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   "source": [
    "<h1>Canlab Automated Preprocessing with Nipype</h1>\n",
    "<p><em>Written by Luke Chang 9/24/2014 for Columbia PTSD Data</em></p>\n",
    "<h2>Overview</h2>\n",
    "<p>We create a preprocessing pipeline using <a href=\"http://nipy.sourceforge.net/nipype/\">Nipype</a> for every subject and iterate over runs.  The pipeline is using spm for now to be consistent with Tor's original pipeline, but it is really easy to switch out any part with other software packages (e.g., fsl, nipy, afni, freesurfer, etc.). The pipeline can be automatically distributed to multiple processors using \"multiprocessing\" plugin or to Blanca using the \"sge\" plugin.  This <a href=\"http://ipython.org/notebook.html\">iPython notebook</a> can be run on your local computer via an SSH tunnel to Blanca.  Here is a helpful <a href=\"http://damontallen.github.io/IPython-quick-ref-sheets/\">link</a> with shortcuts for using iPython notebooks.  There is also a separate python script that can be run on an interactive node on blanca that works better right now until I solve the queue issue (currently waiting to submit jobs until previous one are out of completed state ~ 20 min)</p>\n",
    "<p>You will probably need to set up paths to my python build if you run this on Blanca (/projects/luch0518/software/).  The RC \"modules\" are outdated an cannot be updated without root access.</p>\n",
    "\n",
    "<h2>Preprocessing Steps</h2>\n",
    "<ol>\n",
    "<li>Slice timing</li>\n",
    "<li>Realignment to mean image</li>\n",
    "<li>Coregister anatomical to functional</li>\n",
    "<li>Segment anatomical with bias correction</li>\n",
    "<li>Normalize anatomical to MNI space</li>\n",
    "<li>Apply normalization to functionals</li>\n",
    "<li>Smooth - 8mm</li>\n",
    "<li>Artifact Detection - 3 sd of global mean (3.5 sd of root mean squared sucessive differences, mahalonobis distance across slices?  ask tor for more details, but ignore fo now)</li>\n",
    "<li>Create nuisance covariate matrix for glm (note the file has headers and NaNs for missing data in the derivative columns)</li>\n",
    "</ol>\n",
    "\n",
    "<h2>Figures Generated</h2>\n",
    "<ul>\n",
    "<li>Realignment Parameters (plot_realign/Realignment_parameters.pdf)</li>\n",
    "<li>MNI space single subject overlayed on mean warped functional (plot_normalization_check/Normalized_Functional_Check.pdf)</li>\n",
    "<li>Spikes identified using global signal (art/plot.rafunc.png)</li>\n",
    "</ul>\n",
    "<h2>To Do</h2>\n",
    "<ul>\n",
    "<li>Push general code to github and import functions</li>\n",
    "<li>Finish making all of Tor's Plots</li>\n",
    "<li>Figure out how Tor identifies spikes using mahalanobis distance</li>\n",
    "<li>Figure out how to fix delay introduced by completed states in Blanca Queue</li>\n",
    "<li>Add Sagittal plane to coregistration check</li>\n",
    "<li>Make function to look at overlap of all normalized binary masks.  Will need to add new node for this.</li>\n",
    "</ul>\n",
    "\n",
    "<h2>How to Set up iPython Notebook on Blanca</h2>\n",
    "<ol>\n",
    "<li>1) log on to Blanca and make sure you are using Luke's Python Anaconda distribution\n",
    "<br><br>\n",
    "```/projects/luch0518/software/anaconda```<br><br></li>\n",
    "<li>Start ipython notebook server on blanca.  Disable browser and specify port\n",
    "<br><br>```\n",
    "ipython notebook --no-browser --port=8889```\n",
    "<br><br>\n",
    "</li>\n",
    "\n",
    "<li>On your local computer, login to blanca using portforwarding\n",
    "<br><br>\n",
    "```\n",
    "ssh -N -f -L localhost:8889:localhost:8889 luch0518@blogin01.rc.colorado.edu\n",
    "```\n",
    "<br><br></li>\n",
    "\n",
    "<li>Open your browser to localhost:8889/tree</li>\n",
    "\n",
    "<li>The iPython notebook server should shutdown when you close the SSH connection, but just in case you can also kill the process.  Here is the command to find the process id:\n",
    "<br><br>\n",
    "```ps aux  | grep localhost:8889```\n",
    "<br><br></li>\n",
    "\n",
    "<li>Nipype can submit jobs automatically to Blanca.  You can check your queue or delete any process\n",
    "<br><br>\n",
    "<em>Check queue</em>\n",
    "<br><br>\n",
    "```qstat -u $USER```\n",
    "<br><br>\n",
    "<em>Delete Queue</em>\n",
    "<br><br>\n",
