{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interfaces\n",
    "\n",
    "In Nipype, interfaces are python modules that allow you to use various external packages (e.g. FSL, SPM or FreeSurfer), even if they themselves are written in another programming language than python. Such an interface knows what sort of options an external program has and how to execute it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interfaces vs. Workflows\n",
    "\n",
    "Interfaces are the building blocks that solve well-defined tasks. We solve more complex tasks by combining interfaces with workflows:\n",
    "\n",
    "<table style=\"width: 100%; font-size: 14px;\">\n",
    "  <thead>\n",
    "    <th style=\"text-align:left\">Interfaces</th>\n",
    "    <th style=\"text-align:left\">Workflows</th>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "    <tr>\n",
    "      <td style=\"text-align:left\">Wrap *unitary* tasks</td>\n",
    "      <td style=\"text-align:left\">Wrap *meta*-tasks\n",
    "        <li style=\"text-align:left\">implemented with nipype interfaces wrapped inside ``Node`` objects</li>\n",
    "        <li style=\"text-align:left\">subworkflows can also be added to a workflow without any wrapping</li>\n",
    "      </td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td style=\"text-align:left\">Keep track of the inputs and outputs, and check their expected types</td>\n",
    "      <td style=\"text-align:left\">Do not have inputs/outputs, but expose them from the interfaces wrapped inside</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td style=\"text-align:left\">Do not cache results (unless you use [interface caching](advanced_interfaces_caching.ipynb))</td>\n",
    "      <td style=\"text-align:left\">Cache results</td>\n",
    "    </tr>\n",
    "    <tr>\n",
    "      <td style=\"text-align:left\">Run by a nipype plugin</td>\n",
    "      <td style=\"text-align:left\">Run by a nipype plugin</td>\n",
    "    </tr>\n",
    "  </tbody>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate why interfaces are so useful, let's have a look at the brain extraction algorithm [BET](http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET) from FSL. Once in its original framework and once in the Nipype framework."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BET in the original framework\n",
    "\n",
    "Let's take a look at one of the T1 images we have in our dataset on which we want to run BET."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from nilearn.plotting import plot_anat\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plot_anat('/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz', title='original',\n",
    "          display_mode='ortho', dim=-1, draw_cross=False, annotate=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In its simplest form, you can run BET by just specifying the input image and tell it what to name the output image:\n",
    "\n",
    "    bet <input> <output>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "FILENAME=/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w\n",
    "\n",
    "bet ${FILENAME}.nii.gz /output/sub-01_ses-test_T1w_bet.nii.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_anat('/output/sub-01_ses-test_T1w_bet.nii.gz', title='original',\n",
    "          display_mode='ortho', dim=-1, draw_cross=False, annotate=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect! Exactly what we want. Hmm... what else could we want from BET? Well, it's actually a fairly complicated program. As is the case for all FSL binaries, just call it with the help flag `-h` to see all its options."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Usage:    bet <input> <output> [options]\r\n",
      "\r\n",
      "Main bet2 options:\r\n",
      "  -o          generate brain surface outline overlaid onto original image\r\n",
      "  -m          generate binary brain mask\r\n",
      "  -s          generate approximate skull image\r\n",
      "  -n          don't generate segmented brain image output\r\n",
      "  -f <f>      fractional intensity threshold (0->1); default=0.5; smaller values give larger brain outline estimates\r\n",
      "  -g <g>      vertical gradient in fractional intensity threshold (-1->1); default=0; positive values give larger brain outline at bottom, smaller at top\r\n",
      "  -r <r>      head radius (mm not voxels); initial surface sphere is set to half of this\r\n",
      "  -c <x y z>  centre-of-gravity (voxels not mm) of initial mesh surface.\r\n",
      "  -t          apply thresholding to segmented brain image and mask\r\n",
      "  -e          generates brain surface as mesh in .vtk format\r\n",
      "\r\n",
      "Variations on default bet2 functionality (mutually exclusive options):\r\n",
      "  (default)   just run bet2\r\n",
      "  -R          robust brain centre estimation (iterates BET several times)\r\n",
      "  -S          eye & optic nerve cleanup (can be useful in SIENA)\r\n",
      "  -B          bias field & neck cleanup (can be useful in SIENA)\r\n",
      "  -Z          improve BET if FOV is very small in Z (by temporarily padding end slices)\r\n",
