old_contents: """
  ==========================
  Callosal bundles using AFQ API
  ==========================
  An example using the AFQ API to find callosal bundles using the templates from:
  http://hdl.handle.net/1773/34926
  """
  import os.path as op
  
  import plotly
  
  from AFQ import api
  from AFQ.definitions.mask import RoiMask
  import AFQ.data as afd
  
  ##########################################################################
  # Get some example data
  # ---------------------
  #
  # Retrieves `Stanford HARDI dataset <https://purl.stanford.edu/ng782rw8378>`_.
  #
  
  afd.organize_stanford_data(clear_previous_afq=True)
  
  ##########################################################################
  # Set tractography parameters (optional)
  # ---------------------
  # We make this tracking_params which we will pass to the AFQ object
  # which specifies that we want 100,000 seeds randomly distributed
  # in the ROIs of every bundle.
  #
  # We only do this to make this example faster and consume less space.
  
  tracking_params = dict(seed_mask=RoiMask(),
                         n_seeds=10000,
                         random_seeds=True,
                         rng_seed=42)
  
  ##########################################################################
  # Initialize an AFQ object:
  # -------------------------
  #
  # We specify bundle_info as the default bundles list (api.BUNDLES) plus the
  # callosal bundle list. This tells the AFQ object to use bundles from both
  # the standard and callosal templates.
  
  myafq = api.AFQ(bids_path=op.join(afd.afq_home,
                                    'stanford_hardi'),
                  dmriprep='vistasoft',
                  bundle_info=api.BUNDLES + api.CALLOSUM_BUNDLES,
                  tracking_params=tracking_params)
  
  ##########################################################################
  # Visualizing bundles and tract profiles:
  # ---------------------------------------
  # This would run the script and visualize the bundles using the plotly
  # interactive visualization, which should automatically open in a
  # new browser window.
  bundle_html = myafq.all_bundles_figure
  plotly.io.show(bundle_html["01"])
  
new_contents: """
  ==========================
  Callosal bundles using AFQ API
  ==========================
  An example using the AFQ API to find callosal bundles using the templates from:
  http://hdl.handle.net/1773/34926
  """
  import os.path as op
  
  import plotly
  
  from AFQ import api
  from AFQ.definitions.mask import RoiMask
  import AFQ.data as afd
  
  ##########################################################################
  # Get some example data
  # ---------------------
  #
  # Retrieves `Stanford HARDI dataset <https://purl.stanford.edu/ng782rw8378>`_.
  #
  
  afd.organize_stanford_data(clear_previous_afq=True)
  
  ##########################################################################
  # Set tractography parameters (optional)
  # ---------------------
  # We make this tracking_params which we will pass to the AFQ object
  # which specifies that we want 100,000 seeds randomly distributed
  # in the ROIs of every bundle.
  #
  # We only do this to make this example faster and consume less space.
  
  tracking_params = dict(seed_mask=RoiMask(),
                         n_seeds=10000,
                         random_seeds=True,
                         rng_seed=42)
  
  ##########################################################################
  # Initialize an AFQ object:
  # -------------------------
  #
  # We specify bundle_info as the callosal bundles only
  # (`api.CALLOSUM_BUNDLES`). If we want to segment both the callosum
  # and the other bundles, we would pass `api.CALLOSUM_BUNDLES + api.BUNDLES`
  # instead. This would tell the AFQ object to use bundles from both
  # the standard and callosal templates.
  
  myafq = api.AFQ(bids_path=op.join(afd.afq_home,
                                    'stanford_hardi'),
                  dmriprep='vistasoft',
                  bundle_info=api.CALLOSUM_BUNDLES,
                  tracking_params=tracking_params,
                  viz_backend='plotly_no_gif')
  
  # Calling export all produces all of the outputs of processing, including
  # tractography, scalar maps, tract profiles and visualizations:
  myafq.export_all()
  
  
  ##########################################################################
  # Visualizing bundles and tract profiles:
  # ---------------------------------------
  # This would run the script and visualize the bundles using the plotly
  # interactive visualization, which should automatically open in a
  # new browser window.
  bundle_html = myafq.all_bundles_figure
  plotly.io.show(bundle_html["01"])
  
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