Merge branch 'master' of github.com:nipy/nipype

Author: glatard

.gitignore

@@ -14,3 +14,4 @@
 .pydevproject
 .idea/
 /documentation.zip
+.DS_Store

CHANGES

@@ -12,6 +12,7 @@ Next release
 * FIX: FUGUE is now properly listing outputs. (https://github.com/nipy/nipype/pull/978)
 * ENH: Improved FieldMap-Based (FMB) workflow for correction of susceptibility distortions in EPI seqs.
        (https://github.com/nipy/nipype/pull/1019)
+* FIX: In the FSLXcommand _list_outputs function fixed for loop range (https://github.com/nipy/nipype/pull/1071)
 * ENH: Dropped support for now 7 years old Python 2.6 (https://github.com/nipy/nipype/pull/1069)
 * FIX: terminal_output is not mandatory anymore (https://github.com/nipy/nipype/pull/1070)
 * ENH: Added "nipype_cmd" tool for running interfaces from the command line (https://github.com/nipy/nipype/pull/795)
@@ -54,6 +55,12 @@ Next release
 * FIX: typo in nipype.interfaces.freesurfer.utils.py Tkregister2 (https://github.com/nipy/nipype/pull/1083)
 * FIX: SSHDataGrabber outputs now return full path to the grabbed/downloaded files. (https://github.com/nipy/nipype/pull/1086)
 * FIX: Add QA output for TSNR to resting workflow (https://github.com/nipy/nipype/pull/1088)
+* FIX: Change N4BiasFieldCorrection to use short tag for dimensionality (backward compatible) (https://github.com/nipy/nipype/pull/1096)
+* ENH: Added -newgrid input to Warp in AFNI (3dWarp wrapper) (https://github.com/nipy/nipype/pull/1128)
+* FIX: Fixed AFNI Copy interface to use positional inputs as required (https://github.com/nipy/nipype/pull/1131)
+* ENH: Added a check in Dcm2nii to check if nipype created the config.ini file and remove if true (https://github.com/nipy/nipype/pull/1132)
+* ENH: Use a while loop to wait for Xvfb (up to a max wait time "xvfb_max_wait" in config file, default 10)
+       (https://github.com/nipy/nipype/pull/1142)
 
 Release 0.10.0 (October 10, 2014)
 ============

README.rst

@@ -77,10 +77,10 @@ to `NeuroStars.org <http://neurostars.org>`_ with a *nipype* tag. `NeuroStars.or
        http://neurostars.org/t/nipype/
 	   
 
-To participate in the Nipype development related discussion please use the following mailing list::
+To participate in the Nipype development related discussions please use the following mailing list::
        
-       http://projects.scipy.org/mailman/listinfo/nipy-devel
-	   
+       http://mail.python.org/mailman/listinfo/neuroimaging
+
 Please add *[nipype]* to the subject line when posting on the mailing list.
 
 
@@ -124,5 +124,5 @@ There are interfaces to some GNU code but these are entirely optional.
 All trademarks referenced herein are property of their respective
 holders.
 
-Copyright (c) 2009-2014, NIPY Developers
+Copyright (c) 2009-2015, NIPY Developers
 All rights reserved.

doc/quickstart.rst

@@ -50,3 +50,13 @@ Developer guides
    devel/gitwash/index
 
 .. include:: links_names.txt
+
+Useful links for beginners
+===========================
+
+Getting started with Python - Tutorials. `Available here`__
+
+Python for Beginners `Available here`__
+
+   __ http://www.codecademy.com/en/tracks/python
+   __ https://www.python.org/about/gettingstarted/

doc/users/config_file.rst

@@ -125,6 +125,9 @@ Execution
     all pending jobs and checking for job completion. To be nice to cluster
     schedulers the default is set to 60 seconds.
 