      "  -F          apply to 4D FMRI data (uses -f 0.3 and dilates brain mask slightly)\r\n",
      "  -A          run bet2 and then betsurf to get additional skull and scalp surfaces (includes registrations)\r\n",
      "  -A2 <T2>    as with -A, when also feeding in non-brain-extracted T2 (includes registrations)\r\n",
      "\r\n",
      "Miscellaneous options:\r\n",
      "  -v          verbose (switch on diagnostic messages)\r\n",
      "  -h          display this help, then exits\r\n",
      "  -d          debug (don't delete temporary intermediate images)\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!bet -h"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that BET can also return a binary brain mask as a result of the skull-strip, which can be useful for masking our GLM analyses (among other things). Let's run it again including that option and see the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "\n",
    "FILENAME=/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w\n",
    "\n",
    "bet ${FILENAME}.nii.gz /output/sub-01_ses-test_T1w_bet.nii.gz -m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages/nilearn/image/resampling.py:512: UserWarning: Casting data from int32 to float32\n",
      "  warnings.warn(\"Casting data from %s to %s\" % (data.dtype.name, aux))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_anat('/output/sub-01_ses-test_T1w_bet_mask.nii.gz', title='original',\n",
    "          display_mode='ortho', dim=-1, draw_cross=False, annotate=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's look at the BET interface in Nipype. First, we have to import it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BET in the Nipype framework\n",
    "\n",
    "So how can we run BET in the Nipype framework?\n",
    "\n",
    "First things first, we need to import the ``BET`` class from Nipype's ``interfaces`` module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nipype.interfaces.fsl import BET"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the BET function accessible, we just have to specify the input and output file. And finally, we have to run the command. So exactly like in the original framework."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "skullstrip = BET()\n",
    "skullstrip.inputs.in_file = \"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\"\n",
    "skullstrip.inputs.out_file = \"/output/T1w_nipype_bet.nii.gz\"\n",
    "res = skullstrip.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we now look at the results from Nipype, we see that it is exactly the same as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_anat('/output/T1w_nipype_bet.nii.gz', title='original',\n",
    "          display_mode='ortho', dim=-1, draw_cross=False, annotate=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is not surprising, because Nipype used exactly the same bash code that we were using in the original framework example above. To verify this, we can call the ``cmdline`` function of the constructed BET instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bet /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz /output/T1w_nipype_bet.nii.gz\n"
     ]
    }
   ],
   "source": [
    "print(skullstrip.cmdline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another way to set the inputs on an interface object is to use them as keyword arguments when you construct the interface instance. Let's write the Nipype code from above in this way, but let's also add the option to create a brain mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "skullstrip = BET(in_file=\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\",\n",
    "                 out_file=\"/output/T1w_nipype_bet.nii.gz\",\n",
    "                 mask=True)\n",
    "res = skullstrip.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now if we plot this, we see again that this worked exactly as before. No surprise there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda-latest/envs/neuro/lib/python3.7/site-packages/nilearn/image/resampling.py:512: UserWarning: Casting data from int32 to float32\n",
      "  warnings.warn(\"Casting data from %s to %s\" % (data.dtype.name, aux))\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_anat('/output/T1w_nipype_bet_mask.nii.gz', title='after skullstrip',\n",
    "          display_mode='ortho', dim=-1, draw_cross=False, annotate=False);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Help Function\n",
    "\n",
    "But how did we know what the names of the input parameters are? In the original framework, we were able to just run ``BET``, without any additional parameters to get an information page. In the Nipype framework, we can achieve the same thing by using the ``help()`` function on an interface class. For the BET example, this is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wraps the executable command ``bet``.\n",
      "\n",
      "FSL BET wrapper for skull stripping\n",
      "\n",
      "For complete details, see the `BET Documentation.\n",
      "<https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/BET/UserGuide>`_\n",
      "\n",
      "Examples\n",
      "--------\n",
      ">>> from nipype.interfaces import fsl\n",
