further doc fixes - removed parallelization for now.

Author: chrisgorgo

circle.yml

@@ -21,17 +21,17 @@ test:
   override:
     - set -o pipefail && cd doc && make html 2>&1 | tee ~/log.txt
     - cat ~/log.txt && if grep -q "ERROR" ~/log.txt; then false; else true; fi
-    - if [ "$CIRCLE_NODE_INDEX" = "0" ]; then . /usr/share/fsl/5.0/etc/fslconf/fsl.sh && python ~/nipype/tools/run_examples.py fmri_fsl_feeds Linear l1pipeline; fi:
+    - . /usr/share/fsl/5.0/etc/fslconf/fsl.sh && python ~/nipype/tools/run_examples.py fmri_fsl_feeds Linear l1pipeline:
         pwd: ../examples
-    - if [ "$CIRCLE_NODE_INDEX" = "1" ]; then . /usr/share/fsl/5.0/etc/fslconf/fsl.sh && python ~/nipype/tools/run_examples.py fmri_spm_dartel Linear level1 l2pipeline; fi:
+    - . /usr/share/fsl/5.0/etc/fslconf/fsl.sh && python ~/nipype/tools/run_examples.py fmri_spm_dartel Linear level1 l2pipeline:
         pwd: ../examples
         environment:
           SPMMCRCMD: "$HOME/spm12/run_spm12.sh $HOME/mcr/v85/ script"
           FORCE_SPMMCR: 1
         timeout: 1600
-    - if [ "$CIRCLE_NODE_INDEX" = "0" ]; then . /usr/share/fsl/5.0/etc/fslconf/fsl.sh && python ~/nipype/tools/run_examples.py fmri_fsl_reuse Linear level1_workflow; fi:
+    - . /usr/share/fsl/5.0/etc/fslconf/fsl.sh && python ~/nipype/tools/run_examples.py fmri_fsl_reuse Linear level1_workflow:
         pwd: ../examples
-    - if [ "$CIRCLE_NODE_INDEX" = "0" ]; then . /usr/share/fsl/5.0/etc/fslconf/fsl.sh && python ~/nipype/tools/run_examples.py fmri_spm_nested Linear level1 l2pipeline; fi:
+    - . /usr/share/fsl/5.0/etc/fslconf/fsl.sh && python ~/nipype/tools/run_examples.py fmri_spm_nested Linear level1 l2pipeline:
         pwd: ../examples
         environment:
           SPMMCRCMD: "$HOME/spm12/run_spm12.sh $HOME/mcr/v85/ script"

doc/users/config_file.rst

@@ -80,8 +80,8 @@ Execution
 *remove_unnecessary_outputs*
 	This will remove any interface outputs not needed by the workflow. If the
 	required outputs from a node changes, rerunning the workflow will rerun the
-	node. Outputs of leaf nodes (nodes whose outputs are not connected to any 
-	other nodes) will never be deleted independent of this parameter. (possible 
+	node. Outputs of leaf nodes (nodes whose outputs are not connected to any
+	other nodes) will never be deleted independent of this parameter. (possible
 	values: ``true`` and ``false``; default value: ``true``)
 
 *try_hard_link_datasink*
@@ -129,7 +129,7 @@ Execution
     If this is set to True, the node's output directory will contain full
     parameterization of any iterable, otherwise parameterizations over 32
     characters will be replaced by their hash. (possible values: ``true`` and
-	``false``; default value: ``true``)
+    ``false``; default value: ``true``)
 
 *poll_sleep_duration*
     This controls how long the job submission loop will sleep between submitting
@@ -146,7 +146,7 @@ Example
 
 	[logging]
 	workflow_level = DEBUG
-	
+
 	[execution]
 	stop_on_first_crash = true
 	hash_method = timestamp
@@ -156,9 +156,9 @@ Workflow.config property has a form of a nested dictionary reflecting the
 structure of the .cfg file.
 
 ::
-  
+
   myworkflow = pe.Workflow()
-  myworkflow.config['execution'] = {'stop_on_first_rerun': 'True', 
+  myworkflow.config['execution'] = {'stop_on_first_rerun': 'True',
                                      'hash_method': 'timestamp'}
 
 You can also directly set global config options in your workflow script. An

doc/users/plugins.rst

@@ -71,7 +71,7 @@ a local system.
 
