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Erik Ziegler
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HTML cleanup
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‎examples/dmri_connectivity_advanced.py

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@@ -74,7 +74,7 @@
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This needs to point to the freesurfer subjects directory (Recon-all must have been run on subj1 from the FSL course data)
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Alternatively, the reconstructed subject data can be downloaded from:
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http://dl.dropbox.com/u/315714/subj1.zip
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* http://dl.dropbox.com/u/315714/subj1.zip
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"""
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@@ -85,7 +85,7 @@
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"""
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This needs to point to the fdt folder you can find after extracting
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http://www.fmrib.ox.ac.uk/fslcourse/fsl_course_data2.tar.gz
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* http://www.fmrib.ox.ac.uk/fslcourse/fsl_course_data2.tar.gz
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"""
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‎examples/dmri_group_connectivity_camino.py

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@@ -40,8 +40,8 @@
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Output data can be visualized in ConnectomeViewer, TrackVis,
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and anything that can view Nifti files.
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ConnectomeViewer: https://github.com/LTS5/connectomeviewer
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TrackVis: http://trackvis.org/
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* ConnectomeViewer: https://github.com/LTS5/connectomeviewer
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* TrackVis: http://trackvis.org/
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The fiber data is available in Numpy arrays, and the connectivity matrix
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is also produced as a MATLAB matrix.
@@ -107,10 +107,10 @@
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"""
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110-
.. warning::
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.. warning::
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The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dwi'.
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The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
112+
The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dwi'.
113+
The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
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"""
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@@ -119,20 +119,20 @@
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bvals=[['subject_id', 'bvals']])
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"""
122-
This line creates the processing workflow given the information input about the groups and subjects.
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This line creates the processing workflow given the information input about the groups and subjects.
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.. seealso::
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.. seealso::
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* nipype/workflows/dmri/mrtrix/group_connectivity.py
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* nipype/workflows/dmri/camino/connectivity_mapping.py
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* :ref:`dmri_connectivity`
126+
* nipype/workflows/dmri/mrtrix/group_connectivity.py
127+
* nipype/workflows/dmri/camino/connectivity_mapping.py
128+
* :ref:`dmri_connectivity`
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"""
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132132
l1pipeline = create_group_connectivity_pipeline(group_list, group_id, data_dir, subjects_dir, output_dir, info)
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134134
"""
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Define the parcellation scheme to use.
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Define the parcellation scheme to use.
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"""
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parcellation_scheme = 'NativeFreesurfer'
@@ -141,21 +141,21 @@
141141
l1pipeline.inputs.connectivity.inputnode.resolution_network_file = cmp_config._get_lausanne_parcellation(parcellation_scheme)['freesurferaparc']['node_information_graphml']
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143143
"""
144-
The first level pipeline we have tweaked here is run within the for loop.
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The first level pipeline we have tweaked here is run within the for loop.
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"""
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l1pipeline.run()
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l1pipeline.write_graph(format='eps', graph2use='flat')
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150150
"""
151-
Next we create and run the second-level pipeline. The purpose of this workflow is simple:
152-
It is used to merge each subject's CFF file into one, so that there is a single file containing
153-
all of the networks for each group. This can be useful for performing Network Brain Statistics
154-
using the NBS plugin in ConnectomeViewer.
151+
Next we create and run the second-level pipeline. The purpose of this workflow is simple:
152+
It is used to merge each subject's CFF file into one, so that there is a single file containing
153+
all of the networks for each group. This can be useful for performing Network Brain Statistics
154+
using the NBS plugin in ConnectomeViewer.
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.. seealso::
156+
.. seealso::
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http://www.connectomeviewer.org/documentation/users/tutorials/tut_nbs.html
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http://www.connectomeviewer.org/documentation/users/tutorials/tut_nbs.html
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"""
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‎examples/dmri_group_connectivity_mrtrix.py

