Apply suggestions from code review

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

Author: oesteban

doc/interfaces.rst

@@ -20,7 +20,7 @@ Nipype provides some *in-house* interfaces to help with workflow
 management tasks, basic image manipulations, and filesystem/storage
 interfaces:
 
-    * `"Algorithms" <api/generated/nipype.algorithms.html>`__
+    * `Algorithms <api/generated/nipype.algorithms.html>`__
     * `Image manipulation <api/generated/nipype.interfaces.image.html>`__
     * `I/O Operations <api/generated/nipype.interfaces.io.html>`__
     * `Self-reporting interfaces <api/generated/nipype.interfaces.mixins.html>`__
@@ -127,4 +127,4 @@ Index of Interfaces
     :maxdepth: 3
 
     api/generated/nipype.algorithms
-    api/generated/nipype.interfaces
+    api/generated/nipype.interfaces

examples/fmri_spm_auditory.py

@@ -326,7 +326,7 @@ def makelist(item):
 pipeline. Thus for this pipeline there will be subject specific
 sub-directories.
 
-The :func:`nipype.pipeline.engine.Pipeline.connect` function creates the
+The :func:`nipype.pipeline.engine.Workflow.connect` function creates the
 links between the processes, i.e., how data should flow in and out of
 the processing nodes.
 """

nipype/algorithms/modelgen.py

@@ -54,6 +54,7 @@ def spm_hrf(RT, P=None, fMRI_T=16):
     python implementation of spm_hrf
 
     See ``spm_hrf`` for implementation details::
+
       % RT   - scan repeat time
       % p    - parameters of the response function (two gamma
       % functions)

nipype/interfaces/base/core.py

@@ -143,7 +143,7 @@ class BaseInterface(Interface):
 
     Attributes
     ----------
-    input_spec: HasTraits
+    input_spec: :obj:`nipype.interfaces.base.spec.TraitedSpec`
         points to the traited class for the inputs
     output_spec: HasTraits
         points to the traited class for the outputs
@@ -326,7 +326,7 @@ def run(self, cwd=None, ignore_exception=None, **inputs):
 
         Returns
         -------
-        results :  :obj:`InterfaceResult`
+        results :  :obj:`nipype.interfaces.base.core.InterfaceResult`
             A copy of the instance that was executed, provenance information and,
             if successful, results
 

nipype/interfaces/base/support.py

@@ -33,9 +33,9 @@ def __str__(self):
 
 class Bunch(object):
     """
-    Dictionary-like class that provides attribute-style access to it's items.
+    Dictionary-like class that provides attribute-style access to its items.
 
-    A ``Bunch`` is a simple container that stores it's items as class
+    A ``Bunch`` is a simple container that stores its items as class
     attributes [1]_. Internally all items are stored in a dictionary and
     the class exposes several of the dictionary methods.
 

nipype/interfaces/cmtk/cmtk.py

@@ -137,16 +137,16 @@ def create_endpoints_array(fib, voxelSize):
 
     Parameters
     ----------
-    fib :
+    fib : array-like
       the fibers data
-    voxelSize:
+    voxelSize : tuple
       3-tuple containing the voxel size of the ROI image
 
     Returns
     -------
-    endpoints : matrix of size [#fibers, 2, 3]
+    endpoints : ndarray of size [#fibers, 2, 3]
       containing for each fiber the ndex of its first and last point in the voxelSize volume
-    endpointsmm : matrix of size [#fibers, 2, 3]
+    endpointsmm : ndarray of size [#fibers, 2, 3]
       endpoints in milimeter coordinates
 
     """

nipype/interfaces/cmtk/parcellation.py

@@ -665,18 +665,18 @@ def extract(Z, shape, position, fill):
 
     Parameters
     ----------
-    Z :
+    Z : array-like
         the original data
-    shape :
+    shape : tuple
         tuple containing neighbourhood dimensions
     position :
         tuple containing central point indexes
-    fill :
+    fill : float
        value for the padding of Z
 
     Returns
     -------
-    R :
+    R : ndarray
         the neighbourhood of the specified point in Z
 
     """

nipype/interfaces/diffusion_toolkit/dti.py

@@ -190,8 +190,8 @@ class DTITrackerInputSpec(CommandLineInputSpec):
         desc="""\
 Tracking algorithm.
 
-* fact -> use FACT method for tracking. this is the default method.
-* rk2  -> use 2nd order runge-kutta method for tracking.
+* fact -> use FACT method for tracking. This is the default method.
+* rk2  -> use 2nd order Runge-Kutta method for tracking.
 * tl   -> use tensorline method for tracking.
 * sl   -> use interpolated streamline method with fixed step-length
 

nipype/interfaces/diffusion_toolkit/odf.py

@@ -264,7 +264,7 @@ class ODFTrackerInputSpec(CommandLineInputSpec):
     runge_kutta2 = traits.Bool(
         argstr="-rk2",
         desc="""\
-use 2nd order runge-kutta method for tracking.
+use 2nd order Runge-Kutta method for tracking.
 default tracking method is non-interpolate streamline""",
     )
     step_length = traits.Float(