      ">>> btr = fsl.BET()\n",
      ">>> btr.inputs.in_file = 'structural.nii'\n",
      ">>> btr.inputs.frac = 0.7\n",
      ">>> btr.inputs.out_file = 'brain_anat.nii'\n",
      ">>> btr.cmdline\n",
      "'bet structural.nii brain_anat.nii -f 0.70'\n",
      ">>> res = btr.run() # doctest: +SKIP\n",
      "\n",
      "Inputs::\n",
      "\n",
      "        [Mandatory]\n",
      "        in_file: (a pathlike object or string representing an existing file)\n",
      "                input file to skull strip\n",
      "                argument: ``%s``, position: 0\n",
      "\n",
      "        [Optional]\n",
      "        out_file: (a pathlike object or string representing a file)\n",
      "                name of output skull stripped image\n",
      "                argument: ``%s``, position: 1\n",
      "        outline: (a boolean)\n",
      "                create surface outline image\n",
      "                argument: ``-o``\n",
      "        mask: (a boolean)\n",
      "                create binary mask image\n",
      "                argument: ``-m``\n",
      "        skull: (a boolean)\n",
      "                create skull image\n",
      "                argument: ``-s``\n",
      "        no_output: (a boolean)\n",
      "                Don't generate segmented output\n",
      "                argument: ``-n``\n",
      "        frac: (a float)\n",
      "                fractional intensity threshold\n",
      "                argument: ``-f %.2f``\n",
      "        vertical_gradient: (a float)\n",
      "                vertical gradient in fractional intensity threshold (-1, 1)\n",
      "                argument: ``-g %.2f``\n",
      "        radius: (an integer)\n",
      "                head radius\n",
      "                argument: ``-r %d``\n",
      "        center: (a list of at most 3 items which are an integer)\n",
      "                center of gravity in voxels\n",
      "                argument: ``-c %s``\n",
      "        threshold: (a boolean)\n",
      "                apply thresholding to segmented brain image and mask\n",
      "                argument: ``-t``\n",
      "        mesh: (a boolean)\n",
      "                generate a vtk mesh brain surface\n",
      "                argument: ``-e``\n",
      "        robust: (a boolean)\n",
      "                robust brain centre estimation (iterates BET several times)\n",
      "                argument: ``-R``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        padding: (a boolean)\n",
      "                improve BET if FOV is very small in Z (by temporarily padding end\n",
      "                slices)\n",
      "                argument: ``-Z``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        remove_eyes: (a boolean)\n",
      "                eye & optic nerve cleanup (can be useful in SIENA)\n",
      "                argument: ``-S``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        surfaces: (a boolean)\n",
      "                run bet2 and then betsurf to get additional skull and scalp surfaces\n",
      "                (includes registrations)\n",
      "                argument: ``-A``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        t2_guided: (a pathlike object or string representing a file)\n",
      "                as with creating surfaces, when also feeding in non-brain-extracted\n",
      "                T2 (includes registrations)\n",
      "                argument: ``-A2 %s``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        functional: (a boolean)\n",
      "                apply to 4D fMRI data\n",
      "                argument: ``-F``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        reduce_bias: (a boolean)\n",
      "                bias field and neck cleanup\n",
      "                argument: ``-B``\n",
      "                mutually_exclusive: functional, reduce_bias, robust, padding,\n",
      "                  remove_eyes, surfaces, t2_guided\n",
      "        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or\n",
      "                  'NIFTI_PAIR_GZ')\n",
      "                FSL output type\n",
      "        args: (a string)\n",
      "                Additional parameters to the command\n",
      "                argument: ``%s``\n",
      "        environ: (a dictionary with keys which are a bytes or None or a value\n",
      "                  of class 'str' and with values which are a bytes or None or a\n",
      "                  value of class 'str', nipype default value: {})\n",
      "                Environment variables\n",
      "\n",
      "Outputs::\n",
      "\n",
      "        out_file: (a pathlike object or string representing a file)\n",
      "                path/name of skullstripped file (if generated)\n",
      "        mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of binary brain mask (if generated)\n",
      "        outline_file: (a pathlike object or string representing a file)\n",
      "                path/name of outline file (if generated)\n",
      "        meshfile: (a pathlike object or string representing a file)\n",
      "                path/name of vtk mesh file (if generated)\n",
