More doc fixes.

Author: chrisgorgo

doc/users/install.rst

@@ -9,11 +9,11 @@ This page covers the necessary steps to install Nipype.
 Download
 --------
 
-Release 0.7.0: [`zip <https://github.com/nipy/nipype/archive/0.7.zip>`_ `tar.gz
-<https://github.com/nipy/nipype/archive/0.7.tar.gz>`_]
+Release 0.7.0: [`zip <https://github.com/nipy/nipype/archive/0.7.zip>`__ `tar.gz
+<https://github.com/nipy/nipype/archive/0.7.tar.gz>`__]
 
-Development: [`zip <http://github.com/nipy/nipype/zipball/master>`_ `tar
-<http://github.com/nipy/nipype/tarball/master>`_]
+Development: [`zip <http://github.com/nipy/nipype/zipball/master>`__ `tar.gz 
+<http://github.com/nipy/nipype/tarball/master>`__]
 
 `Prior downloads <http://github.com/nipy/nipype/tags>`_
 

examples/dmri_connectivity_advanced.py

@@ -139,16 +139,13 @@
 """
 Creating the workflow's nodes
 =============================
-"""
 
-"""
 Conversion nodes
 ----------------
-"""
 
-"""
 A number of conversion operations are required to obtain NIFTI files from the FreesurferSource for each subject.
 Nodes are used to convert the following:
+
     * Original structural image to NIFTI
     * Pial, white, inflated, and spherical surfaces for both the left and right hemispheres are converted to GIFTI for visualization in ConnectomeViewer
     * Parcellated annotation files for the left and right hemispheres are also converted to GIFTI
@@ -200,6 +197,7 @@
 
 """
 Tensors are fitted to each voxel in the diffusion-weighted image and from these three maps are created:
+
     * Major eigenvector in each voxel
     * Apparent diffusion coefficient
     * Fractional anisotropy
@@ -282,9 +280,7 @@
 """
 Structural segmentation nodes
 -----------------------------
-"""
 
-"""
 The following node identifies the transformation between the diffusion-weighted
 image and the structural image. This transformation is then applied to the tracts
 so that they are in the same space as the regions of interest.
@@ -355,10 +351,7 @@
 Connecting the workflow
 =======================
 Here we connect our processing pipeline.
-"""
 
-
-"""
 Connecting the inputs, FreeSurfer nodes, and conversions
 --------------------------------------------------------
 """
@@ -436,7 +429,6 @@
 mapping.connect([(tensor2fa, MRconvert_fa,[("FA","in_file")])])
 
 """
-
 This block creates the rough brain mask to be multiplied, mulitplies it with the
 fractional anisotropy image, and thresholds it to get the single-fiber voxels.
 """

examples/smri_ants_build_template.py

@@ -68,12 +68,14 @@
 """
 3. Define the workflow and its working directory
 """
+
 tbuilder=pe.Workflow(name="ANTSTemplateBuilder")
 tbuilder.base_dir=requestedPath
 
 """
 4. Define data sources. In real life these would be replace by DataGrabbers
 """
+
 datasource = pe.Node(interface=util.IdentityInterface(fields=
                     ['imageList', 'passiveImagesDictionariesList']),
                     run_without_submitting=True,
@@ -84,6 +86,7 @@
 """
 5. Template is initialized by a simple average
 """
+
 initAvg = pe.Node(interface=ants.AverageImages(), name ='initAvg')
 initAvg.inputs.dimension = 3
 initAvg.inputs.normalize = True

examples/tessellation_tutorial.py

@@ -1,8 +1,8 @@
 #!/usr/bin/env python
 """
-=============================================
+=================================================
 sMRI: Regional Tessellation and Surface Smoothing
-=============================================
+=================================================
 
 Introduction
 ============
@@ -26,8 +26,7 @@
 
 .. warning::
 
-	This workflow will take several hours to finish entirely, since smoothing
-    the larger cortical surfaces is very time consuming.
+	This workflow will take several hours to finish entirely, since smoothing the larger cortical surfaces is very time consuming.
 
 Packages and Data Setup
 =======================

tools/interfacedocgen.py

@@ -302,7 +302,7 @@ def generate_api_doc(self, uri):
             ad += '\n.. _%s\n\n' % label
             ad += '\n.. index:: %s\n\n' % c
             ad += c + '\n' + self.rst_section_levels[2] * len(c) + '\n\n'
-            ad += "`Link to code <%s>`_\n\n" % get_file_url(classinst)
+            ad += "`Link to code <%s>`__\n\n" % get_file_url(classinst)
             ad += trim(classinst.help(returnhelp=True),
                        self.rst_section_levels[3]) + '\n'
 
@@ -313,7 +313,7 @@ def generate_api_doc(self, uri):
             label = ':func:`' + name + '`'
             ad += '\n.. _%s:\n\n' % (uri + '.' + name)
             ad += '\n'.join((label, self.rst_section_levels[2] * len(label)))
-            ad += "\n\n`Link to code <%s>`_\n\n" % get_file_url(finst)
+            ad += "\n\n`Link to code <%s>`__\n\n" % get_file_url(finst)
             helpstr = trim(finst.__doc__, self.rst_section_levels[3])
             ad += '\n\n' + helpstr + '\n\n'
 
@@ -332,7 +332,7 @@ def generate_api_doc(self, uri):
             label = ':func:`' + name + '`'
             ad += '\n.. _%s:\n\n' % (uri + '.' + name)
             ad += '\n'.join((label, self.rst_section_levels[2] * len(label)))
-            ad += "\n\n`Link to code <%s>`_\n\n" % get_file_url(finst)
+            ad += "\n\n`Link to code <%s>`__\n\n" % get_file_url(finst)
             helpstr = trim(finst.__doc__, self.rst_section_levels[3])
             ad += '\n\n' + helpstr + '\n\n'