # IPython log file
  
  import napari
  import numpy as np
  from skimage import feature
  
  arr0 = np.asarray(da.from_zarr('Pia/Large and dense finished/train_TR67_Inj7_fr112_PL.zarr'))
  arr1 = np.asarray(da.from_zarr('Volga/Large and Dense DONE/train_TR67_Inj7_fr112.zarr'))
  coords0 = pd.DataFrame(measure.regionprops_table(arr0, properties=('centroid',))).to_numpy()
  coords1 = pd.DataFrame(measure.regionprops_table(arr1, properties=('centroid',))).to_numpy()
  viewer = napari.Viewer()
  viewer.open('/home/jni/Dropbox/data/Deep learning training/Large and Dense/train_TR67_Inj7_fr112.nd2')
  matches = feature.match_descriptors(coords0, coords1)
  matches0 = np.zeros(len(coords0), dtype=bool)
  matches1 = np.zeros(len(coords1), dtype=bool)
  matches0[matches[:, 0]] = True
  matches1[matches[:, 1]] = True
  colorcycle0 = [(0.0, 0.0, 0.0, 0.0), (1.0, 1.0, 0.0, 1.0)]
  colorcycle1 = [(0.0, 0.0, 0.0, 0.0), (0.0, 1.0, 1.0, 1.0)]
  viewer.add_points(coords0, name='pia', edge_color='white', size=np.where(matches0, 1, 3), face_color=matches0, face_color_cycle=colorcycle0, scale=viewer.layers[0].scale)
  
new_contents: # IPython log file
  
  import os
  
  import dask.array as da
  import napari
  import numpy as np
  from skimage import feature
  
  datadir = os.path.expanduser('~/Dropbox/data/Deep learning training')
  
  arr0 = np.asarray(
      da.from_zarr(
          os.path.join(
              datadir,
              'Proofread files/Pia/Large and dense finished/'
              'train_TR67_Inj7_fr112_PL.zarr',
          )
      )
  )
  arr1 = np.asarray(
      da.from_zarr(
          os.path.join(
              datadir,
              'Proofread files/Volga/Large and Dense DONE/'
              'train_TR67_Inj7_fr112.zarr',
          )
      )
  )
  coords0 = pd.DataFrame(
      measure.regionprops_table(arr0, properties=('centroid',))
  ).to_numpy()
  coords1 = pd.DataFrame(
      measure.regionprops_table(arr1, properties=('centroid',))
  ).to_numpy()
  
  matches = feature.match_descriptors(coords0, coords1)
  matches0 = np.zeros(len(coords0), dtype=bool)
  matches1 = np.zeros(len(coords1), dtype=bool)
  matches0[matches[:, 0]] = True
  matches1[matches[:, 1]] = True
  colorcycle0 = ['black', 'yellow']
  colorcycle1 = ['black', 'cyan']
  
  viewer = napari.Viewer()
  viewer.open(os.path.join(datadir, 'Large and Dense/train_TR67_Inj7_fr112.nd2'))
  viewer.add_points(
      coords0,
      name='pia',
      edge_color='white',
      size=np.where(matches0, 1, 3),
      face_color={
          'colors': 'matched',
          'categorical_colormap': {True: 'black', False: 'yellow'},
          'mode': 'cycle',
      },
      scale=viewer.layers[0].scale,
      properties={'matched': matches0},
  )
  viewer.add_points(
      coords1,
      name='volga',
      edge_color='white',
      size=np.where(matches1, 1, 3),
      face_color={
          'colors': 'matched',
          'categorical_colormap': {True: 'black', False: 'cyan'},
          'mode': 'cycle',
      },
      scale=viewer.layers[0].scale,
      properties={'matched': matches1},
  )
  
subject: Make script cross-platform