      "        inskull_mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of inskull mask (if generated)\n",
      "        inskull_mesh_file: (a pathlike object or string representing a file)\n",
      "                path/name of inskull mesh outline (if generated)\n",
      "        outskull_mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of outskull mask (if generated)\n",
      "        outskull_mesh_file: (a pathlike object or string representing a file)\n",
      "                path/name of outskull mesh outline (if generated)\n",
      "        outskin_mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of outskin mask (if generated)\n",
      "        outskin_mesh_file: (a pathlike object or string representing a file)\n",
      "                path/name of outskin mesh outline (if generated)\n",
      "        skull_mask_file: (a pathlike object or string representing a file)\n",
      "                path/name of skull mask (if generated)\n",
      "        skull_file: (a pathlike object or string representing a file)\n",
      "                path/name of skull file (if generated)\n",
      "\n",
      "References:\n",
      "-----------\n",
      "BibTeX('@article{JenkinsonBeckmannBehrensWoolrichSmith2012,author={M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, and S.M. Smith},title={FSL},journal={NeuroImage},volume={62},pages={782-790},year={2012},}', key='JenkinsonBeckmannBehrensWoolrichSmith2012')\n"
     ]
    }
   ],
   "source": [
    "BET.help()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, we get three different pieces of information. ***First***, a general explanation of the class.\n",
    "\n",
    "    Wraps command **bet**\n",
    "\n",
    "    Use FSL BET command for skull stripping.\n",
    "\n",
    "    For complete details, see the `BET Documentation.\n",
    "    <http://www.fmrib.ox.ac.uk/fsl/bet2/index.html>`_\n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "    >>> from nipype.interfaces import fsl\n",
    "    >>> from nipype.testing import  example_data\n",
    "    >>> btr = fsl.BET()\n",
    "    >>> btr.inputs.in_file = example_data('structural.nii')\n",
    "    >>> btr.inputs.frac = 0.7\n",
    "    >>> res = btr.run() # doctest: +SKIP\n",
    "\n",
    "***Second***, a list of all possible input parameters.\n",
    "\n",
    "    Inputs:\n",
    "\n",
    "        [Mandatory]\n",
    "        in_file: (an existing file name)\n",
    "            input file to skull strip\n",
    "            flag: %s, position: 0\n",
    "\n",
    "        [Optional]\n",
    "        args: (a string)\n",
    "            Additional parameters to the command\n",
    "            flag: %s\n",
    "        center: (a list of at most 3 items which are an integer (int or\n",
    "             long))\n",
    "            center of gravity in voxels\n",
    "            flag: -c %s\n",
    "        environ: (a dictionary with keys which are a value of type 'str' and\n",
    "             with values which are a value of type 'str', nipype default value:\n",
    "             {})\n",
    "            Environment variables\n",
    "        frac: (a float)\n",
    "            fractional intensity threshold\n",
    "            flag: -f %.2f\n",
    "        functional: (a boolean)\n",
    "            apply to 4D fMRI data\n",
    "            flag: -F\n",
    "            mutually_exclusive: functional, reduce_bias, robust, padding,\n",
    "             remove_eyes, surfaces, t2_guided\n",
    "        ignore_exception: (a boolean, nipype default value: False)\n",
    "            Print an error message instead of throwing an exception in case the\n",
    "            interface fails to run\n",
    "        mask: (a boolean)\n",
    "            create binary mask image\n",
    "            flag: -m\n",
    "        mesh: (a boolean)\n",
    "            generate a vtk mesh brain surface\n",
    "            flag: -e\n",
    "        no_output: (a boolean)\n",
    "            Don't generate segmented output\n",
    "            flag: -n\n",
    "        out_file: (a file name)\n",
    "            name of output skull stripped image\n",
    "            flag: %s, position: 1\n",
    "        outline: (a boolean)\n",
    "            create surface outline image\n",
    "            flag: -o\n",
    "        output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or\n",
    "             'NIFTI')\n",
    "            FSL output type\n",
    "        padding: (a boolean)\n",
    "            improve BET if FOV is very small in Z (by temporarily padding end\n",
    "            slices)\n",
    "            flag: -Z\n",
    "            mutually_exclusive: functional, reduce_bias, robust, padding,\n",
    "             remove_eyes, surfaces, t2_guided\n",
    "        radius: (an integer (int or long))\n",
    "            head radius\n",
    "            flag: -r %d\n",
    "        reduce_bias: (a boolean)\n",
    "            bias field and neck cleanup\n",
    "            flag: -B\n",
    "            mutually_exclusive: functional, reduce_bias, robust, padding,\n",
    "             remove_eyes, surfaces, t2_guided\n",
    "        remove_eyes: (a boolean)\n",