    "```qselect -u $USER | xargs qdel```<br></li>\n",
    "</ol>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Function definitions</h1>\n",
    "<p>The following code creates a preprocessing workflow.  We define several helper functions and also create a few new interfaces for new functionality to nipype such as plotting a montage of the normalization, creating a covariate matrix, and plotting realignment parameters.  It is quite easy to code wrappers for other functions including matlab.  In the preprocessing workflow, we define nodes and then how the output of each node is routed or connected to the input of the next node.  This creates a graphical model of the pipeline.</p>"
   ]
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   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Couldn't import dot_parser, loading of dot files will not be possible.\n"
     ]
    }
   ],
   "source": [
    "from nipype.interfaces import spm\n",
    "import nipype.interfaces.io as nio           # Data i/o\n",
    "import nipype.interfaces.utility as util     # utility\n",
    "from nipype.pipeline.engine import Node, Workflow\n",
    "from nipype.interfaces.base import BaseInterface, TraitedSpec, File, traits\n",
    "import nipype.algorithms.rapidart as ra      # artifact detection\n",
    "from nipype.interfaces.nipy.preprocess import ComputeMask\n",
    "import nipype.interfaces.matlab as mlab\n",
    "import os\n",
    "import nibabel as nib\n",
    "from IPython.display import Image\n",
    "import glob\n",
    "\n",
    "# Specify various inputs files for pipeline\n",
    "# spm_path = '/projects/ics/software/spm/spm8_r5236/'\n",
    "spm_path = '/Users/lukechang/Documents/Matlab/spm8/'\n",
    "canonical_file = spm_path + 'canonical/single_subj_T1.nii'\n",
    "template_file = spm_path + 'templates/T1.nii'\n",
    "\n",
    "# Set the way matlab should be called\n",
    "# mlab.MatlabCommand.set_default_matlab_cmd(\"matlab -nodesktop -nosplash -nojvm -noFigureWindows\")\n",
    "mlab.MatlabCommand.set_default_matlab_cmd(\"matlab -nodesktop -nosplash\")\n",
    "mlab.MatlabCommand.set_default_paths(spm_path)\n",
    "\n",
    "\n",
    "###############################\n",
    "## Functions\n",
    "###############################\n",
    "\n",
    "def get_n_slices(volume):\n",
    "    import nibabel as nib\n",
    "    nii = nib.load(volume)\n",
    "    return nii.get_shape()[2]\n",
    "\n",
    "def get_ta(tr, n_slices):\n",
    "    return tr - tr/float(n_slices)\n",
    "\n",
    "def get_slice_order(volume):\n",
    "    import nibabel as nib\n",
    "    nii = nib.load(volume)\n",
    "    n_slices = nii.get_shape()[2]\n",
    "    return range(1,n_slices+1)\n",
    "\n",
    "def get_vox_dims(volume):\n",
    "    import nibabel as nib\n",
    "    if isinstance(volume, list):\n",
    "        volume = volume[0]\n",
    "    nii = nib.load(volume)\n",
    "    hdr = nii.get_header()\n",
    "    voxdims = hdr.get_zooms()\n",
    "    return [float(voxdims[0]), float(voxdims[1]), float(voxdims[2])]\n",
    "\n",
    "class Plot_Coregistration_Montage_InputSpec(TraitedSpec):\n",
    "    wra_img = File(exists=True, mandatory=True) \n",
    "    canonical_img = File(exists=True, mandatory=True)\n",
    "    title = traits.Str(\"Normalized Functional Check\", usedefault=True)\n",
    "    \n",
    "class Plot_Coregistration_Montage_OutputSpec(TraitedSpec):\n",
    "    plot = File(exists=True)\n",
    "\n",
    "class Plot_Coregistration_Montage(BaseInterface):\n",
    "    # This function creates a plot of an axial montage of the average normalized functional data \n",
    "    # with the spm MNI space single subject T1 overlaid. Useful for checking normalization\n",
    "    input_spec = Plot_Coregistration_Montage_InputSpec\n",
    "    output_spec = Plot_Coregistration_Montage_OutputSpec\n",
    "\n",
    "    def _run_interface(self, runtime):\n",
    "        import nibabel as nib\n",
    "        from nilearn import plotting, datasets, image\n",
    "        from nipype.interfaces.base import isdefined\n",
    "        import numpy as np\n",
    "        import pylab as plt\n",
    "        import os\n",
    "        \n",
    "        wra_img = nib.load(self.inputs.wra_img)\n",
    "        canonical_img = nib.load(self.inputs.canonical_img)\n",
    "        title = self.inputs.title\n",
    "        mean_wraimg = image.mean_img(wra_img)\n",
