More HTML fixes

Erik Ziegler · Erik Ziegler · commit 65a4faaf0672 · 2012-03-06T13:06:57.000+01:00
diff --git a/examples/dmri_group_connectivity_camino.py b/examples/dmri_group_connectivity_camino.py
@@ -55,7 +55,7 @@
 
 import nipype.interfaces.fsl as fsl
 import nipype.interfaces.freesurfer as fs    # freesurfer
-import  os.path as op                      # system functions
+import os.path as op                      # system functions
 import cmp
 from nipype.workflows.dmri.camino.group_connectivity import create_group_connectivity_pipeline
 from nipype.workflows.dmri.connectivity.group_connectivity import (create_merge_networks_by_group_workflow, 
@@ -103,8 +103,8 @@
 
 .. warning::
 
-The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dwi'.
-The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
+    The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dwi'.
+    The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
 
 """
 
@@ -120,9 +120,9 @@
 """
 
 """
-Next we create and run the second-level pipeline. The purpose of this workflow is simple:
-It is used to merge each subject's CFF file into one, so that there is a single file containing
-all of the networks for each group. This can be useful for performing Network Brain Statistics
+The purpose of the second-level workflow is simple: It is used to merge each 
+subject's CFF file into one, so that there is a single file containing all of the
+networks for each group. This can be useful for performing Network Brain Statistics
 using the NBS plugin in ConnectomeViewer.
 
 .. seealso::
@@ -137,11 +137,14 @@
     title += group_id
     if not idx == len(group_list.keys()) - 1:
         title += '-'
+        
     info = dict(dwi=[['subject_id', 'dti']],
                 bvecs=[['subject_id', 'bvecs']],
                 bvals=[['subject_id', 'bvals']])
+                
     l1pipeline = create_group_connectivity_pipeline(group_list, group_id, data_dir, subjects_dir, output_dir, info)
 
+    # Here we define the parcellation scheme and the number of tracks to produce
     parcellation_scheme = 'NativeFreesurfer'
     cmp_config = cmp.configuration.PipelineConfiguration()
     cmp_config.parcellation_scheme = parcellation_scheme
@@ -150,6 +153,7 @@
     l1pipeline.run()
     l1pipeline.write_graph(format='eps', graph2use='flat')
 
+    # The second-level pipeline is created here
     l2pipeline = create_merge_networks_by_group_workflow(group_list, group_id, data_dir, subjects_dir, output_dir)
     l2pipeline.run()
     l2pipeline.write_graph(format='eps', graph2use='flat')
diff --git a/examples/dmri_group_connectivity_mrtrix.py b/examples/dmri_group_connectivity_mrtrix.py
@@ -100,31 +100,31 @@
 """
 
 .. warning::
+
     The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dwi'.
     The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
 
-
 """
 
 """
-This line creates the processing workflow given the information input about the groups and subjects.
+The workflow is created given the information input about the groups and subjects.
 
 .. seealso::
 
     * nipype/workflows/dmri/mrtrix/group_connectivity.py
     * nipype/workflows/dmri/mrtrix/connectivity_mapping.py
-    * :ref:`dmri_connectivity_advanced
+    * :ref:`dmri_connectivity_advanced`
 
 """
 
 """
-These values relate to the absolute threshold used on the fractional anisotropy map. This is done
+We set values for absolute threshold used on the fractional anisotropy map. This is done
 in order to identify single-fiber voxels. In brains with more damage, however, it may be necessary
 to reduce the threshold, since their brains are have lower average fractional anisotropy values.
 """
 
 """
-These lines relate to inverting the b-vectors in the encoding file, and setting the
+We invert the b-vectors in the encoding file, and set the
 maximum harmonic order of the pre-tractography spherical deconvolution step. This is
 done to show how to set inputs that will affect both groups.
 """
@@ -159,9 +159,12 @@
     else:
         l1pipeline.inputs.connectivity.mapping.threshold_FA.absolute_threshold_value = 0.7
 
+    # Here with invert the b-vectors in the Y direction and set the maximum harmonic order of the
+    # spherical deconvolution step
     l1pipeline.inputs.connectivity.mapping.fsl2mrtrix.invert_y = True
     l1pipeline.inputs.connectivity.mapping.csdeconv.maximum_harmonic_order = 6
     
+    # Here we define the parcellation scheme and the number of tracks to produce
     parcellation_name = 'scale500'
     l1pipeline.inputs.connectivity.mapping.Parcellate.parcellation_name = parcellation_name
     cmp_config = cmp.configuration.PipelineConfiguration()
@@ -172,6 +175,7 @@
     l1pipeline.run()
     l1pipeline.write_graph(format='eps', graph2use='flat')
 
+    # The second-level pipeline is created here
     l2pipeline = create_merge_network_results_by_group_workflow(group_list, group_id, data_dir, subjects_dir, output_dir)
     l2pipeline.inputs.l2inputnode.network_file = cmp_config._get_lausanne_parcellation('Lausanne2008')[parcellation_name]['node_information_graphml']
     l2pipeline.run()
