{
 "metadata": {
  "name": "Task 3"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Convert two DTI images and run DTIFit"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from glob import glob\n",
      "from nipype.interfaces.dcm2nii import Dcm2nii\n",
      "from nipype.interfaces.fsl import DTIFit, BET\n",
      "\n",
      "import nipype.pipeline.engine as pe"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Version 1: Just convert 1 file"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convert = pe.Node(Dcm2nii(), name='convert_dicom')\n",
      "skull_stripper = pe.Node(BET(mask=True), name = 'skull_stripper')\n",
      "dtifit = pe.Node(DTIFit(), name = 'dtifit')\n",
      "\n",
      "convert_flow = pe.Workflow(name = 'convert_and_fit')\n",
      "convert_flow.connect([(convert, dtifit, [('converted_files', 'dwi'),\n",
      "                                         ('bvals', 'bvals'),\n",
      "                                         ('bvecs', 'bvecs')]),\n",
      "                      (convert, skull_stripper, [('converted_files', 'in_file')]),\n",
      "                      (skull_stripper, dtifit, [('mask_file', 'mask')])\n",
      "                      ])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fl = glob('/opt/data/NIPYPE_DATA/2475376/session1/DTI_mx_137/*.dcm')\n",
      "convert_flow.inputs.convert_dicom.source_names = fl[0] # or fl\n",
      "convert_flow.base_dir = '/mnt/mydir/'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "convert_flow.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,434 workflow INFO:\n",
        "\t ['check', 'execution', 'logging']\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,439 workflow INFO:\n",
        "\t Running serially.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,441 workflow INFO:\n",
        "\t Executing node convert_dicom in dir: /mnt/mydir/convert_and_fit/convert_dicom\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,442 workflow INFO:\n",
        "\t Collecting precomputed outputs\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,457 workflow INFO:\n",
        "\t Executing node skull_stripper in dir: /mnt/mydir/convert_and_fit/skull_stripper\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,459 workflow INFO:\n",
        "\t Collecting precomputed outputs\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,485 workflow INFO:\n",
        "\t Executing node dtifit in dir: /mnt/mydir/convert_and_fit/dtifit\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,487 workflow INFO:\n",
        "\t Collecting precomputed outputs\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<networkx.classes.digraph.DiGraph at 0x5036e90>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Version 2: Mapnodes"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convert = pe.MapNode(Dcm2nii(), name='convert_dicom', iterfield=['source_names'])\n",
      "skull_stripper = pe.MapNode(BET(mask=True), name = 'skull_stripper', iterfield=['in_file'])\n",
      "dtifit = pe.MapNode(DTIFit(), name = 'dtifit', iterfield=['dwi','bvals', 'bvecs','mask'])\n",
      "\n",
      "convert_flow = pe.Workflow(name = 'convert_and_fit_mapnode')\n",
      "convert_flow.connect([(convert, dtifit, [('converted_files', 'dwi'),\n",
      "                                         ('bvals', 'bvals'),\n",
      "                                         ('bvecs', 'bvecs')]),\n",
      "                      (convert, skull_stripper, [('converted_files', 'in_file')]),\n",
      "                      (skull_stripper, dtifit, [('mask_file', 'mask')])\n",
      "                      ])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fl = glob('/opt/data/NIPYPE_DATA/2475376/session*/DTI_mx_137/*-0001.dcm')\n",
      "convert_flow.inputs.convert_dicom.source_names = fl\n",
      "convert_flow.base_dir = '/mnt/mydir/'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "execution_graph = convert_flow.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,541 workflow INFO:\n",
        "\t ['check', 'execution', 'logging']\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,548 workflow INFO:\n",
        "\t Running serially.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,551 workflow INFO:\n",
        "\t Executing node convert_dicom in dir: /mnt/mydir/convert_and_fit_mapnode/convert_dicom\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,575 workflow INFO:\n",
        "\t Executing node skull_stripper in dir: /mnt/mydir/convert_and_fit_mapnode/skull_stripper\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,617 workflow INFO:\n",
        "\t Executing node dtifit in dir: /mnt/mydir/convert_and_fit_mapnode/dtifit\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convert_flow.write_graph()\n",
      "from IPython.display import Image\n",
      "Image(filename='/mnt/mydir/convert_and_fit_mapnode/graph.dot.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,723 workflow INFO:\n",
        "\t Converting dotfile: /mnt/mydir/convert_and_fit_mapnode/graph.dot to png format\n"
       ]
      },
      {
       "output_type": "pyout",
       "png": 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NTY3qugDaA7IeaFJycvKgQYNmzZrl7+/fpycfVoWamhorKyuVdmFlZQVZrycg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\n",
       "prompt_number": 9,
       "text": [
        "<IPython.core.display.Image at 0x50600d0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nodes = execution_graph.nodes()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_nodes(nodes, name):\n",