    "        \n",
    "        if title != \"\":\n",
    "            filename = title.replace(\" \", \"_\")+\".pdf\"\n",
    "        else:\n",
    "            filename = \"plot.pdf\"\n",
    "        \n",
    "        fig = plotting.plot_anat(mean_wraimg, title=\"wrafunc & canonical single subject\", cut_coords=range(-40, 40, 10), display_mode='z')\n",
    "        fig.add_edges(canonical_img)     \n",
    "        fig.savefig(filename)\n",
    "        fig.close()\n",
    "        \n",
    "        self._plot = filename\n",
    "        \n",
    "        runtime.returncode=0\n",
    "        return runtime\n",
    "    \n",
    "    def _list_outputs(self):\n",
    "        outputs = self._outputs().get()\n",
    "        outputs[\"plot\"] = os.path.abspath(self._plot)\n",
    "        return outputs\n",
    "    \n",
    "class PlotRealignmentParametersInputSpec(TraitedSpec):\n",
    "    realignment_parameters = File(exists=True, mandatory=True)\n",
    "    outlier_files = File(exists=True)\n",
    "    title = traits.Str(\"Realignment parameters\", usedefault=True)\n",
    "    dpi = traits.Int(300, usedefault = True)\n",
    "    \n",
    "class PlotRealignmentParametersOutputSpec(TraitedSpec):\n",
    "    plot = File(exists=True)\n",
    "\n",
    "class PlotRealignmentParameters(BaseInterface):\n",
    "    #This function is adapted from Chris Gorgolewski's code and generates a plot of the realignment parameters\n",
    "\n",
    "    input_spec = PlotRealignmentParametersInputSpec\n",
    "    output_spec = PlotRealignmentParametersOutputSpec\n",
    "    \n",
    "    def _run_interface(self, runtime):\n",
    "        from nipype.interfaces.base import isdefined\n",
    "        import numpy as np\n",
    "        import pylab as plt\n",
    "        import os\n",
    "        realignment_parameters = np.loadtxt(self.inputs.realignment_parameters)\n",
    "        title = self.inputs.title\n",
    "        \n",
    "        F = plt.figure(figsize=(8.3,11.7))\n",
    "        F.text(0.5, 0.96, self.inputs.title, horizontalalignment='center')\n",
    "        ax1 = plt.subplot2grid((2,2),(0,0), colspan=2)\n",
    "        handles =ax1.plot(realignment_parameters[:,0:3])\n",
    "        ax1.legend(handles, [\"x translation\", \"y translation\", \"z translation\"], loc=0)\n",
    "        ax1.set_xlabel(\"image #\")\n",
    "        ax1.set_ylabel(\"mm\")\n",
    "        ax1.set_xlim((0,realignment_parameters.shape[0]-1))\n",
    "        ax1.set_ylim(bottom = realignment_parameters[:,0:3].min(), top = realignment_parameters[:,0:3].max())\n",
    "        \n",
    "        ax2 = plt.subplot2grid((2,2),(1,0), colspan=2)\n",
    "        handles= ax2.plot(realignment_parameters[:,3:6]*180.0/np.pi)\n",
    "        ax2.legend(handles, [\"pitch\", \"roll\", \"yaw\"], loc=0)\n",
    "        ax2.set_xlabel(\"image #\")\n",
    "        ax2.set_ylabel(\"degrees\")\n",
    "        ax2.set_xlim((0,realignment_parameters.shape[0]-1))\n",
    "        ax2.set_ylim(bottom=(realignment_parameters[:,3:6]*180.0/np.pi).min(), top= (realignment_parameters[:,3:6]*180.0/np.pi).max())\n",
    "        \n",
    "        if isdefined(self.inputs.outlier_files):\n",
    "            try:\n",
    "                outliers = np.loadtxt(self.inputs.outlier_files)\n",
    "            except IOError as e:\n",
    "                if e.args[0] == \"End-of-file reached before encountering data.\":\n",
    "                    pass\n",
    "                else:\n",
    "                    raise\n",
    "            else:\n",
    "                if outliers.size > 0:\n",
    "                    ax1.vlines(outliers, ax1.get_ylim()[0], ax1.get_ylim()[1])\n",
    "                    ax2.vlines(outliers, ax2.get_ylim()[0], ax2.get_ylim()[1])\n",
    "        \n",
    "        if title != \"\":\n",
    "            filename = title.replace(\" \", \"_\")+\".pdf\"\n",
    "        else:\n",
    "            filename = \"plot.pdf\"\n",
    "        \n",
    "        F.savefig(filename,papertype=\"a4\",dpi=self.inputs.dpi)\n",
    "        plt.clf()\n",
    "        plt.close()\n",
    "        del F\n",
    "        \n",
    "        self._plot = filename\n",
    "        \n",
    "        runtime.returncode=0\n",
    "        return runtime\n",
    "    \n",
    "    def _list_outputs(self):\n",
    "        outputs = self._outputs().get()\n",
