More fixed links.

Author: chrisgorgo

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 :ref:`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
 
@@ -109,7 +109,7 @@
 
     * nipype/workflows/dmri/mrtrix/group_connectivity.py
     * nipype/workflows/dmri/camino/connectivity_mapping.py
-    * :ref:`dmri_connectivity`
+    * :doc:`dmri_connectivity`
 
 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

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 :ref:`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
 
@@ -107,7 +107,7 @@
 
     * nipype/workflows/dmri/mrtrix/group_connectivity.py
     * nipype/workflows/dmri/mrtrix/connectivity_mapping.py
-    * :ref:`dmri_connectivity_advanced`
+    * :doc:`dmri_connectivity_advanced`
 
 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

nipype/algorithms/rapidart.py

@@ -565,8 +565,8 @@ class StimulusCorrelation(BaseInterface):
 
     Currently this class supports an SPM generated design matrix and requires
     intensity parameters. This implies that one must run
-    :ref:`nipype.algorithms.rapidart.ArtifactDetect`
-    and :ref:`nipype.interfaces.spm.model.Level1Design` prior to running this or
+    :ref:`ArtifactDetect <nipype.algorithms.rapidart.ArtifactDetect>`
+    and :ref:`Level1Design <nipype.interfaces.spm.model.Level1Design>` prior to running this or
     provide an SPM.mat file and intensity parameters through some other means.
 
     Examples

nipype/workflows/dmri/camino/diffusion.py

@@ -8,7 +8,7 @@
 
 def create_camino_dti_pipeline(name="dtiproc"):
     """Creates a pipeline that does the same diffusion processing as in the
-    :ref:`dmri_camino_dti` example script. Given a diffusion-weighted image,
+    :doc:`../../users/examples/dmri_camino_dti` example script. Given a diffusion-weighted image,
     b-values, and b-vectors, the workflow will return the tractography
     computed from diffusion tensors and from PICo probabilistic tractography.
 

nipype/workflows/dmri/mrtrix/diffusion.py

@@ -5,7 +5,7 @@
 
 def create_mrtrix_dti_pipeline(name="dtiproc", tractography_type = 'probabilistic'):
     """Creates a pipeline that does the same diffusion processing as in the
-    :ref:`dmri_mrtrix_dti` example script. Given a diffusion-weighted image,
+    :doc:`../../users/examples/dmri_mrtrix_dti` example script. Given a diffusion-weighted image,
     b-values, and b-vectors, the workflow will return the tractography
     computed from spherical deconvolution and probabilistic streamline tractography