 Optional arguments::
 
-  n_procs :  Number of processes to launch in parallel, if not set number of 
+  n_procs :  Number of processes to launch in parallel, if not set number of
   processors/threads will be automatically detected
 
 To distribute processing on a multicore machine, simply call::
@@ -118,7 +118,7 @@ Optional arguments::
 
 For example, the following snippet executes the workflow on myqueue with
 a custom template::
- 
+
        workflow.run(plugin='SGE',
           plugin_args=dict(template='mytemplate.sh', qsub_args='-q myqueue')
 
@@ -136,18 +136,18 @@ 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 an execution plugin working with Sun Grid Engine that allows for
-submitting entire graph of dependent jobs at once. This way Nipype does not 
+submitting entire graph of dependent jobs at once. This way Nipype does not
 need to run a monitoring process - SGE takes care of this. The use of SGEGraph_
-is preferred over SGE_ since the latter adds unnecessary load on the submit 
+is preferred over SGE_ since the latter adds unnecessary load on the submit
 machine.
 
 .. note::
 
-  When rerunning unfinished workflows using SGEGraph you may decide not to 
+  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::
@@ -177,20 +177,20 @@ Optional arguments::
 
   template: custom template file to use
   sbatch_args: any other command line args to be passed to bsub.
-  
-  
+
+
 SLURMGraph
 ~~~~~~~~~~
 SLURMGraph_ is an execution plugin working with SLURM that allows for
-submitting entire graph of dependent jobs at once. This way Nipype does not 
+submitting entire graph of dependent jobs at once. This way Nipype does not
 need to run a monitoring process - SLURM takes care of this. The use of SLURMGraph_
-plugin is preferred over the vanilla SLURM_ plugin since the latter adds 
-unnecessary load on the submit machine. 
+plugin is preferred over the vanilla SLURM_ plugin since the latter adds
+unnecessary load on the submit machine.
 
 
 .. note::
 
-  When rerunning unfinished workflows using SLURMGraph you may decide not to 
+  When rerunning unfinished workflows using SLURMGraph 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::
@@ -205,11 +205,11 @@ DAGMan
 ~~~~~~
 
 With its DAGMan_ component HTCondor_ (previously Condor) allows for submitting
-entire graphs of dependent jobs at once (similar to SGEGraph_ and SLURMGaaoh_). 
-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 workflow graph, 
+entire graphs of dependent jobs at once (similar to SGEGraph_ and SLURMGraph_). 
+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 workflow graph,
 while job scheduling and dependency resolution are entirely managed by HTCondor_.
 
 Please note that although DAGMan_ supports specification of data dependencies
@@ -320,4 +320,3 @@ Optional arguments::
 .. _HTCondor documentation: http://research.cs.wisc.edu/htcondor/manual
 .. _DMTCP: http://dmtcp.sourceforge.net
 .. _SLURM: http://slurm.schedmd.com/
-

examples/fmri_ants_openfmri.py

@@ -106,8 +106,6 @@ def create_reg_workflow(name='registration'):
         outputspec.transformed_files : transformed files in target space
         outputspec.transformed_mean : mean image in target space
 
-    Example
-    -------
     """
 
     register = pe.Workflow(name=name)
@@ -304,8 +302,6 @@ def create_fs_reg_workflow(name='registration'):
     Parameters
     ----------
 
-    ::
-
         name : name of workflow (default: 'registration')
 
     Inputs::
@@ -321,9 +317,6 @@ def create_fs_reg_workflow(name='registration'):
         outputspec.transformed_files : transformed files in target space
         outputspec.transformed_mean : mean image in target space
 
-    Example
-    -------
-
     """
 
     register = Workflow(name=name)
@@ -1106,7 +1099,7 @@ def get_subs(subject_id, conds, run_id, model_id, task_id):
                           'task%03d' % int(args.task))
     derivatives = args.derivatives
     if derivatives is None:
-       derivatives = False
+        derivatives = False
     wf = analyze_openfmri_dataset(data_dir=os.path.abspath(args.datasetdir),
                                   subject=args.subject,
                                   model_id=int(args.model),

examples/rsfmri_vol_surface_preprocessing.py

@@ -370,8 +370,6 @@ def create_reg_workflow(name='registration'):
     Parameters
     ----------
 
-    ::
-
         name : name of workflow (default: 'registration')
 
     Inputs::

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -348,8 +348,6 @@ def create_reg_workflow(name='registration'):
     Parameters
     ----------
 
-    ::
-
         name : name of workflow (default: 'registration')
 
     Inputs::
@@ -366,9 +364,6 @@ def create_reg_workflow(name='registration'):
         outputspec.transformed_files : transformed files in target space
         outputspec.transformed_mean : mean image in target space
 
-    Example
-    -------
-
     """
 
     register = Workflow(name=name)