    "            eye & optic nerve cleanup (can be useful in SIENA)\n",
    "            flag: -S\n",
    "            mutually_exclusive: functional, reduce_bias, robust, padding,\n",
    "             remove_eyes, surfaces, t2_guided\n",
    "        robust: (a boolean)\n",
    "            robust brain centre estimation (iterates BET several times)\n",
    "            flag: -R\n",
    "            mutually_exclusive: functional, reduce_bias, robust, padding,\n",
    "             remove_eyes, surfaces, t2_guided\n",
    "        skull: (a boolean)\n",
    "            create skull image\n",
    "            flag: -s\n",
    "        surfaces: (a boolean)\n",
    "            run bet2 and then betsurf to get additional skull and scalp surfaces\n",
    "            (includes registrations)\n",
    "            flag: -A\n",
    "            mutually_exclusive: functional, reduce_bias, robust, padding,\n",
    "             remove_eyes, surfaces, t2_guided\n",
    "        t2_guided: (a file name)\n",
    "            as with creating surfaces, when also feeding in non-brain-extracted\n",
    "            T2 (includes registrations)\n",
    "            flag: -A2 %s\n",
    "            mutually_exclusive: functional, reduce_bias, robust, padding,\n",
    "             remove_eyes, surfaces, t2_guided\n",
    "        terminal_output: ('stream' or 'allatonce' or 'file' or 'none')\n",
    "            Control terminal output: `stream` - displays to terminal immediately\n",
    "            (default), `allatonce` - waits till command is finished to display\n",
    "            output, `file` - writes output to file, `none` - output is ignored\n",
    "        threshold: (a boolean)\n",
    "            apply thresholding to segmented brain image and mask\n",
    "            flag: -t\n",
    "        vertical_gradient: (a float)\n",
    "            vertical gradient in fractional intensity threshold (-1, 1)\n",
    "            flag: -g %.2f\n",
    "\n",
    "And ***third***, a list of all possible output parameters.\n",
    "\n",
    "    Outputs:\n",
    "\n",
    "        inskull_mask_file: (a file name)\n",
    "            path/name of inskull mask (if generated)\n",
    "        inskull_mesh_file: (a file name)\n",
    "            path/name of inskull mesh outline (if generated)\n",
    "        mask_file: (a file name)\n",
    "            path/name of binary brain mask (if generated)\n",
    "        meshfile: (a file name)\n",
    "            path/name of vtk mesh file (if generated)\n",
    "        out_file: (a file name)\n",
    "            path/name of skullstripped file (if generated)\n",
    "        outline_file: (a file name)\n",
    "            path/name of outline file (if generated)\n",
    "        outskin_mask_file: (a file name)\n",
    "            path/name of outskin mask (if generated)\n",
    "        outskin_mesh_file: (a file name)\n",
    "            path/name of outskin mesh outline (if generated)\n",
    "        outskull_mask_file: (a file name)\n",
    "            path/name of outskull mask (if generated)\n",
    "        outskull_mesh_file: (a file name)\n",
    "            path/name of outskull mesh outline (if generated)\n",
    "        skull_mask_file: (a file name)\n",
    "            path/name of skull mask (if generated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So here we see that Nipype also has output parameters. This is very practical. Because instead of typing the full path name to the mask volume, we can also more directly use the ``mask_file`` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/output/T1w_nipype_bet_mask.nii.gz\n"
     ]
    }
   ],
   "source": [
    "print(res.outputs.mask_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interface errors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To execute any interface class we use the ``run`` method on that object. For FSL, Freesurfer, and other programs, this will just make a system call with the command line we saw above. For MATLAB-based programs like SPM, it will actually generate a ``.m`` file and run a MATLAB process to execute it. All of that is handled in the background.\n",
    "\n",
    "But what happens if we didn't specify all necessary inputs? For instance, you need to give BET a file to work on. If you try and run it without setting the input ``in_file``, you'll get a Python exception before anything actually gets executed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ValueError: BET requires a value for input 'in_file'. For a list of required inputs, see BET.help()\n"
     ]
    }
   ],
   "source": [
    "skullstrip2 = BET()\n",
    "try:\n",
    "    skullstrip2.run()\n",
    "except(ValueError) as err:\n",
    "    print(\"ValueError:\", err)\n",
    "else:\n",
    "    raise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nipype also knows some things about what sort of values should get passed to the inputs and will raise (hopefully) informative exceptions when they are violated -- before anything gets processed. For example, BET just lets you say \"create a mask,\" it doesn't let you name it. You may forget this, and try to give it a name. In this case, Nipype will raise a ``TraitError`` telling you what you did wrong:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TraitError: The 'mask' trait of a BETInputSpec instance must be a boolean, but a value of 'mask_file.nii' <class 'str'> was specified.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    skullstrip.inputs.mask = \"mask_file.nii\"\n",
