Merge pull request #980 from miykael/master

HTML structure format corrections for example scripts

Author: chrisgorgo

examples/dmri_camino_dti.py

@@ -8,7 +8,7 @@
 ============
 
 This script, camino_dti_tutorial.py, demonstrates the ability to perform basic diffusion analysis
-in a Nipype pipeline.
+in a Nipype pipeline::
 
     python dmri_camino_dti.py
 

examples/dmri_connectivity.py

@@ -9,7 +9,7 @@
 
 This script, connectivity_tutorial.py, demonstrates the ability to perform connectivity mapping
 using Nipype for pipelining, Freesurfer for Reconstruction / Parcellation, Camino for tensor-fitting
-and tractography, and the Connectome Mapping Toolkit (CMTK) for connectivity analysis.
+and tractography, and the Connectome Mapping Toolkit (CMTK) for connectivity analysis::
 
     python connectivity_tutorial.py
 
@@ -26,9 +26,9 @@
 
     * http://db.tt/1vx4vLeP
 
-Along with Camino (http://web4.cs.ucl.ac.uk/research/medic/camino/pmwiki/pmwiki.php?n=Main.HomePage),
-Camino-Trackvis (http://www.nitrc.org/projects/camino-trackvis/), FSL (http://www.fmrib.ox.ac.uk/fsl/),
-and Freesurfer (http://surfer.nmr.mgh.harvard.edu/), you must also have the Connectome File Format
+Along with `Camino <http://web4.cs.ucl.ac.uk/research/medic/camino/pmwiki/pmwiki.php?n=Main.HomePage>`_,
+`Camino-Trackvis <http://www.nitrc.org/projects/camino-trackvis/>`_, `FSL <http://www.fmrib.ox.ac.uk/fsl/>`_,
+and `Freesurfer <http://surfer.nmr.mgh.harvard.edu/>`_, you must also have the Connectome File Format
 library installed as well as the Connectome Mapper.
 
 These are written by Stephan Gerhard and can be obtained from:
@@ -583,7 +583,7 @@ def select_aparc_annot(list_of_files):
     connectivity.write_graph(format='eps')
 
 """
-The output CFF file of this pipeline can be loaded in the Connectome Viewer (http://www.cmtk.org)
+The output CFF file of this pipeline can be loaded in the `Connectome Viewer <http://www.cmtk.org>`_.
 After loading the network into memory it can be examined in 3D or as a connectivity matrix
 using the default scripts produced by the Code Oracle.
 To compare networks, one must use the MergeCNetworks interface to merge two networks into

examples/dmri_connectivity_advanced.py

@@ -9,7 +9,7 @@
 
 This script, connectivity_tutorial_advanced.py, demonstrates the ability to perform connectivity mapping
 using Nipype for pipelining, Freesurfer for Reconstruction / Segmentation, MRtrix for spherical deconvolution
-and tractography, and the Connectome Mapping Toolkit (CMTK) for further parcellation and connectivity analysis.
+and tractography, and the Connectome Mapping Toolkit (CMTK) for further parcellation and connectivity analysis::
 
     python connectivity_tutorial_advanced.py
 

examples/dmri_fsl_dti.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 """
-===============
-dMRI [DTI, FSL]
-===============
+==============
+dMRI: DTI, FSL
+==============
 
 A pipeline example that uses several interfaces to perform analysis on
 diffusion weighted images using FSL FDT tools.

examples/dmri_group_connectivity_camino.py

@@ -8,7 +8,7 @@
 
 This script, dmri_group_connectivity_camino.py, runs group-based connectivity analysis using
 the dmri.camino.connectivity_mapping Nipype workflow. Further detail on the processing can be
-found in :doc:`dmri_connectivity`. This tutorial can be run using:
+found in :doc:`dmri_connectivity`. This tutorial can be run using::
 
     python dmri_group_connectivity_camino.py
 

examples/dmri_group_connectivity_mrtrix.py

@@ -8,7 +8,7 @@
 
 This script, dmri_group_connectivity_mrtrix.py, runs group-based connectivity analysis using
 the dmri.mrtrix.connectivity_mapping Nipype workflow. Further detail on the processing can be
-found in :doc:`dmri_connectivity_advanced`. This tutorial can be run using:
+found in :doc:`dmri_connectivity_advanced`. This tutorial can be run using::
 
     python dmri_group_connectivity_mrtrix.py
 

examples/dmri_mrtrix_dti.py

@@ -8,7 +8,7 @@
 ============
 
 This script, dmri_mrtrix_dti.py, demonstrates the ability to perform advanced diffusion analysis
-in a Nipype pipeline.
+in a Nipype pipeline::
 
     python dmri_mrtrix_dti.py
 

examples/dmri_tbss_nki.py

@@ -9,6 +9,7 @@
 A pipeline to do a TBSS analysis on the NKI rockland sample data
 