      "    return [node for node in nodes if name in str(node)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "get_nodes(nodes, 'dtifit')[0].result.outputs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "Bunch(FA=['/mnt/mydir/convert_and_fit_mapnode/dtifit/mapflow/_dtifit0/dtifit__FA.nii.gz', '/mnt/mydir/convert_and_fit_mapnode/dtifit/mapflow/_dtifit1/dtifit__FA.nii.gz'], L1=<undefined>, L2=<undefined>, L3=<undefined>, MD=['/mnt/mydir/convert_and_fit_mapnode/dtifit/mapflow/_dtifit0/dtifit__MD.nii.gz', '/mnt/mydir/convert_and_fit_mapnode/dtifit/mapflow/_dtifit1/dtifit__MD.nii.gz'], MO=<undefined>, S0=<undefined>, V1=<undefined>, V2=<undefined>, V3=<undefined>, tensor=<undefined>)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nipype.interfaces.io import DataGrabber, DataSink"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dg = pe.Node(DataGrabber(infields=['subject_id', 'session'], outfields=['diffusion']),\n",
      "             name = 'datasource')\n",
      "dg.inputs.base_directory = '/opt/data/NIPYPE_DATA/'\n",
      "dg.inputs.template = '%s/session%d/DTI*/*-0001.dcm'\n",
      "dg.inputs.subject_id = '2475376'\n",
      "dg.inputs.session = [1, 2]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convert_flow.connect(dg, 'diffusion', convert, 'source_names')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "execution_graph = convert_flow.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,824 workflow INFO:\n",
        "\t ['check', 'execution', 'logging']\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,844 workflow INFO:\n",
        "\t Running serially.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,846 workflow INFO:\n",
        "\t Executing node datasource in dir: /mnt/mydir/convert_and_fit_mapnode/datasource\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,872 workflow INFO:\n",
        "\t Executing node convert_dicom in dir: /mnt/mydir/convert_and_fit_mapnode/convert_dicom\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,907 workflow INFO:\n",
        "\t Executing node skull_stripper in dir: /mnt/mydir/convert_and_fit_mapnode/skull_stripper\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:32,951 workflow INFO:\n",
        "\t Executing node dtifit in dir: /mnt/mydir/convert_and_fit_mapnode/dtifit\n"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convert_flow.write_graph()\n",
      "from IPython.display import Image\n",
      "Image(filename='/mnt/mydir/convert_and_fit_mapnode/graph.dot.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,57 workflow INFO:\n",
        "\t Converting dotfile: /mnt/mydir/convert_and_fit_mapnode/graph.dot to png format\n"
       ]
      },
      {
       "output_type": "pyout",
       "png": 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qAAAIIVRfX3/79u0rV66kpKSUlZVxudyJEycmJSWNGzeux/0dQdb3CXw+/+zZ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\n",
       "prompt_number": 17,
       "text": [
        "<IPython.core.display.Image at 0x511d750>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ds = pe.Node(DataSink(), name='sinker')\n",
      "ds.inputs.base_directory = '/mnt/mydir/outputs'\n",
      "convert_flow.connect(skull_stripper, 'mask_file', ds, 'mask')\n",
      "convert_flow.connect(dtifit, 'FA', ds, 'dti.@FA')\n",
      "convert_flow.connect(dtifit, 'MD', ds, 'dti.@MD')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convert_flow.run()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,102 workflow INFO:\n",
        "\t ['check', 'execution', 'logging']\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,113 workflow INFO:\n",
        "\t Running serially.\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,115 workflow INFO:\n",
        "\t Executing node datasource in dir: /mnt/mydir/convert_and_fit_mapnode/datasource\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,130 workflow INFO:\n",
        "\t Executing node convert_dicom in dir: /mnt/mydir/convert_and_fit_mapnode/convert_dicom\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,156 workflow INFO:\n",
        "\t Executing node skull_stripper in dir: /mnt/mydir/convert_and_fit_mapnode/skull_stripper\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,199 workflow INFO:\n",
        "\t Executing node dtifit in dir: /mnt/mydir/convert_and_fit_mapnode/dtifit\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,230 workflow INFO:\n",
        "\t Executing node sinker in dir: /mnt/mydir/convert_and_fit_mapnode/sinker\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,232 workflow INFO:\n",
        "\t Collecting precomputed outputs\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<networkx.classes.digraph.DiGraph at 0x5051190>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "convert_flow.write_graph()\n",
      "from IPython.display import Image\n",
      "Image(filename='/mnt/mydir/convert_and_fit_mapnode/graph.dot.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "120909-13:17:33,364 workflow INFO:\n",
        "\t Converting dotfile: /mnt/mydir/convert_and_fit_mapnode/graph.dot to png format\n"
       ]
      },
      {
       "output_type": "pyout",
       "png": 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dyK8xFRUVVlZWK1eu1G4ZQM/pVJ/GEokkOjq6t7f31q1b9vb22i4HvKD9+/evW7euqqrK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\nD7IeAAAU3/8HoN+PhrWtzsAAAAAASUVORK5CYII=\n",
       "prompt_number": 20,
       "text": [
        "<IPython.core.display.Image at 0x5051310>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dg.iterables = ('subject_id', ['2475376', '3313349',  '9630905'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "eg = convert_flow.run('MultiProc', plugin_args={'n_procs': 2})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    }
   ],
   "metadata": {}
  }
 ]
}