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@@ -105,9 +105,9 @@
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"""
107107
108-
.. warning::
109-
The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dwi'.
110-
The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
108+
.. warning::
109+
The 'info' dictionary below is used to define the input files. In this case, the diffusion weighted image contains the string 'dwi'.
110+
The same applies to the b-values and b-vector files, and this must be changed to fit your naming scheme.
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"""
@@ -116,23 +116,23 @@
116116
bvals=[['subject_id', 'bvals']])
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118118
"""
119-
This line creates the processing workflow given the information input about the groups and subjects.
119+
This line creates the processing workflow given the information input about the groups and subjects.
120120
121-
.. seealso::
121+
.. seealso::
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* nipype/workflows/dmri/mrtrix/group_connectivity.py
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* nipype/workflows/dmri/mrtrix/connectivity_mapping.py
125-
* :ref:`dmri_connectivity_advanced
123+
* nipype/workflows/dmri/mrtrix/group_connectivity.py
124+
* nipype/workflows/dmri/mrtrix/connectivity_mapping.py
125+
* :ref:`dmri_connectivity_advanced
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"""
128128

129129
l1pipeline = create_group_connectivity_pipeline(group_list, group_id, data_dir, subjects_dir, output_dir, info)
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131131
"""
132-
This is used to demonstrate the ease through which different parameters can be set for each group.
133-
These values relate to the absolute threshold used on the fractional anisotropy map. This is done
134-
in order to identify single-fiber voxels. In brains with more damage, however, it may be necessary
135-
to reduce the threshold, since their brains are have lower average fractional anisotropy values.
132+
This is used to demonstrate the ease through which different parameters can be set for each group.
133+
These values relate to the absolute threshold used on the fractional anisotropy map. This is done
134+
in order to identify single-fiber voxels. In brains with more damage, however, it may be necessary
135+
to reduce the threshold, since their brains are have lower average fractional anisotropy values.
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"""
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if group_id == 'parkinsons':
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141141
l1pipeline.inputs.connectivity.mapping.threshold_FA.absolute_threshold_value = 0.7
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"""
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These lines relate to inverting the b-vectors in the encoding file, and setting the
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maximum harmonic order of the pre-tractography spherical deconvolution step. This is
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done to show how to set inputs that will affect both groups.
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These lines relate to inverting the b-vectors in the encoding file, and setting the
145+
maximum harmonic order of the pre-tractography spherical deconvolution step. This is
146+
done to show how to set inputs that will affect both groups.
147147
"""
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149149
l1pipeline.inputs.connectivity.mapping.fsl2mrtrix.invert_y = True
150150
l1pipeline.inputs.connectivity.mapping.csdeconv.maximum_harmonic_order = 6
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"""
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Define the parcellation scheme to use.
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Define the parcellation scheme to use.
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"""
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parcellation_name = 'scale500'
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160160
l1pipeline.inputs.connectivity.mapping.inputnode_within.resolution_network_file = cmp_config._get_lausanne_parcellation('Lausanne2008')[parcellation_name]['node_information_graphml']
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162162
"""
163-
Set the maximum number of tracks to obtain
163+
Set the maximum number of tracks to obtain
164164
"""
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166166
l1pipeline.inputs.connectivity.mapping.probCSDstreamtrack.desired_number_of_tracks = 100000
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168168
"""
169-
The first level pipeline we have tweaked here is run within the for loop.
169+
The first level pipeline we have tweaked here is run within the for loop.
170170
"""
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172172
l1pipeline.run()
173173
l1pipeline.write_graph(format='eps', graph2use='flat')
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175175
"""
176-
Next we create and run the second-level pipeline. The purpose of this workflow is simple:
177-
It is used to merge each subject's CFF file into one, so that there is a single file containing
178-
all of the networks for each group. This can be useful for performing Network Brain Statistics
179-
using the NBS plugin in ConnectomeViewer.
176+
Next we create and run the second-level pipeline. The purpose of this workflow is simple:
177+
It is used to merge each subject's CFF file into one, so that there is a single file containing
178+
all of the networks for each group. This can be useful for performing Network Brain Statistics
179+
using the NBS plugin in ConnectomeViewer.
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.. seealso::
181+
.. seealso::
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http://www.connectomeviewer.org/documentation/users/tutorials/tut_nbs.html
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http://www.connectomeviewer.org/documentation/users/tutorials/tut_nbs.html
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"""
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‎examples/dmri_mrtrix_dti.py

