Merge branch 'fix_tutorials' of https://github.com/duboism/nipype into fix_tutorials

Conflicts:
	doc/devel/cmd_interface_devel.rst
	doc/devel/matlab_interface_devel.rst
	doc/users/tutorial_101.rst
	examples/fmri_ants_openfmri.py
	examples/rsfmri_vol_surface_preprocessing_nipy.py

Author: Mathieu Dubois

doc/devel/cmd_interface_devel.rst

@@ -39,7 +39,11 @@ above example we have used the ``desc`` metadata which holds human readable
 description of the input. The ``mandatory`` flag forces Nipype to throw an
 exception if the input was not set. ``exists`` is a special flag that works only
 for ``File traits`` and checks if the provided file exists. More details can be
+<<<<<<< HEAD
 found at :doc:`interface_specs`.
+=======
+found at :ref:`interface_specs`.
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
 
 The input and output specifications have to be connected to the our example
 interface class:

doc/devel/matlab_interface_devel.rst

@@ -21,6 +21,7 @@ Example 2
 +++++++++
 
 By subclassing :class:`nipype.interfaces.matlab.MatlabCommand` for your main class, and :class:`nipype.interfaces.matlab.MatlabInputSpec` for your input spec, you gain access to some useful MATLAB hooks
+<<<<<<< HEAD
 
 .. literalinclude:: matlab_example2.py
 
@@ -29,4 +30,13 @@ By subclassing :class:`nipype.interfaces.matlab.MatlabCommand` for your main cla
   You can download :download:`the source code of this example <matlab_example2.py>`.
 
 .. include:: ../links_names.txt
+=======
 
+.. literalinclude:: matlab_example2.py
+
+.. admonition:: Example source code
+
+  You can download :download:`the source code of this example <matlab_example2.py>`.
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
+
+.. include:: ../links_names.txt

doc/users/tutorial_101.rst

@@ -43,7 +43,11 @@ This would be equivalent to:
 .. testcode::
 
    realigner = pe.Node(interface=spm.Realign(infile=os.abspath('somefuncrun.nii'),
+<<<<<<< HEAD
                                              register_to_mean = True),
+=======
+                                             register_to_mean = True), 
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
                        name='realign')
 
 In Pythonic terms, this is saying that interface option in Node accepts
@@ -93,8 +97,13 @@ This results in a workflow containing two isolated nodes:
 
 **5. Connecting nodes to each other**
 
+<<<<<<< HEAD
 We want to connect the output produced by the node realignment to the input of
 the node smoothing. This is done as follows:
+=======
+We want to connect the output produced by realignment to the input of
+smoothing. This is done as follows:
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
 
 .. literalinclude:: tutorial_101.py
    :lines: 31

examples/fmri_ants_openfmri.py

@@ -76,6 +76,7 @@ def median(in_files):
 
     out_file: a 3D Nifti file
     """
+
     average = None
     for idx, filename in enumerate(filename_to_list(in_files)):
         img = nb.load(filename)
@@ -111,6 +112,13 @@ def create_reg_workflow(name='registration'):
         outputspec.anat2target_transform : FLIRT+FNIRT transform
         outputspec.transformed_files : transformed files in target space
         outputspec.transformed_mean : mean image in target space
+<<<<<<< HEAD
+=======
+
+    Example
+    -------
+        See code below
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
     """
 
     register = pe.Workflow(name=name)
@@ -196,11 +204,12 @@ def create_reg_workflow(name='registration'):
 
     """
     Compute registration between the subject's structural and MNI template
-    This is currently set to perform a very quick registration. However, the
-    registration can be made significantly more accurate for cortical
-    structures by increasing the number of iterations
-    All parameters are set using the example from:
-    #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
+
+        * All parameters are set using the example from: \
+          `newAntsExample.sh <https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh>`_
+        * This is currently set to perform a very quick registration. However,\
+          the registration can be made significantly more accurate for cortical\
+          structures by increasing the number of iterations.
     """
 
     reg = pe.Node(ants.Registration(), name='antsRegister')
@@ -242,7 +251,11 @@ def create_reg_workflow(name='registration'):
 
     merge = pe.Node(niu.Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
+<<<<<<< HEAD
     register.connect(reg, 'composite_transform', merge, 'in1')
+=======
+    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
 