    "except(Exception) as err:\n",
    "    if \"TraitError\" in str(err.__class__):\n",
    "        print(\"TraitError:\", err)\n",
    "    else:\n",
    "        raise\n",
    "else:\n",
    "    raise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, Nipype knows that, for inputs corresponding to files you are going to process, they should exist in your file system. If you pass a string that doesn't correspond to an existing file, it will error and let you know:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TraitError: The 'in_file' trait of a BETInputSpec instance must be a pathlike object or string representing an existing file, but a value of '/data/oops_a_typo.nii' <class 'str'> was specified.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    skullstrip.inputs.in_file = \"/data/oops_a_typo.nii\"\n",
    "except(Exception) as err:\n",
    "    if \"TraitError\" in str(err.__class__):\n",
    "        print(\"TraitError:\", err)\n",
    "    else:\n",
    "        raise\n",
    "else:\n",
    "    raise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It turns out that for default output files, you don't even need to specify a name. Nipype will know what files are going to be created and will generate a name for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bet /data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz /home/neuro/workshop_weizmann/workshop/nipype/notebooks/sub-01_ses-test_T1w_brain.nii.gz\n"
     ]
    }
   ],
   "source": [
    "skullstrip = BET(in_file=\"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\")\n",
    "print(skullstrip.cmdline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that it is going to write the output file to the local directory.\n",
    "\n",
    "What if you just ran this interface and wanted to know what it called the file that was produced? As you might have noticed before, calling the ``run`` method returned an object called ``InterfaceResult`` that we saved under the variable ``res``.  Let's inspect that object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "inskull_mask_file = <undefined>\n",
      "inskull_mesh_file = <undefined>\n",
      "mask_file = <undefined>\n",
      "meshfile = <undefined>\n",
      "out_file = /home/neuro/workshop_weizmann/workshop/nipype/notebooks/sub-01_ses-test_T1w_brain.nii.gz\n",
      "outline_file = <undefined>\n",
      "outskin_mask_file = <undefined>\n",
      "outskin_mesh_file = <undefined>\n",
      "outskull_mask_file = <undefined>\n",
      "outskull_mesh_file = <undefined>\n",
      "skull_file = <undefined>\n",
      "skull_mask_file = <undefined>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res = skullstrip.run()\n",
    "print(res.outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that four possible files can be generated by BET. Here we ran it in the most simple way possible, so it just generated an ``out_file``, which is the skull-stripped image. Let's see what happens when we generate a mask. By the way, you can also set inputs at runtime by including them as arguments to the ``run`` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "inskull_mask_file = <undefined>\n",
      "inskull_mesh_file = <undefined>\n",
      "mask_file = /home/neuro/workshop_weizmann/workshop/nipype/notebooks/sub-01_ses-test_T1w_brain_mask.nii.gz\n",
      "meshfile = <undefined>\n",
      "out_file = /home/neuro/workshop_weizmann/workshop/nipype/notebooks/sub-01_ses-test_T1w_brain.nii.gz\n",
      "outline_file = <undefined>\n",
      "outskin_mask_file = <undefined>\n",
      "outskin_mesh_file = <undefined>\n",
      "outskull_mask_file = <undefined>\n",
      "outskull_mesh_file = <undefined>\n",
      "skull_file = <undefined>\n",
      "skull_mask_file = <undefined>\n",
      "\n"
     ]
    }
   ],
   "source": [
    "res2 = skullstrip.run(mask=True)\n",
    "print(res2.outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nipype knows that if you ask for a mask, BET is going to generate it in a particular way and makes that information available to you."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Why this is amazing!\n",
    "\n",
    "**A major motivating objective for Nipype is to streamline the integration of different analysis packages so that you can use the algorithms you feel are best suited to your particular problem.**\n",
    "\n",
    "Say that you want to use BET, as SPM does not offer a way to create an explicit mask from functional data, but that otherwise, you want your processing to occur in SPM. Although possible to do this in a MATLAB script, it might not be all that clean, particularly if you want your skullstrip to happen in the middle of your workflow (for instance, after realignment). Nipype provides a unified representation of interfaces across analysis packages.\n",
    "\n",
