Merge pull request #577 from satra/enh/tutorial_notebook

fix: updated tutorial to include download of data

Author: satra

examples/nipype_tutorial.ipynb

@@ -3,7 +3,7 @@
   "_draft": {
    "nbviewer_url": "gisting : nipype.ipynb\r\n"
   },
-  "name": "nipype"
+  "name": "nipype_tutorial"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -440,7 +440,7 @@
       "# Some preliminaries\n",
       "import os\n",
       "cwd = os.getcwd()\n",
-      "tutorial_dir = '/software/temp/nipype-tutorial/mgh/'\n",
+      "tutorial_dir = '/software/temp/nipype-tutorial/ohbm/'\n",
       "if not os.path.exists(tutorial_dir):\n",
       "    os.mkdir(tutorial_dir)\n",
       "os.chdir(tutorial_dir)"
@@ -457,18 +457,21 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "if not os.path.exists('ds107_raw_0.tgz'):\n",
-      "    os.system('curl -O https://openfmri.org/system/files/ds107_raw_0.tgz')"
-     ],
-     "language": "python",
-     "metadata": {},
-     "outputs": []
-    },
-    {
-     "cell_type": "code",
-     "collapsed": false,
-     "input": [
-      "subjects_dir = '/software/temp/nipype-tutorial/mgh/openfmri/ds107/'"
+      "import urllib\n",
+      "required_files = ['ds107/sub001/BOLD/task001_run001/bold.nii.gz',\n",
+      "                  'ds107/sub001/BOLD/task001_run002/bold.nii.gz',\n",
+      "                  'ds107/sub001/anatomy/highres001.nii.gz',\n",
+      "                  'ds107/sub044/BOLD/task001_run001/bold.nii.gz',\n",
+      "                  'ds107/sub044/BOLD/task001_run002/bold.nii.gz',\n",
+      "                  'ds107/sub044/anatomy/highres001.nii.gz'\n",
+      "                  ]\n",
+      "base_url = 'http://openfmri.aws.amazon.com.s3.amazonaws.com/'\n",
+      "for filepath in required_files:\n",
+      "    file_location = os.path.join(tutorial_dir, filepath)\n",
+      "    if not os.path.exists(file_location):\n",
+      "        print('Retrieving: ' + file_location)\n",
+      "        os.makedirs(os.path.dirname(file_location))\n",
+      "        urllib.urlretrieve(base_url + filepath, file_location)"
      ],
      "language": "python",
      "metadata": {},
@@ -1485,15 +1488,25 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from nipype.workflows.smri.freesurfer.utils import create_getmask_flow\n",
+      "config.set_default_config()\n",
+      "logging.update_logging(config)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "from nipype.workflows.fmri.fsl.preprocess import create_susan_smooth\n",
       "\n",
-      "getmask = create_getmask_flow()\n",
-      "getmask.inputs.inputspec.source_file = 'mean.nii'\n",
-      "getmask.inputs.inputspec.subject_id = 'sub001'\n",
-      "getmask.inputs.inputspec.subjects_dir = subjects_dir\n",
-      "getmask.inputs.inputspec.contrast_type = 't2'\n",
+      "smooth = create_susan_smooth()\n",
+      "smooth.inputs.inputnode.in_files = opap('output/realigned/_subject_id_sub044/rbold_out.nii')\n",
+      "smooth.inputs.inputnode.fwhm = 5\n",
+      "smooth.inputs.inputnode.mask_file = 'mask.nii'\n",
       "\n",
-      "getmask.run() # Will error because mean.nii does not exist"
+      "smooth.run() # Will error because mask.nii does not exist"
      ],
      "language": "python",
      "metadata": {},
@@ -1503,18 +1516,24 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "from nipype.interfaces.fsl.maths import MeanImage\n",
+      "from nipype.interfaces.fsl import BET, MeanImage, ImageMaths\n",
       "from nipype.pipeline.engine import Node\n",
       "\n",
-      "mi = Node(MeanImage(), name='mean_image')\n",
-      "mi.inputs.in_file = opap('ds107/sub001/BOLD/task001_run001/bold.nii.gz')\n",
+      "\n",
+      "remove_nan = Node(ImageMaths(op_string= '-nan'), name='nanremove')\n",
+      "remove_nan.inputs.in_file = opap('output/realigned/_subject_id_sub044/rbold_out.nii')\n",
+      "\n",
+      "mi = Node(MeanImage(), name='mean')\n",
+      "\n",
+      "mask = Node(BET(mask=True), name='mask')\n",
       "\n",
       "wf = Workflow('reuse')\n",
       "wf.base_dir = opap('.')\n",
-      "wf.connect(mi, 'out_file', getmask, 'inputspec.source_file')\n",
+      "wf.connect(remove_nan, 'out_file', mi, 'in_file')\n",
+      "wf.connect(mi, 'out_file', mask, 'in_file')\n",
+      "wf.connect(mask, 'out_file', smooth, 'inputnode.mask_file')\n",
+      "wf.connect(remove_nan, 'out_file', smooth, 'inputnode.in_files')\n",
       "\n",
-      "config.set_default_config()\n",
-      "logging.update_logging(config)\n",
       "wf.run()"
      ],
      "language": "python",
@@ -1536,10 +1555,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "print(getmask.list_node_names())\n",
-      "bin_node = getmask.get_node('threshold2')\n",
-      "bin_node.inputs.dilate = 3\n",
-      "bin_node.inputs.erode = 2"
+      "print(smooth.list_node_names())\n",
+      "\n",
+      "median = smooth.get_node('median')\n",
+      "median.inputs.op_string = '-k %s -p 60'"
      ],
      "language": "python",
      "metadata": {