Apply suggestions from code review

Co-Authored-By: Chris Markiewicz <effigies@gmail.com>

Author: oesteban

examples/fmri_spm_auditory.py

@@ -148,7 +148,7 @@ def get_vox_dims(volume):
 l1analysis = pe.Workflow(name='analysis')
 
 """Generate SPM-specific design information using
-:ref:`nipype.algorithms.modelgen.SpecifyModel`.
+:ref:`nipype.algorithms.modelgen.SpecifySPMModel`.
 """
 
 modelspec = pe.Node(interface=model.SpecifySPMModel(), name="modelspec")
@@ -273,7 +273,7 @@ def makelist(item):
 -----------------------------------------
 Here we create a structure that provides information
 about the experimental paradigm. This is used by the
-:ref:`nipype.algorithms.modelgen.SpecifyModel`
+:ref:`nipype.algorithms.modelgen.SpecifySPMModel`
 to create the information necessary to generate an SPM design matrix.
 """
 
@@ -327,7 +327,7 @@ def makelist(item):
 pipeline. Thus for this pipeline there will be subject specific
 sub-directories.
 
-The :func:`nipype.pipeline.engine.Workflow.connect` function creates the
+The :func:`~nipype.pipeline.engine.workflows.Workflow.connect` method creates the
 links between the processes, i.e., how data should flow in and out of
 the processing nodes.
 """
@@ -391,8 +391,8 @@ def getstripdir(subject_id):
 The code discussed above sets up all the necessary data structures
 with appropriate parameters and the connectivity between the
 processes, but does not generate any output. To actually run the
-analysis on the data the :func:`nipype.pipeline.engine.workflows.Workflow.run`
-function needs to be called.
+analysis on the data the :func:`~nipype.pipeline.engine.workflows.Workflow.run`
+method needs to be called.
 """
 
 if __name__ == '__main__':

nipype/algorithms/confounds.py

@@ -654,10 +654,10 @@ def _run_interface(self, runtime):
         else:
             components_criterion = 6
             IFLOGGER.warning(
-                "``num_components`` and ``variance_threshold`` are "
+                "`num_components` and `variance_threshold` are "
                 "not defined. Setting number of components to 6 "
                 "for backward compatibility. Please set either "
-                "``num_components`` or ``variance_threshold``, as "
+                "`num_components` or `variance_threshold`, as "
                 "this feature may be deprecated in the future."
             )
 

nipype/interfaces/afni/preprocess.py

@@ -3145,7 +3145,7 @@ class TSmoothInputSpec(AFNICommandInputSpec):
         argstr="-osf",
     )
     lin3 = traits.Int(
-        desc=r"3 point linear filter: :math:`0.5\,(1-m)\,a + m\,b + 0.5\,(1-m)\,c"
+        desc=r"3 point linear filter: :math:`0.5\,(1-m)\,a + m\,b + 0.5\,(1-m)\,c`"
         "Here, 'm' is a number strictly between 0 and 1.",
         argstr="-3lin %d",
     )

nipype/interfaces/base/core.py

@@ -145,7 +145,7 @@ class BaseInterface(Interface):
     ----------
     input_spec: :obj:`nipype.interfaces.base.specs.TraitedSpec`
         points to the traited class for the inputs
-    output_spec: HasTraits
+    output_spec: :obj:`nipype.interfaces.base.specs.TraitedSpec`
         points to the traited class for the outputs
     _redirect_x: bool
         should be set to ``True`` when the interface requires

nipype/interfaces/cmtk/cmtk.py

@@ -145,9 +145,9 @@ def create_endpoints_array(fib, voxelSize):
     Returns
     -------
     endpoints : ndarray of size [#fibers, 2, 3]
-      containing for each fiber the ndex of its first and last point in the voxelSize volume
+      containing for each fiber the index of its first and last point in the voxelSize volume
     endpointsmm : ndarray of size [#fibers, 2, 3]
-      endpoints in milimeter coordinates
+      endpoints in millimeter coordinates
 
     """
     # Init

nipype/interfaces/nilearn.py

@@ -1,7 +1,7 @@
 # -*- coding: utf-8 -*-
 # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
 # vi: set ft=python sts=4 ts=4 sw=4 et:
-"""Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data."""
+"""Nilearn is a Python library for fast and easy statistical learning on NeuroImaging data."""
 import os
 
 import numpy as np