 """
+
 from nipype.workflows.dmri.fsl.dti import create_eddy_correct_pipeline
 from nipype.workflows.dmri.fsl.tbss import create_tbss_non_FA, create_tbss_all
 

examples/fmri_ants_openfmri.py

@@ -7,7 +7,7 @@
 =============================================
 
 A growing number of datasets are available on `OpenfMRI <http://openfmri.org>`_.
-This script demonstrates how to use nipype to analyze a data set.
+This script demonstrates how to use nipype to analyze a data set::
 
     python fmri_ants_openfmri.py --datasetdir ds107
 """
@@ -47,19 +47,16 @@ def create_reg_workflow(name='registration'):
 
     Parameters
     ----------
-
-    ::
-
         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.anatomical_image : anatomical image to coregister to
         inputspec.target_image : registration target
 
-    Outputs::
+    Outputs:
 
         outputspec.func2anat_transform : FLIRT transform
         outputspec.anat2target_transform : FLIRT+FNIRT transform
@@ -68,7 +65,6 @@ def create_reg_workflow(name='registration'):
 
     Example
     -------
-
     """
 
     register = pe.Workflow(name=name)
@@ -130,6 +126,7 @@ def create_reg_workflow(name='registration'):
     register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
     register.connect(mean2anat, 'out_matrix_file',
                      mean2anatbbr, 'in_matrix_file')
+
     """
     Convert the BBRegister transformation to ANTS ITK format
     """
@@ -197,6 +194,7 @@ def create_reg_workflow(name='registration'):
     """
     Transform the mean image. First to anatomical and then to target
     """
+
     warpmean = pe.Node(ants.ApplyTransforms(),
                        name='warpmean')
     warpmean.inputs.input_image_type = 3
@@ -238,6 +236,10 @@ def create_reg_workflow(name='registration'):
 
     return register
 
+"""
+Get info for a given subject
+"""
+
 def get_subjectinfo(subject_id, base_dir, task_id, model_id):
     """Get info for a given subject
 
@@ -291,6 +293,9 @@ def get_subjectinfo(subject_id, base_dir, task_id, model_id):
     TR = np.genfromtxt(os.path.join(base_dir, 'scan_key.txt'))[1]
     return run_ids[task_id - 1], conds[task_id - 1], TR
 
+"""
+Analyzes an open fmri dataset
+"""
 
 def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
                              task_id=None, output_dir=None, subj_prefix='*',
@@ -621,6 +626,7 @@ def get_subs(subject_id, conds, model_id, task_id):
     """
     Set processing parameters
     """
+
     preproc.inputs.inputspec.fwhm = fwhm
     gethighpass.inputs.hpcutoff = hpcutoff
     modelspec.inputs.high_pass_filter_cutoff = hpcutoff
@@ -634,6 +640,10 @@ def get_subs(subject_id, conds, model_id, task_id):
     datasink.inputs.base_directory = output_dir
     return wf
 
+"""
+The following functions run the whole workflow.
+"""
+
 if __name__ == '__main__':
     import argparse
     defstr = ' (default %(default)s)'

examples/fmri_nipy_glm.py

@@ -6,17 +6,16 @@
 fMRI: NiPy GLM, SPM
 ===================
 
-
 The fmri_nipy_glm.py integrates several interfaces to perform a first level
 analysis on a two-subject data set. It is very similar to the spm_tutorial with
 the difference of using nipy for fitting GLM model and estimating contrasts.
-The tutorial can
-be found in the examples folder. Run the tutorial from inside the
-nipype tutorial directory:
+The tutorial can be found in the examples folder. Run the tutorial from inside
+the nipype tutorial directory::
 
     python fmri_nipy_glm.py
 
 """
+
 from nipype.interfaces.nipy.model import FitGLM, EstimateContrast
 from nipype.interfaces.nipy.preprocess import ComputeMask