Merge branch 'master' into enh/DipyMultiTensorSimulate

Author: oesteban

.travis.yml

@@ -2,7 +2,6 @@ cache:
   - apt
 language: python
 python:
-  - 2.6
   - 2.7
 env:
   - INSTALL_DEB_DEPENDECIES=true

CHANGES

@@ -1,6 +1,18 @@
 Next release
 ============
 
+* ENH: New mesh.MeshWarpMaths to operate on surface-defined warpings
+       (https://github.com/nipy/nipype/pull/1016)
+* FIX: Refactor P2PDistance, change name to ComputeMeshWarp, add regression tests,
+       fix bug in area weighted distance, and added optimizations
+       (https://github.com/nipy/nipype/pull/1016)
+* ENH: Add an option not to resubmit Nodes that finished running when using SGEGraph (https://github.com/nipy/nipype/pull/1002)
+* 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)
+* 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)
 * FIX: Fixed Camino output naming (https://github.com/nipy/nipype/pull/1061)
 * ENH: Add the average distance to ErrorMap (https://github.com/nipy/nipype/pull/1039)
 * ENH: Inputs with name_source can be now chained in cascade (https://github.com/nipy/nipype/pull/938)
@@ -37,6 +49,7 @@ Next release
 * ENH: Usability improvements in cluster environments (https://github.com/nipy/nipype/pull/1025)
 * ENH: ANTs JointFusion() (https://github.com/nipy/nipype/pull/1042)
 * ENH: Added csvReader() utility (https://github.com/nipy/nipype/pull/1044)
+* FIX: typo in nipype.interfaces.freesurfer.utils.py Tkregister2 (https://github.com/nipy/nipype/pull/1083)
 
 Release 0.10.0 (October 10, 2014)
 ============

bin/nipype_cmd

@@ -0,0 +1,8 @@
+#!/usr/bin/env python
+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+import sys
+from nipype.utils.nipype_cmd import main
+
+if __name__ == '__main__':
+    main(sys.argv)

doc/quickstart.rst

@@ -16,7 +16,7 @@ Downloading and installing
 Beginner's guide
 ================
 
-Beginner's tutorials (IPython Notebooks). `Availible here`__
+Beginner's tutorials (IPython Notebooks). `Available here`__
 
 Michael Notter's Nipype guide. `Available here`__
 

doc/users/index.rst

@@ -37,6 +37,7 @@
    saving_workflows
    spmmcr
    mipav
+   nipypecmd
 
 
 