    "        outputs[\"plot\"] = os.path.abspath(self._plot)\n",
    "        return outputs\n",
    "\n",
    "class Create_Covariates_InputSpec(TraitedSpec):\t\n",
    "    realignment_parameters = File(exists=True, mandatory=True) \n",
    "    spike_id = File(exists=True, mandatory=True)\n",
    "\n",
    "class Create_Covariates_OutputSpec(TraitedSpec):\n",
    "    covariates = File(exists=True)\n",
    "\n",
    "class Create_Covariates(BaseInterface):\n",
    "    # This function creates a matrix of the nuisance covariates used for the GLM.\n",
    "    # Note that the output file has headers and also has NaNs that may need to be accounted for.\n",
    "    input_spec = Create_Covariates_InputSpec\n",
    "    output_spec = Create_Covariates_OutputSpec\n",
    "\n",
    "    def _run_interface(self, runtime):\n",
    "        import pandas as pd\n",
    "        import numpy as np\n",
    "\n",
    "        ra = pd.read_table(self.inputs.realignment_parameters, header=None, sep=r\"\\s*\", names=['ra' + str(x) for x in range(1,7)])\n",
    "        spike = pd.read_table(self.inputs.spike_id, header=None,names=['Spikes'])\n",
    "\n",
    "        ra = ra-ra.mean() #mean center\n",
    "        ra[['rasq' + str(x) for x in range(1,7)]] = ra**2 #add squared\n",
    "        ra[['radiff' + str(x) for x in range(1,7)]] = pd.DataFrame(ra[ra.columns[0:6]].diff()) #derivative\n",
    "        ra[['radiffsq' + str(x) for x in range(1,7)]] = pd.DataFrame(ra[ra.columns[0:6]].diff())**2 #derivatives squared\n",
    "\n",
    "        #build spike regressors\n",
    "        for i,loc in enumerate(spike['Spikes']):\n",
    "            ra['spike' + str(i+1)] = 0\n",
    "            ra['spike' + str(i+1)].iloc[loc] = 1\n",
    "\n",
    "        filename = 'covariates.csv'\n",
    "        ra.to_csv(filename, index=False) #write out to file\n",
    "        self._covariates = filename\n",
    "\n",
    "        runtime.returncode=0\n",
    "        return runtime\n",
    "\n",
    "    def _list_outputs(self):\n",
    "        outputs = self._outputs().get()\n",
    "        outputs[\"covariates\"] = os.path.abspath(self._covariates)\n",
    "        return outputs\n",
    "\n",
    "    \n",
    "def create_preproc_func_pipeline(data_dir=None, subject_id=None, task_list=None):\n",
    "\n",
    "    ###############################\n",
    "    ## Set up Nodes\n",
    "    ###############################\n",
    "\n",
    "    #Setup Data Source for Input Data\n",
    "    #     data_dir = '/Users/lukechang/Dropbox/PTSD/Data/Imaging'\n",
    "    #     task_list = ['s1_r1Cond','s1_r1Ext','s1_r2Rec','s1_r2Ren']\n",
    "    ds = Node(nio.DataGrabber(infields=['subject_id', 'task_id'], outfields=['func', 'struc']),name='datasource')\n",
    "    ds.inputs.base_directory = os.path.abspath(data_dir + '/' + subject_id)\n",
    "    ds.inputs.template = '*'\n",
    "    ds.inputs.sort_filelist = True\n",
    "    ds.inputs.template_args = {'func': [['task_id']], 'struc':[]}\n",
    "    ds.inputs.field_template = {'func': 'Functional/Raw/%s/func.nii','struc': 'Structural/SPGR/spgr.nii'}\n",
    "    ds.inputs.subject_id = subject_id\n",
    "    ds.inputs.task_id = task_list\n",
    "    ds.iterables = ('task_id',task_list)\n",
    "    # ds.run().outputs #show datafiles\n",
    "\n",
    "    # #Setup Data Sinker for writing output files\n",
    "    # datasink = Node(nio.DataSink(), name='sinker')\n",
    "    # datasink.inputs.base_directory = '/path/to/output'\n",
    "    # workflow.connect(realigner, 'realignment_parameters', datasink, 'motion.@par')\n",
    "    # datasink.inputs.substitutions = [('_variable', 'variable'),('file_subject_', '')]\n",
    "\n",
    "    #Get Timing Acquisition for slice timing\n",
    "    tr = 2\n",
    "    ta = Node(interface=util.Function(input_names=['tr', 'n_slices'], output_names=['ta'],  function = get_ta), name=\"ta\")\n",
    "    ta.inputs.tr=tr\n",
    "\n",
    "    #Slice Timing: sequential ascending \n",
    "    slice_timing = Node(interface=spm.SliceTiming(), name=\"slice_timing\")\n",
    "    slice_timing.inputs.time_repetition = tr\n",
    "    slice_timing.inputs.ref_slice = 1\n",
    "\n",
    "    #Realignment - 6 parameters - realign to first image of very first series.\n",
    "    realign = Node(interface=spm.Realign(), name=\"realign\")\n",
    "    realign.inputs.register_to_mean = True\n",