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@@ -7,14 +7,14 @@
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Introduction
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============
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10-
This script, mrtrix_dti_tutorial.py, demonstrates the ability to perform advanced diffusion analysis
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This script, dmri_mrtrix_dti.py, demonstrates the ability to perform advanced diffusion analysis
1111
in a Nipype pipeline.
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python dmri_mrtrix_dti.py
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We perform this analysis using the FSL course data, which can be acquired from here:
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17-
http://www.fmrib.ox.ac.uk/fslcourse/fsl_course_data2.tar.gz
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* http://www.fmrib.ox.ac.uk/fslcourse/fsl_course_data2.tar.gz
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Import necessary modules from nipype.
2020
"""
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3030
"""
3131
This needs to point to the fdt folder you can find after extracting
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http://www.fmrib.ox.ac.uk/fslcourse/fsl_course_data2.tar.gz
33+
* http://www.fmrib.ox.ac.uk/fslcourse/fsl_course_data2.tar.gz
3434
3535
"""
3636

@@ -70,18 +70,18 @@
7070
inputnode = pe.Node(interface=util.IdentityInterface(fields=["dwi", "bvecs", "bvals"]), name="inputnode")
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7272
"""
73-
Diffusion processing nodes
74-
--------------------------
73+
Diffusion processing nodes
74+
--------------------------
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.. seealso::
76+
.. seealso::
7777
78-
dmri_connectivity_advanced.py
79-
Tutorial with further detail on using MRtrix tractography for connectivity analysis
78+
dmri_connectivity_advanced.py
79+
Tutorial with further detail on using MRtrix tractography for connectivity analysis
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http://www.brain.org.au/software/mrtrix/index.html
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MRtrix's online documentation
81+
http://www.brain.org.au/software/mrtrix/index.html
82+
MRtrix's online documentation
8383
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b-values and b-vectors stored in FSL's format are converted into a single encoding file for MRTrix.
84+
b-values and b-vectors stored in FSL's format are converted into a single encoding file for MRTrix.
8585
"""
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8787
fsl2mrtrix = pe.Node(interface=mrtrix.FSL2MRTrix(),name='fsl2mrtrix')
@@ -136,12 +136,12 @@
136136
threshold_wmmask.inputs.absolute_threshold_value = 0.4
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"""
139-
The spherical deconvolution step depends on the estimate of the response function
140-
in the highly anisotropic voxels we obtained above.
139+
The spherical deconvolution step depends on the estimate of the response function
140+
in the highly anisotropic voxels we obtained above.
141141
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.. warning::
142+
.. warning::
143143
144-
For damaged or pathological brains one should take care to lower the maximum harmonic order of these steps.
144+
For damaged or pathological brains one should take care to lower the maximum harmonic order of these steps.
145145
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"""
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@@ -242,7 +242,7 @@
242242
"""
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244244
dwiproc = pe.Workflow(name="dwiproc")
245-
dwiproc.base_dir = os.path.abspath('mrtrix_dti_tutorial')
245+
dwiproc.base_dir = os.path.abspath('dmri_mrtrix_dti')
246246
dwiproc.connect([
247247
(infosource,datasource,[('subject_id', 'subject_id')]),
248248
(datasource,tractography,[('dwi','inputnode.dwi'),

‎nipype/workflows/dmri/camino/diffusion.py

Lines changed: 1 addition & 1 deletion
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@@ -8,7 +8,7 @@
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def create_camino_dti_pipeline(name="dtiproc"):
1010
"""Creates a pipeline that does the same diffusion processing as in the
11-
camino_dti_tutorial example script. Given a diffusion-weighted image,
11+
:ref:`dmri_camino_dti` example script. Given a diffusion-weighted image,
1212
b-values, and b-vectors, the workflow will return the tractography
1313
computed from diffusion tensors and from PICo probabilistic tractography.
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‎nipype/workflows/dmri/mrtrix/diffusion.py

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def create_mrtrix_dti_pipeline(name="dtiproc", tractography_type = 'probabilistic'):
77
"""Creates a pipeline that does the same diffusion processing as in the
8-
:ref:`example_mrtrix_dti` example script. Given a diffusion-weighted image,
8+
:ref:`dmri_mrtrix_dti` example script. Given a diffusion-weighted image,
99
b-values, and b-vectors, the workflow will return the tractography
1010
computed from spherical deconvolution and probabilistic streamline tractography
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