after comments from Chris

Author: djarecka

doc/devel/cmd_interface_devel.rst

@@ -39,7 +39,7 @@ above example we have used the ``desc`` metadata which holds human readable
 description of the input. The ``mandatory`` flag forces Nipype to throw an
 exception if the input was not set. ``exists`` is a special flag that works only
 for ``File traits`` and checks if the provided file exists. More details can be
-found at :ref:`interface_specs`.
+found at :doc:`interface_specs`.
 
 The input and output specifications have to be connected to the our example
 interface class:
@@ -80,7 +80,7 @@ symbols. For an input defined in InputSpec to be included into the executed
 commandline ``argstr`` has to be included. Additionally inside the main
 interface class you need to specify the name of the executable by assigning it
 to the ``_cmd`` field. Also the main interface class needs to inherit from
-:class:`CommandLine <nipype.interfaces.base.CommandLine>`:
+:class:`CommandLine <nipype.interfaces.base.core.CommandLine>`:
 
 .. testcode::
 
@@ -92,7 +92,7 @@ to the ``_cmd`` field. Also the main interface class needs to inherit from
 There is one more thing we need to take care of. When the executable finishes
 processing it will presumably create some output files. We need to know which
 files to look for, check if they exist and expose them to whatever node would
-like to use them. This is done by implementing :func:`_list_outputs <nipype.interfaces.base.BaseInterface._list_outputs>` method in the
+like to use them. This is done by implementing ``_list_outputs`` method in the
 main interface class. Basically what it does is assigning the expected output
 files to the fields of our output spec:
 

doc/devel/matlab_interface_devel.rst

@@ -24,61 +24,6 @@ By subclassing :class:`nipype.interfaces.matlab.MatlabCommand` for your main
 class, and :class:`nipype.interfaces.matlab.MatlabInputSpec` for your input and
 output spec, you gain access to some useful MATLAB hooks
 
-.. testcode::
-
-    import os
-    from nipype.interfaces.base import File, traits
-    from nipype.interfaces.matlab import MatlabCommand, MatlabInputSpec
-
-    class HelloWorldInputSpec( MatlabInputSpec):
-        name = traits.Str(mandatory = True,
-                          desc = 'Name of person to say hello to')
-
-    class HelloWorldOutputSpec(MatlabInputSpec):
-        matlab_output = traits.Str()
-
-    class HelloWorld(MatlabCommand):
-        """ Basic Hello World that displays Hello <name> in MATLAB
-
-        Returns
-        -------
-
-        matlab_output : capture of matlab output which may be
-                        parsed by user to get computation results
-
-        Examples
-        --------
-
-        >>> hello = HelloWorld()
-        >>> hello.inputs.name = 'hello_world'
-        >>> out = hello.run()
-        >>> print(out.outputs.matlab_output)
-        """
-        input_spec = HelloWorldInputSpec
-        output_spec = HelloWorldOutputSpec
-
-        def _my_script(self):
-            """This is where you implement your script"""
-            script = """
-            disp('Hello %s Python')
-            two = 1 + 1
-            """%(self.inputs.name)
-            return script
-
-        def run(self, **inputs):
-            ## inject your script
-            self.inputs.script =  self._my_script()
-            results = super(MatlabCommand, self).run( **inputs)
-            stdout = results.runtime.stdout
-            # attach stdout to outputs to access matlab results
-            results.outputs.matlab_output = stdout
-            return results
-
-        def _list_outputs(self):
-            outputs = self._outputs().get()
-            return outputs
-
-
 .. literalinclude:: matlab_example2.py
 
 .. admonition:: Example source code

doc/index.rst

@@ -18,7 +18,7 @@
         packages (e.g., ANTS_, SPM_, FSL_, FreeSurfer_, Camino_, MRtrix_, MNE_, AFNI_, 
         Slicer_), eases the design of workflows within and between packages, and
         reduces the learning curve necessary to use different packages. Nipype is
-        creating a collaborative platform for neuroimaging software development 
+        creating a collaborative platform for neuroimaging software development
         in a high-level language and addressing limitations of existing pipeline
         systems.
 

examples/fmri_ants_openfmri.py

@@ -161,10 +161,10 @@ def create_reg_workflow(name='registration'):
     """
     Compute registration between the subject's structural and MNI template
 
-        * All parameters are set using the example from: \
-          `newAntsExample.sh <https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh>`_
-        * This is currently set to perform a very quick registration. However,\
-          the registration can be made significantly more accurate for cortical\
+        * All parameters are set using the example from:
+          #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
+        * This is currently set to perform a very quick registration. However,
+          the registration can be made significantly more accurate for cortical
           structures by increasing the number of iterations.
     """
 
@@ -257,9 +257,7 @@ def create_reg_workflow(name='registration'):
 
 
 def get_aparc_aseg(files):
-    """
-    Return the aparc+aseg.mgz file
-    """
+    """Return the aparc+aseg.mgz file"""
 
     for name in files:
         if 'aparc+aseg.mgz' in name:
@@ -371,10 +369,10 @@ def create_fs_reg_workflow(name='registration'):
     """
     Compute registration between the subject's structural and MNI template
 
-        * All parameters are set using the example from: \
-          `newAntsExample.sh <https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh>`_
-        * This is currently set to perform a very quick registration. However,\
-          the registration can be made significantly more accurate for cortical\
+        * All parameters are set using the example from:
+          #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
+        * This is currently set to perform a very quick registration. However,
+          the registration can be made significantly more accurate for cortical
           structures by increasing the number of iterations.
     """
 

examples/rsfmri_vol_surface_preprocessing_nipy.py

@@ -444,10 +444,10 @@ def create_reg_workflow(name='registration'):
     """
     Compute registration between the subject's structural and MNI template
 
-        * All parameters are set using the example from: \
-          `newAntsExample.sh <https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh>`_
-        * This is currently set to perform a very quick registration. However,\
-          the registration can be made significantly more accurate for cortical\
+        * All parameters are set using the example from:
+          #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
+        * This is currently set to perform a very quick registration. However,
+          the registration can be made significantly more accurate for cortical
           structures by increasing the number of iterations.
     """