doc/users/install.rst

@@ -107,7 +107,7 @@ recommendations.
 Must Have
 ~~~~~~~~~
 
-Python_ 2.6 - 2.7
+Python_ 2.7
 
 Nibabel_ 1.0 - 1.4
   Neuroimaging file i/o library

doc/users/nipypecmd.rst

@@ -0,0 +1,68 @@
+.. _nipypecmd:
+
+============================================================
+Running Nipype Interfaces from the command line (nipype_cmd)
+============================================================
+
+The primary use of Nipype_ is to build automated non-interactive pipelines.
+However, sometimes there is a need to run some interfaces quickly from the command line.
+This is especially useful when running Interfaces wrapping code that does not have 
+command line equivalents (nipy or SPM). Being able to run Nipype interfaces opens new
+possibilities such as inclusion of SPM processing steps in bash scripts.
+
+To run Nipype Interafces you need to use the nipype_cmd tool that should already be installed.
+The tool allows you to list Interfaces available in a certain package:
+
+.. testcode::
+	
+	
+	$nipype_cmd nipype.interfaces.nipy
+
+	Available Interfaces:
+	    SpaceTimeRealigner
+	    Similarity
+	    ComputeMask
+	    FitGLM
+	    EstimateContrast
+	    FmriRealign4d
+		
+After selecting a particular Interface you can learn what inputs it requires:
+
+.. testcode::
+	
+	
+	$nipype_cmd nipype.interfaces.nipy ComputeMask --help
+	
+	usage:nipype_cmd nipype.interfaces.nipy ComputeMask [-h] [--M M] [--cc CC]
+	                                                     [--ignore_exception IGNORE_EXCEPTION]
+	                                                     [--m M]
+	                                                     [--reference_volume REFERENCE_VOLUME]
+	                                                     mean_volume
+
+	Run ComputeMask
+
+	positional arguments:
+	  mean_volume           mean EPI image, used to compute the threshold for the
+	                        mask
+
+	optional arguments:
+	  -h, --help            show this help message and exit
+	  --M M                 upper fraction of the histogram to be discarded
+	  --cc CC               Keep only the largest connected component
+	  --ignore_exception IGNORE_EXCEPTION
+	                        Print an error message instead of throwing an
+	                        exception in case the interface fails to run
+	  --m M                 lower fraction of the histogram to be discarded
+	  --reference_volume REFERENCE_VOLUME
+	                        reference volume used to compute the mask. If none is
+	                        give, the mean volume is used.
+							
+Finally you can run run the Interface:
+
+.. testcode::
+	
+	$nipype_cmd nipype.interfaces.nipy ComputeMask mean.nii.gz
+
+All that from the command line without having to start python interpreter manually.
+
+.. include:: ../links_names.txt

doc/users/plugins.rst

@@ -136,13 +136,28 @@ particular node might use more resources than other nodes in a workflow.
   this local configuration::
 
      node.plugin_args = {'qsub_args': '-l nodes=1:ppn=3', 'overwrite': True}
+	 
+SGEGraph
+~~~~~~~~
+SGEGraph_ is a exuction plugin working with Sun Grid Engine that allows for
+submitting entire graph of dependent jobs at once. This way Nipype does not 
+need to run a monitoring process - SGE takes care of this.
+
+.. note::
+
+  When rerunning unfinished workflows using SGEGraph you may decide not to 
+  submit jobs for Nodes that previously finished running. This can speed up
+  execution, but new or modified inputs that would previously trigger a Node
+  to rerun will be ignored. The following option turns on this functionality::
+
+     workflow.run(plugin='SGEGraph', plugin_args = {'dont_resubmit_completed_jobs': True})
 
 LSF
 ---
 
-Submitting via LSF is almost identical to SGE above above except for the optional arguments field:
+Submitting via LSF is almost identical to SGE above above except for the optional arguments field::
 
-       workflow.run(plugin='LSF')
+    workflow.run(plugin='LSF')
 
 Optional arguments::
 
@@ -168,7 +183,7 @@ DAGMan
 ~~~~~~
 
 With its DAGMan_ component HTCondor_ (previously Condor) allows for submitting
-entire graphs of dependent jobs at once. With the ``CondorDAGMan`` plug-in
+entire graphs of dependent jobs at once (similar to SGEGraph_). With the ``CondorDAGMan`` plug-in
 Nipype can utilize this functionality to submit complete workflows directly and
 in a single step.  Consequently, and in contrast to other plug-ins, workflow
 execution returns almost instantaneously -- Nipype is only used to generate the

examples/fmri_ants_openfmri.py

@@ -241,7 +241,7 @@ def create_reg_workflow(name='registration'):
 
     warpmean = pe.Node(ants.ApplyTransforms(),
                        name='warpmean')
-    warpmean.inputs.input_image_type = 3
+    warpmean.inputs.input_image_type = 0
     warpmean.inputs.interpolation = 'Linear'
     warpmean.inputs.invert_transform_flags = [False, False]
     warpmean.inputs.terminal_output = 'file'
@@ -257,7 +257,7 @@ def create_reg_workflow(name='registration'):
     warpall = pe.MapNode(ants.ApplyTransforms(),
                          iterfield=['input_image'],
                          name='warpall')
-    warpall.inputs.input_image_type = 3
+    warpall.inputs.input_image_type = 0
     warpall.inputs.interpolation = 'Linear'
     warpall.inputs.invert_transform_flags = [False, False]
     warpall.inputs.terminal_output = 'file'
@@ -449,7 +449,7 @@ def create_fs_reg_workflow(name='registration'):
     Transform the mean image. First to anatomical and then to target
     """
     warpmean = Node(ants.ApplyTransforms(), name='warpmean')
-    warpmean.inputs.input_image_type = 3
+    warpmean.inputs.input_image_type = 0
     warpmean.inputs.interpolation = 'Linear'
     warpmean.inputs.invert_transform_flags = [False, False]
     warpmean.inputs.terminal_output = 'file'
@@ -464,7 +464,7 @@ def create_fs_reg_workflow(name='registration'):
     warpall = pe.MapNode(ants.ApplyTransforms(),
                          iterfield=['input_image'],
                          name='warpall')
-    warpall.inputs.input_image_type = 3
+    warpall.inputs.input_image_type = 0
     warpall.inputs.interpolation = 'Linear'
     warpall.inputs.invert_transform_flags = [False, False]
     warpall.inputs.terminal_output = 'file'