preprocessing_notebook.ipynb

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   "source": [
    "from os.path import join as opj\n",
    "from dsp_functions import *\n",
    "from rsfmri_preproc import preproc_workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# specify the input directory\n",
    "input_dir = 'Path/To/BIDS/format/'"
   ],
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   "source": [
    "# specify the output directory and subdirectories\n",
    "\n",
    "experiment_dir = '/path/to/outputs'\n",
    "output_directory = 'datasink'  # subdirectory name for datasink\n",
    "working_directory = 'workingdir'  # subdirectory name for working directory (saving intermediate steps)"
   ],
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   "source": [
    "# list of subject identifiers, input your subject numbers.\n",
    "subject_list = ['01', '02', '03']  # is your iterable list detailing each subject number"
   ],
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   "cell_type": "code",
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   "source": [
    "# list of session identifiers\n",
    "ses_list = ['1', '2']  # is your iterable list identifying each session id number"
   ],
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   "source": [
    "# use this to specify the formatting of your input file path names - this is the BIDS standard format c. 2019\n",
    "anatomical_filename = opj('sub-ID{subject_id}', 'ses-{ses}', 'anat', 'sub-ID{subject_id}_ses-{ses}*.nii.gz')\n",
    "functional_filename = opj('sub-ID{subject_id}', 'ses-{ses}', 'func', 'sub-ID{subject_id}_ses-{ses}*.nii.gz')"
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   "cell_type": "code",
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   "source": [
    "# create the workflow (saves graphical output upon execution)\n",
    "pipeline = preproc_workflow(input_dir=input_dir,\n",
    "                             output_dir=experiment_dir,\n",
    "                             anat_file=anatomical_filename,\n",
    "                             func_file=functional_filename)"
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   "source": [
    "# runs the pipline\n",
    "pipeline.run('MultiProc', plugin_args={'n_procs' : 6}) # runs the entire workflow with 6 virtual processes"
   ],
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