    "\n",
    "    #Plot Realignment\n",
    "    plot_realign = Node(interface=PlotRealignmentParameters(), name=\"plot_realign\")\n",
    "\n",
    "    #Artifact Detection\n",
    "    art = Node(interface=ra.ArtifactDetect(), name=\"art\")\n",
    "    art.inputs.use_differences      = [True,False]\n",
    "    art.inputs.use_norm             = True\n",
    "    art.inputs.norm_threshold       = 1\n",
    "    art.inputs.zintensity_threshold = 3\n",
    "    art.inputs.mask_type            = 'file'\n",
    "    art.inputs.parameter_source     = 'SPM'\n",
    "    \n",
    "    #Coregister - 12 parameters, cost function = 'nmi', fwhm 7, interpolate, don't mask\n",
    "    #anatomical to functional mean across all available data.\n",
    "    coregister = Node(interface=spm.Coregister(), name=\"coregister\")\n",
    "    coregister.inputs.jobtype = 'estimate'\n",
    "\n",
    "    # Segment structural, gray/white/csf,mni, \n",
    "    segment = Node(interface=spm.Segment(), name=\"segment\")\n",
    "    segment.inputs.save_bias_corrected = True\n",
    "    \n",
    "    #Normalize - structural to MNI - then apply this to the coregistered functionals\n",
    "    normalize = Node(interface=spm.Normalize(), name = \"normalize\")\n",
    "    normalize.inputs.template = os.path.abspath(template_file)\n",
    "\n",
    "    #Plot normalization Check\n",
    "    plot_normalization_check = Node(interface=Plot_Coregistration_Montage(), name=\"plot_normalization_check\")\n",
    "    plot_normalization_check.inputs.canonical_img = canonical_file\n",
    "    \n",
    "    #Create Mask\n",
    "    compute_mask = Node(interface=ComputeMask(), name=\"compute_mask\")\n",
    "    #remove lower 5% of histogram of mean image\n",
    "    compute_mask.inputs.m = .05\n",
    "        \n",
    "    #Smooth\n",
    "    #implicit masking (.im) = 0, dtype = 0\n",
    "    smooth = Node(interface=spm.Smooth(), name = \"smooth\")\n",
    "    fwhmlist = [8]\n",
    "    smooth.iterables = ('fwhm',fwhmlist)\n",
    "\n",
    "    #Create Covariate matrix\n",
    "    make_covariates = Node(interface=Create_Covariates(), name=\"make_covariates\")\n",
    "   \n",
    "    ###############################\n",
    "    ## Create Pipeline\n",
    "    ###############################\n",
    "\n",
    "    Preprocessed = Workflow(name=\"Preprocessed\")\n",
    "    Preprocessed.base_dir = os.path.abspath(data_dir + '/' + subject_id + '/Functional')\n",
    "\n",
    "    Preprocessed.connect([  \n",
    "                        (ds, ta, [(('func', get_n_slices), \"n_slices\")]),\n",
    "                        (ta, slice_timing, [(\"ta\", \"time_acquisition\")]),\n",
    "                        (ds, slice_timing, [('func', 'in_files'),\n",
    "                                            (('func', get_n_slices), \"num_slices\"),\n",
    "                                            (('func', get_slice_order), \"slice_order\"),\n",
    "                                            ]),\n",
    "                        (slice_timing, realign, [('timecorrected_files', 'in_files')]),\n",
    "                        (realign, compute_mask, [('mean_image','mean_volume')]),\n",
    "                        (realign,coregister, [('mean_image', 'target')]),\n",
    "                        (ds,coregister, [('struc', 'source')]),\n",
    "                        (coregister,segment, [('coregistered_source', 'data')]),\n",
    "                        (segment, normalize, [('transformation_mat','parameter_file'),\n",
    "                                             ('bias_corrected_image', 'source'),]),\n",
    "                        (realign,normalize, [('realigned_files', 'apply_to_files'),\n",
    "                                             (('realigned_files', get_vox_dims), 'write_voxel_sizes')]),\n",
    "                        (normalize, smooth, [('normalized_files', 'in_files')]),\n",
    "                        (compute_mask,art,[('brain_mask','mask_file')]),\n",
    "                        (realign,art,[('realignment_parameters','realignment_parameters')]),\n",
    "                        (realign,art,[('realigned_files','realigned_files')]),\n",
    "                        (realign,plot_realign, [('realignment_parameters', 'realignment_parameters')]),\n",
    "                        (normalize, plot_normalization_check, [('normalized_files', 'wra_img')]),\n",