     """
     Transform the mean image. First to anatomical and then to target
@@ -292,32 +305,46 @@ def create_reg_workflow(name='registration'):
     return register
 
 def get_aparc_aseg(files):
-    """Return the aparc+aseg.mgz file"""
+    """
+    Return the aparc+aseg.mgz file
+    """
+
     for name in files:
         if 'aparc+aseg.mgz' in name:
             return name
     raise ValueError('aparc+aseg.mgz not found')
 
+
 def create_fs_reg_workflow(name='registration'):
     """Create a FEAT preprocessing workflow together with freesurfer
 
     Parameters
     ----------
+<<<<<<< HEAD
 
+=======
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
         name : name of workflow (default: 'registration')
 
-    Inputs::
+    Inputs:
 
         inputspec.source_files : files (filename or list of filenames to register)
         inputspec.mean_image : reference image to use
         inputspec.target_image : registration target
 
-    Outputs::
+    Outputs:
 
         outputspec.func2anat_transform : FLIRT transform
         outputspec.anat2target_transform : FLIRT+FNIRT transform
         outputspec.transformed_files : transformed files in target space
         outputspec.transformed_mean : mean image in target space
+<<<<<<< HEAD
+=======
+
+    Example
+    -------
+        See code below
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
     """
 
     register = Workflow(name=name)
@@ -354,8 +381,13 @@ def create_fs_reg_workflow(name='registration'):
     register.connect(fssource, 'T1', convert, 'in_file')
 
     # Coregister the median to the surface
+<<<<<<< HEAD
     bbregister = Node(freesurfer.BBRegister(registered_file=True),
                     name='bbregister')
+=======
+    bbregister = Node(freesurfer.BBRegister(),
+                      name='bbregister')
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
     bbregister.inputs.init = 'fsl'
     bbregister.inputs.contrast_type = 't2'
     bbregister.inputs.out_fsl_file = True
@@ -375,7 +407,7 @@ def create_fs_reg_workflow(name='registration'):
     binarize = Node(fs.Binarize(min=0.5, out_type="nii.gz", dilate=1), name="binarize_aparc")
     register.connect(fssource, ("aparc_aseg", get_aparc_aseg), binarize, "in_file")
 
-    stripper = Node(fsl.ApplyMask(), name ='stripper')
+    stripper = Node(fsl.ApplyMask(), name='stripper')
     register.connect(binarize, "binary_file", stripper, "mask_file")
     register.connect(convert, 'out_file', stripper, 'in_file')
 
@@ -400,16 +432,17 @@ def create_fs_reg_workflow(name='registration'):
     convert2itk.inputs.fsl2ras = True
     convert2itk.inputs.itk_transform = True
     register.connect(bbregister, 'out_fsl_file', convert2itk, 'transform_file')
-    register.connect(inputnode, 'mean_image',convert2itk, 'source_file')
+    register.connect(inputnode, 'mean_image', convert2itk, 'source_file')
     register.connect(stripper, 'out_file', convert2itk, 'reference_file')
 
     """
     Compute registration between the subject's structural and MNI template
-    This is currently set to perform a very quick registration. However, the
-    registration can be made significantly more accurate for cortical
-    structures by increasing the number of iterations
-    All parameters are set using the example from:
-    #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
+
+        * All parameters are set using the example from: \
+          `newAntsExample.sh <https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh>`_
+        * This is currently set to perform a very quick registration. However,\
+          the registration can be made significantly more accurate for cortical\
+          structures by increasing the number of iterations.
     """
 
     reg = Node(ants.Registration(), name='antsRegister')
@@ -441,7 +474,11 @@ def create_fs_reg_workflow(name='registration'):
     reg.plugin_args = {'qsub_args': '-pe orte 4',
                        'sbatch_args': '--mem=6G -c 4'}
     register.connect(stripper, 'out_file', reg, 'moving_image')
+<<<<<<< HEAD
     register.connect(inputnode,'target_image', reg,'fixed_image')
+=======
+    register.connect(inputnode, 'target_image', reg, 'fixed_image')
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
 
     """
     Concatenate the affine and ants transforms into a list
@@ -451,7 +488,11 @@ def create_fs_reg_workflow(name='registration'):
 
     merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
+<<<<<<< HEAD
     register.connect(reg, 'composite_transform', merge, 'in1')
+=======
+    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
 
     """
     Transform the mean image. First to anatomical and then to target
@@ -463,8 +504,6 @@ def create_fs_reg_workflow(name='registration'):
     warpmean.inputs.invert_transform_flags = [False, False]
     warpmean.inputs.terminal_output = 'file'
     warpmean.inputs.args = '--float'
-    #warpmean.inputs.num_threads = 4
-    #warpmean.plugin_args = {'sbatch_args': '--mem=4G -c 4'}
 