+*xvfb_max_wait*
+    Maximum time (in seconds) to wait for Xvfb to start, if the _redirect_x parameter of an Interface is True.
+
 Example
 ~~~~~~~
 

doc/users/tutorial_101.rst

@@ -99,15 +99,15 @@ This results in a workflow containing two isolated nodes:
 
 **5. Connecting nodes to each other**
 
-We want to connect the output produced by realignment to the input of
-smoothing. This is done as follows.
+We want to connect the output produced by the node realignment to the input of
+the node smoothing. This is done as follows.
 
 .. testcode::
 
    workflow.connect(realigner, 'realigned_files', smoother, 'in_files')
 
-or alternatively, a more flexible notation can be used. Although not shown here,
-the following notation can be used to connect multiple outputs from one node to
+
+Although not shown here, the following notation can be used to connect multiple outputs from one node to
 multiple inputs (see step 7 below).
 
 .. testcode::
@@ -189,3 +189,22 @@ inside which are three folders: realign, smooth and artdetect (the names
 of the nodes). The outputs of these routines are in these folders.
 
 .. include:: ../links_names.txt
+
+.. glossary::
+
+   pipeline
+      Connected series of processes (processes can be run parallel and or sequential)
+
+   workflow 
+      (kind of synonymous to pipeline) = hosting the nodes
+
+   node 
+      = switching-point within a pipeline, you can give it a name (in the above example e.g. realigner), 
+      a node usually requires an or several inputs and will produce an or several outputs
+
+   interface 
+      = specific software (e.g. FSL, SPM ...) are wrapped in interfaces, within a node instances of an
+      interface can be run
+
+   modules 
+      for each interface the according modules have to be imported in the usual pythonic manner

examples/fmri_ants_openfmri.py

@@ -124,6 +124,7 @@ def create_reg_workflow(name='registration'):
                                                                  'transformed_files',
                                                                  'transformed_mean',
                                                                  'anat2target',
+                                                                 'mean2anat_mask'
                                                                  ]),
                          name='outputspec')
 
@@ -171,6 +172,13 @@ def create_reg_workflow(name='registration'):
     register.connect(mean2anat, 'out_matrix_file',
                      mean2anatbbr, 'in_matrix_file')
 
+    """
+    Create a mask of the median image coregistered to the anatomical image
+    """
+    
+    mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
+    register.connect(mean2anatbbr, 'out_file', mean2anat_mask, 'in_file')
+
     """
     Convert the BBRegister transformation to ANTS ITK format
     """
@@ -276,6 +284,8 @@ def create_reg_workflow(name='registration'):
     register.connect(warpall, 'output_image', outputnode, 'transformed_files')
     register.connect(mean2anatbbr, 'out_matrix_file',
                      outputnode, 'func2anat_transform')
+    register.connect(mean2anat_mask, 'mask_file',
+                     outputnode, 'mean2anat_mask')
     register.connect(reg, 'composite_transform',
                      outputnode, 'anat2target_transform')
 
@@ -333,7 +343,8 @@ def create_fs_reg_workflow(name='registration'):
                                                           'transformed_files',
                                                           'min_cost_file',
                                                           'anat2target',
-                                                          'aparc'
+                                                          'aparc',
+                                                          'mean2anat_mask'
                                                           ]),
                       name='outputspec')
 
@@ -349,7 +360,7 @@ def create_fs_reg_workflow(name='registration'):
     register.connect(fssource, 'T1', convert, 'in_file')
 
     # Coregister the median to the surface
-    bbregister = Node(freesurfer.BBRegister(),
+    bbregister = Node(freesurfer.BBRegister(registered_file=True),
                     name='bbregister')
     bbregister.inputs.init = 'fsl'
     bbregister.inputs.contrast_type = 't2'
@@ -359,6 +370,10 @@ def create_fs_reg_workflow(name='registration'):
     register.connect(inputnode, 'mean_image', bbregister, 'source_file')
     register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')
 
+    # Create a mask of the median coregistered to the anatomical image
+    mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
+    register.connect(bbregister, 'registered_file', mean2anat_mask, 'in_file')
+
     """
     use aparc+aseg's brain mask
     """
@@ -500,6 +515,8 @@ def create_fs_reg_workflow(name='registration'):
                      outputnode, 'out_reg_file')
     register.connect(bbregister, 'min_cost_file',
                      outputnode, 'min_cost_file')
+    register.connect(mean2anat_mask, 'mask_file',
+                     outputnode, 'mean2anat_mask')
     register.connect(reg, 'composite_transform',
                      outputnode, 'anat2target_transform')
     register.connect(merge, 'out', outputnode, 'transforms')
@@ -964,6 +981,8 @@ def get_subs(subject_id, conds, run_id, model_id, task_id):
         subs.append(('/model%03d/task%03d_' % (model_id, task_id), '/'))
         subs.append(('_bold_dtype_mcf_bet_thresh_dil', '_mask'))
         subs.append(('_output_warped_image', '_anat2target'))
+        subs.append(('median_flirt_brain_mask', 'median_brain_mask'))
+        subs.append(('median_bbreg_brain_mask', 'median_brain_mask'))
         return subs
 
     subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds', 'run_id',
@@ -998,6 +1017,7 @@ def get_subs(subject_id, conds, run_id, model_id, task_id):
                                      ('outputspec.motion_plots',
                                       'qa.motion.plots'),
                                      ('outputspec.mask', 'qa.mask')])])
+    wf.connect(registration, 'outputspec.mean2anat_mask', datasink, 'qa.mask.mean2anat')
     wf.connect(art, 'norm_files', datasink, 'qa.art.@norm')
     wf.connect(art, 'intensity_files', datasink, 'qa.art.@intensity')
     wf.connect(art, 'outlier_files', datasink, 'qa.art.@outlier_files')