    "                        (realign, make_covariates, [('realignment_parameters', 'realignment_parameters')]),\n",
    "                        (art, make_covariates, [('outlier_files', 'spike_id')]),\n",
    "                      ])\n",
    "    return Preprocessed5\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Pipeline</h1>\n",
    "<p>Here is a directed acyclic graph of the preprocessing pipeline.  Each processing step in the workflow is a node in the graph. The node names will be directories created in the 'Preprocessed' folder.  You can easily run different pipelines on the same data without interfering with other pipelines.  All of the files you will need for subsequent analyses will be in each of these folders.  If you make changes to a node, nipype will only rerun the thing you changed and all dependent nodes.  Simply rerun the code.  Also, you can easily iterate over different settings without creating separate pipelines using iterables.  Here we are treating the smoothing parameter as an iterable.  It is currently set at 8mm, but you can input a vector of different sizes and it will run all of them.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:workflow:Converting dotfile: /Users/lukechang/Dropbox/PTSD/Data/Imaging/subj46153C/Preprocessed_Workflow.dot to png format\n"
     ]
    },
    {
     "data": {
      "image/png": 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mthcHASQxhsD9+OOPZejQoVKiRAlaCOPgxUskEAkEKJwiYRY5BhIgARIIQwJw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\nwY9w1Wo4RfjlaNPiiwAuWJ07dzYwLyVFWrduLYHQQbrrbbvttuLK+MYbb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LWjCYXXrppZJAttT0zywKot8mTpwoSXUxBFu0aCExL7gisnBY6jbkh7benQkBkgy//vrrQuxA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      "text/plain": [
       "<IPython.core.display.Image at 0x108db5fd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_dir = '/Users/lukechang/Dropbox/PTSD/Data/Imaging/'\n",
    "sub = 'subj46153C'\n",
    "Preprocessed = create_preproc_func_pipeline(data_dir = data_dir, subject_id=sub)\n",
    "Preprocessed.write_graph(data_dir + sub + \"/Preprocessed_Workflow.dot\")\n",
    "Image(filename=data_dir + sub + '/Preprocessed_Workflow.dot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Run Code</h1>\n",
    "<p>Running the pipeline is really easy and can be run on your local laptop, mac workstation, or blanca.  Basically, we create a Pipeline for a given subject and execute the pipeline iterating over runs. This particular code looks for available subjects and runs and automatically distributes them in parallel. It assumes your data is in the standard Canlab format (e.g., Imaging/Subjects/Functional/Raw/Run/func.nii)</p>\n",
    "<p>Pipeline.run() will excecute the pipeline.\n",
    "<BR>\n",
    "plugin=\"MultiProc\" will run in parallel on a local computer while plugin=\"PBS\" will submit jobs on Blanca</p>\n",
    "<p>Submitting jobs using PBS is really slow because nipype is waiting to submit jobs while they are in \"c\" completed state.  I still need to figure this out.  I have a python script called setup_ptsd.py in my Notebooks folder that I am running using an interactive node on Blanca.  It's really fast! (~10min to run a subject's 8 functional runs)</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:workflow:Converting dotfile: /Users/lukechang/Dropbox/PTSD/Data/Imaging/subj46153C/Preprocessed_Workflow.dot.png to png format\n",
      "INFO:workflow:['check', 'execution', 'logging']\n",
      "INFO:workflow:Running in parallel.\n",
      "INFO:workflow:Submitting 4 jobs\n",
      "INFO:workflow:Executing: datasource.bI.b1 ID: 0\n",
      "INFO:workflow:Executing: datasource.bI.b0 ID: 13\n",
      "INFO:workflow:Executing: datasource.bI.b2 ID: 26\n",
      "INFO:workflow:Executing: datasource.bI.b3 ID: 39\n",
      "INFO:workflow:[Job finished] jobname: datasource.bI.b1 jobid: 0\n",
      "INFO:workflow:Executing node datasource.bI.b1 in dir: /Users/lukechang/Dropbox/PTSD/Data/Imaging/subj46153C/Functional/Preprocessed/_task_id_s2_r2Rec/datasource\n",
      "INFO:workflow:Executing node datasource.bI.b0 in dir: /Users/lukechang/Dropbox/PTSD/Data/Imaging/subj46153C/Functional/Preprocessed/_task_id_s2_r1Cond/datasource\n",
      "INFO:workflow:Executing node datasource.bI.b2 in dir: /Users/lukechang/Dropbox/PTSD/Data/Imaging/subj46153C/Functional/Preprocessed/_task_id_s2_r1Ext/datasource\n",