     """
     Transform the remaining images. First to anatomical and then to target
@@ -488,11 +527,11 @@ def create_fs_reg_workflow(name='registration'):
     register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
     register.connect(warpall, 'output_image', outputnode, 'transformed_files')
 
-    register.connect(inputnode,'target_image', warpmean,'reference_image')
+    register.connect(inputnode, 'target_image', warpmean, 'reference_image')
     register.connect(inputnode, 'mean_image', warpmean, 'input_image')
     register.connect(merge, 'out', warpmean, 'transforms')
-    register.connect(inputnode,'target_image', warpall,'reference_image')
-    register.connect(inputnode,'source_files', warpall, 'input_image')
+    register.connect(inputnode, 'target_image', warpall, 'reference_image')
+    register.connect(inputnode, 'source_files', warpall, 'input_image')
     register.connect(merge, 'out', warpall, 'transforms')
 
     """
@@ -545,6 +584,7 @@ def get_subjectinfo(subject_id, base_dir, task_id, model_id):
     TR : float
         Repetition time
     """
+
     from glob import glob
     import os
     import numpy as np
@@ -574,17 +614,17 @@ def get_subjectinfo(subject_id, base_dir, task_id, model_id):
         runs = [int(val[-3:]) for val in files]
         run_ids.insert(idx, runs)
     json_info = os.path.join(base_dir, subject_id, 'BOLD',
-                                 'task%03d_run%03d' % (task_id, run_ids[task_id - 1][0]),
-                                 'bold_scaninfo.json')
+                             'task%03d_run%03d' % (task_id, run_ids[task_id - 1][0]),
+                             'bold_scaninfo.json')
     if os.path.exists(json_info):
         import json
         with open(json_info, 'rt') as fp:
             data = json.load(fp)
             TR = data['global']['const']['RepetitionTime']/1000.
     else:
         task_scan_key = os.path.join(base_dir, subject_id, 'BOLD',
-                                 'task%03d_run%03d' % (task_id, run_ids[task_id - 1][0]),
-                                 'scan_key.txt')
+                                     'task%03d_run%03d' % (task_id, run_ids[task_id - 1][0]),
+                                     'scan_key.txt')
         if os.path.exists(task_scan_key):
             TR = np.genfromtxt(task_scan_key)[1]
         else:
@@ -1087,7 +1127,7 @@ def get_subs(subject_id, conds, run_id, model_id, task_id):
                         help="FreeSurfer subjects directory (if available)")
     parser.add_argument("--target", dest="target_file",
                         help=("Target in MNI space. Best to use the MindBoggle "
-                              "template - only used with FreeSurfer"
+                              "template - only used with FreeSurfer "
                               "OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz"))
     args = parser.parse_args()
     outdir = args.outdir

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -528,7 +528,11 @@ def create_reg_workflow(name='registration'):
 
     merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
     register.connect(convert2itk, 'itk_transform', merge, 'in2')
+<<<<<<< HEAD
     register.connect(reg, 'composite_transform', merge, 'in1')
+=======
+    register.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
 
     """
     Transform the mean image. First to anatomical and then to target
@@ -619,15 +623,20 @@ def create_workflow(files,
                        name='median')
     wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
 
-    """Segment and Register
     """
+<<<<<<< HEAD
+=======
+    Segment and Register
+    """
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
 
     registration = create_reg_workflow(name='registration')
     wf.connect(calc_median, 'median_file', registration, 'inputspec.mean_image')
     registration.inputs.inputspec.subject_id = subject_id
     registration.inputs.inputspec.subjects_dir = subjects_dir
     registration.inputs.inputspec.target_image = target_file
 
+<<<<<<< HEAD
     """Quantify TSNR in each freesurfer ROI
     """
 
@@ -637,6 +646,8 @@ def create_workflow(files,
     wf.connect(tsnr, 'tsnr_file', get_roi_tsnr, 'in_file')
     wf.connect(registration, 'outputspec.aparc', get_roi_tsnr, 'segmentation_file')
 
+=======
+>>>>>>> cb80e24fc2a68758defcb16c7ab70092aa35b693
     """Use :class:`nipype.algorithms.rapidart` to determine which of the
     images in the functional series are outliers based on deviations in
     intensity or movement.