    "For more on this, check out the [Interfaces](basic_interfaces.ipynb) and the [Workflow](basic_workflow.ipynb) tutorial."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1\n",
    "Import `IsotropicSmooth` from `nipype.interfaces.fsl` and find  the `FSL` command that is being run. What are the mandatory inputs for this interface?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "solution2": "hidden",
    "solution2_first": true
   },
   "outputs": [],
   "source": [
    "# write your solution here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wraps the executable command ``fslmaths``.\n",
      "\n",
      "Use fslmaths to spatially smooth an image with a gaussian kernel.\n",
      "\n",
      "Inputs::\n",
      "\n",
      "        [Mandatory]\n",
      "        fwhm: (a float)\n",
      "                fwhm of smoothing kernel [mm]\n",
      "                argument: ``-s %.5f``, position: 4\n",
      "                mutually_exclusive: sigma\n",
      "        sigma: (a float)\n",
      "                sigma of smoothing kernel [mm]\n",
      "                argument: ``-s %.5f``, position: 4\n",
      "                mutually_exclusive: fwhm\n",
      "        in_file: (a pathlike object or string representing an existing file)\n",
      "                image to operate on\n",
      "                argument: ``%s``, position: 2\n",
      "\n",
      "        [Optional]\n",
      "        out_file: (a pathlike object or string representing a file)\n",
      "                image to write\n",
      "                argument: ``%s``, position: -2\n",
      "        internal_datatype: ('float' or 'char' or 'int' or 'short' or 'double'\n",
      "                  or 'input')\n",
      "                datatype to use for calculations (default is float)\n",
      "                argument: ``-dt %s``, position: 1\n",
      "        output_datatype: ('float' or 'char' or 'int' or 'short' or 'double'\n",
      "                  or 'input')\n",
      "                datatype to use for output (default uses input type)\n",
      "                argument: ``-odt %s``, position: -1\n",
      "        nan2zeros: (a boolean)\n",
      "                change NaNs to zeros before doing anything\n",
      "                argument: ``-nan``, position: 3\n",
      "        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or\n",
      "                  'NIFTI_PAIR_GZ')\n",
      "                FSL output type\n",
      "        args: (a string)\n",
      "                Additional parameters to the command\n",
      "                argument: ``%s``\n",
      "        environ: (a dictionary with keys which are a bytes or None or a value\n",
      "                  of class 'str' and with values which are a bytes or None or a\n",
      "                  value of class 'str', nipype default value: {})\n",
      "                Environment variables\n",
      "\n",
      "Outputs::\n",
      "\n",
      "        out_file: (a pathlike object or string representing an existing file)\n",
      "                image written after calculations\n",
      "\n",
      "References:\n",
      "-----------\n",
      "BibTeX('@article{JenkinsonBeckmannBehrensWoolrichSmith2012,author={M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, and S.M. Smith},title={FSL},journal={NeuroImage},volume={62},pages={782-790},year={2012},}', key='JenkinsonBeckmannBehrensWoolrichSmith2012')\n"
     ]
    }
   ],
   "source": [
    "from nipype.interfaces.fsl import IsotropicSmooth\n",
    "# all this information can be found when we run `help` method. \n",
    "# note that you can either provide `in_file` and `fwhm` or `in_file` and `sigma`\n",
    "IsotropicSmooth.help()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2\n",
    "Run the `IsotropicSmooth` for `/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz` file with a smoothing kernel 4mm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "solution2": "hidden",
    "solution2_first": true
   },
   "outputs": [],
   "source": [
    "# write your solution here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<nipype.interfaces.base.support.InterfaceResult at 0x7f546f0ed4d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smoothing = IsotropicSmooth()\n",
    "smoothing.inputs.in_file = \"/data/ds000114/sub-01/ses-test/anat/sub-01_ses-test_T1w.nii.gz\"\n",
    "smoothing.inputs.fwhm = 4\n",
    "smoothing.inputs.out_file = \"/output/T1w_nipype_smooth.nii.gz\"\n",
    "smoothing.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3\n",
    "Plot the output of your interface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "solution2": "hidden",
    "solution2_first": true
   },
   "outputs": [],
   "source": [
    "# write your solution here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 475.2x187.2 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# we will be using plot_anat from nilearn package\n",
    "from nilearn.plotting import plot_anat\n",
    "%matplotlib inline\n",
    "plot_anat('/output/T1w_nipype_smooth.nii.gz', title='after smoothing',\n",
    "          display_mode='ortho', dim=-1, draw_cross=False, annotate=False);"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}