      "INFO:workflow:Executing node datasource.bI.b3 in dir: /Users/lukechang/Dropbox/PTSD/Data/Imaging/subj46153C/Functional/Preprocessed/_task_id_s2_r2Ren/datasource\n",
      "INFO:workflow:[Job finished] jobname: datasource.bI.b0 jobid: 13\n",
      "INFO:workflow:[Job finished] jobname: datasource.bI.b2 jobid: 26\n",
      "INFO:workflow:[Job finished] jobname: datasource.bI.b3 jobid: 39\n",
      "INFO:workflow:Submitting 4 jobs\n",
      "INFO:workflow:Executing: ta.b1 ID: 1\n",
      "INFO:workflow:[Job finished] jobname: ta.b1 jobid: 1\n",
      "INFO:workflow:Executing: ta.b0 ID: 14\n",
      "INFO:workflow:[Job finished] jobname: ta.b0 jobid: 14\n",
      "INFO:workflow:Executing: ta.b2 ID: 27\n",
      "INFO:workflow:[Job finished] jobname: ta.b2 jobid: 27\n",
      "INFO:workflow:Executing: ta.b3 ID: 40\n",
      "INFO:workflow:[Job finished] jobname: ta.b3 jobid: 40\n",
      "INFO:workflow:Submitting 4 jobs\n",
      "INFO:workflow:Executing: slice_timing.b1 ID: 2\n",
      "INFO:workflow:[Job finished] jobname: slice_timing.b1 jobid: 2\n",
      "INFO:workflow:Executing: slice_timing.b0 ID: 15\n",
      "INFO:workflow:[Job finished] jobname: slice_timing.b0 jobid: 15\n",
      "INFO:workflow:Executing: slice_timing.b2 ID: 28\n",
      "INFO:workflow:[Job finished] jobname: slice_timing.b2 jobid: 28\n",
      "INFO:workflow:Executing: slice_timing.b3 ID: 41\n",
      "INFO:workflow:[Job finished] jobname: slice_timing.b3 jobid: 41\n",
      "INFO:workflow:Submitting 4 jobs\n",
      "INFO:workflow:Executing: realign.b1 ID: 3\n",
      "INFO:workflow:[Job finished] jobname: realign.b1 jobid: 3\n",
      "INFO:workflow:Executing: realign.b0 ID: 16\n",
      "INFO:workflow:[Job finished] jobname: realign.b0 jobid: 16\n",
      "INFO:workflow:Executing: realign.b2 ID: 29\n",
      "INFO:workflow:[Job finished] jobname: realign.b2 jobid: 29\n",
      "INFO:workflow:Executing: realign.b3 ID: 42\n",
      "INFO:workflow:[Job finished] jobname: realign.b3 jobid: 42\n",
      "INFO:workflow:Submitting 12 jobs\n",
      "INFO:workflow:Executing: coregister.b1 ID: 4\n",
      "INFO:workflow:[Job finished] jobname: coregister.b1 jobid: 4\n",
      "INFO:workflow:Executing: plot_realign.b1 ID: 6\n",
      "INFO:workflow:[Job finished] jobname: plot_realign.b1 jobid: 6\n",
      "INFO:workflow:Executing: compute_mask.b1 ID: 7\n",
      "INFO:workflow:[Job finished] jobname: compute_mask.b1 jobid: 7\n",
      "INFO:workflow:Executing: plot_realign.b0 ID: 17\n",
      "INFO:workflow:[Job finished] jobname: plot_realign.b0 jobid: 17\n",
      "INFO:workflow:Executing: compute_mask.b0 ID: 18\n",
      "INFO:workflow:[Job finished] jobname: compute_mask.b0 jobid: 18\n",
      "INFO:workflow:Executing: coregister.b0 ID: 21\n",
      "INFO:workflow:[Job finished] jobname: coregister.b0 jobid: 21\n",
      "INFO:workflow:Executing: coregister.b2 ID: 30\n",
      "INFO:workflow:[Job finished] jobname: coregister.b2 jobid: 30\n",
      "INFO:workflow:Executing: plot_realign.b2 ID: 35\n",
      "INFO:workflow:[Job finished] jobname: plot_realign.b2 jobid: 35\n",
      "INFO:workflow:Executing: compute_mask.b2 ID: 36\n",
      "INFO:workflow:[Job finished] jobname: compute_mask.b2 jobid: 36\n",
      "INFO:workflow:Executing: plot_realign.b3 ID: 43\n",
      "INFO:workflow:[Job finished] jobname: plot_realign.b3 jobid: 43\n",
      "INFO:workflow:Executing: coregister.b3 ID: 44\n",
      "INFO:workflow:[Job finished] jobname: coregister.b3 jobid: 44\n",
      "INFO:workflow:Executing: compute_mask.b3 ID: 46\n",
      "INFO:workflow:[Job finished] jobname: compute_mask.b3 jobid: 46\n",
      "INFO:workflow:Submitting 8 jobs\n",
      "INFO:workflow:Executing: segment.b1 ID: 5\n",
      "INFO:workflow:[Job finished] jobname: segment.b1 jobid: 5\n",
      "INFO:workflow:Executing: art.b1 ID: 10\n",
      "INFO:workflow:[Job finished] jobname: art.b1 jobid: 10\n",
      "INFO:workflow:Executing: art.b0 ID: 19\n",
      "INFO:workflow:[Job finished] jobname: art.b0 jobid: 19\n",
      "INFO:workflow:Executing: segment.b0 ID: 22\n",
      "INFO:workflow:[Job finished] jobname: segment.b0 jobid: 22\n",
      "INFO:workflow:Executing: segment.b2 ID: 31\n",
      "INFO:workflow:[Job finished] jobname: segment.b2 jobid: 31\n",
      "INFO:workflow:Executing: art.b2 ID: 37\n",
      "INFO:workflow:[Job finished] jobname: art.b2 jobid: 37\n",
