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        "\n# Using RecoBundles for bundle recognition\n\nFor bundle recognition, pyAFQ defaults to use the waypoint ROI approach\ndescribed in [Yeatman2012]_. However, as an alternative approach, pyAFQ also\nsupports using the RecoBundles algorithm [Garyfallidis2017]_, which uses an\natlas of bundles in streamlines. This example shows how to\nuse RecoBundles for bundle recognition.\n\nThe code closely resembles the code used in `sphx_glr_tutorial_examples_plot_001-plot_afq_api.py`.\n"
      ]
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "import os.path as op\nimport AFQ.data.fetch as afd\nfrom AFQ.api.group import GroupAFQ\nimport AFQ.api.bundle_dict as abd\n\nafd.organize_stanford_data(clear_previous_afq=\"track\")\n\ntracking_params = dict(n_seeds=25000,\n                       random_seeds=True,\n                       rng_seed=42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Defining the segmentation params\nWe also refer to bundle recognition as the \"segmentation\" of the tractogram.\nParameters of this process are set through a dictionary input to the\n`segmentation_params` argument of the GroupAFQ object. In this case, we\nuse `abd.reco_bd(16)`, which tells pyAFQ to use the RecoBundles\nalgorithm for bundle recognition. This uses 16 bundles, there is also\nan atlas `abd.reco_bd(80)` which uses 80 bundles.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "myafq = GroupAFQ(\n    output_dir=op.join(afd.afq_home, 'stanford_hardi', 'derivatives',\n                       'recobundles'),\n    bids_path=op.join(afd.afq_home, 'stanford_hardi'),\n    # Set the algorithm to use RecoBundles for bundle recognition:\n    bundle_info=abd.reco_bd(16),\n    dwi_preproc_pipeline='vistasoft',\n    t1_preproc_pipeline='freesurfer',\n    tracking_params=tracking_params,\n    viz_backend_spec='plotly_no_gif')\n\nfig_files = myafq.export_all()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## References:\n .. [Garyfallidis2017] Garyfallidis, Eleftherios, Marc-Alexandre C\u00f4t\u00e9,\n                     Francois Rheault, Jasmeen Sidhu, Janice Hau, Laurent\n                     Petit, David Fortin, Stephen Cunanne, and Maxime\n                     Descoteaux. 2017.\u201cRecognition of White Matter Bundles\n                     Using Local and Global Streamline-Based Registration and\n                     Clustering.\u201dNeuroImage 170: 283-295.\n\n"
      ]
    }
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