      "INFO:workflow:Executing: segment.b3 ID: 45\n",
      "INFO:workflow:[Job finished] jobname: segment.b3 jobid: 45\n",
      "INFO:workflow:Executing: art.b3 ID: 50\n",
      "INFO:workflow:[Job finished] jobname: art.b3 jobid: 50\n",
      "INFO:workflow:Submitting 8 jobs\n",
      "INFO:workflow:Executing: normalize.b1 ID: 8\n",
      "INFO:workflow:[Job finished] jobname: normalize.b1 jobid: 8\n",
      "INFO:workflow:Executing: make_covariates.b1 ID: 11\n",
      "INFO:workflow:Executing: make_covariates.b0 ID: 20\n",
      "INFO:workflow:[Job finished] jobname: make_covariates.b0 jobid: 20\n",
      "INFO:workflow:Executing node make_covariates.b1 in dir: /Users/lukechang/Dropbox/PTSD/Data/Imaging/subj46153C/Functional/Preprocessed/_task_id_s2_r2Rec/make_covariates\n",
      "INFO:workflow:Executing: normalize.b0 ID: 23\n",
      "INFO:workflow:[Job finished] jobname: normalize.b0 jobid: 23\n",
      "INFO:workflow:Executing: normalize.b2 ID: 32\n",
      "INFO:workflow:[Job finished] jobname: normalize.b2 jobid: 32\n",
      "INFO:workflow:Executing: make_covariates.b2 ID: 38\n",
      "INFO:workflow:[Job finished] jobname: make_covariates.b2 jobid: 38\n",
      "INFO:workflow:Executing: normalize.b3 ID: 47\n",
      "INFO:workflow:[Job finished] jobname: normalize.b3 jobid: 47\n",
      "INFO:workflow:Executing: make_covariates.b3 ID: 51\n",
      "INFO:workflow:[Job finished] jobname: make_covariates.b3 jobid: 51\n",
      "INFO:workflow:Submitting 8 jobs\n",
      "INFO:workflow:Executing: plot_normalization_check.b1 ID: 9\n",
      "INFO:workflow:[Job finished] jobname: plot_normalization_check.b1 jobid: 9\n",
      "INFO:workflow:Executing: smooth.aI.a0.b1 ID: 12\n",
      "INFO:workflow:[Job finished] jobname: smooth.aI.a0.b1 jobid: 12\n",
      "INFO:workflow:Executing: plot_normalization_check.b0 ID: 24\n",
      "INFO:workflow:[Job finished] jobname: plot_normalization_check.b0 jobid: 24\n",
      "INFO:workflow:Executing: smooth.aI.a0.b0 ID: 25\n",
      "INFO:workflow:[Job finished] jobname: smooth.aI.a0.b0 jobid: 25\n",
      "INFO:workflow:Executing: smooth.aI.a0.b2 ID: 33\n",
      "INFO:workflow:[Job finished] jobname: smooth.aI.a0.b2 jobid: 33\n",
      "INFO:workflow:Executing: plot_normalization_check.b2 ID: 34\n",
      "INFO:workflow:[Job finished] jobname: plot_normalization_check.b2 jobid: 34\n",
      "INFO:workflow:Executing: smooth.aI.a0.b3 ID: 48\n",
      "INFO:workflow:[Job finished] jobname: smooth.aI.a0.b3 jobid: 48\n",
      "INFO:workflow:Executing: plot_normalization_check.b3 ID: 49\n",
      "INFO:workflow:[Job finished] jobname: plot_normalization_check.b3 jobid: 49\n",
      "INFO:workflow:[Job finished] jobname: make_covariates.b1 jobid: 11\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<networkx.classes.digraph.DiGraph at 0x10e242590>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Pipeline for subject\n",
    "data_dir='/Users/lukechang/Dropbox/PTSD/Data/Imaging'\n",
    "\n",
    "# subject_id = 'subj46153C'\n",
    "# task_list=['s1_r1Cond','s1_r1Ext','s2_r1Cond','s2_r2Rec','s1_r1Ext','s1_r2Ren','s2_r1Ext','s2_r2Ren']\n",
    "# Preprocessed = create_preproc_func_pipeline(data_dir=data_dir, subject_id = subject_id, task_list=task_list)\n",
    "\n",
    "sublist = sorted([x.split('/')[-1] for x in glob.glob(data_dir + '/subj*')])\n",
    "\n",
    "#Run first half of subjects\n",
    "for sub in reversed(sublist):\n",
    "    #Glob Subject runs as they vary\n",
    "    runlist = [x.split('/')[-1] for x in glob.glob(data_dir + '/' + sub + '/Functional/Raw/*')]\n",
    "    Preprocessed = create_preproc_func_pipeline(data_dir=data_dir, subject_id = sub, task_list=runlist)\n",
    "\n",
    "    print  data_dir + '/' + sub\n",
    "\n",
    "    # Write out pipeline as a DAG\n",
    "    Preprocessed.write_graph(dotfilename=data_dir + '/' + sub + \"/Functional/Preprocessed_Workflow.dot.svg\",format='svg')\n",
    "#     Preprocessed.write_graph(data_dir + '/' + subject_id + \"/Preprocessed_Workflow.dot.png\")\n",
    "#     Image(filename=data_dir + '/' + sub + \"/Functional/Preprocessed_Workflow.dot.png\")\n",
    "    Preprocessed.run(plugin='MultiProc', plugin_args={'n_procs' : 12}) \n",
    "#     Run on workstation in parallelPreprocessed.run(plugin='PBS') #Run on Blanca in parallel\n"
   ]
  }
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