Merge pull request #2478 from mgxd/fix/lgtm

fix: some lgtm errors

Author: satra

doc/users/nipypecmd.rst

@@ -10,7 +10,7 @@ This is especially useful when running Interfaces wrapping code that does not ha
 command line equivalents (nipy or SPM). Being able to run Nipype interfaces opens new
 possibilities such as inclusion of SPM processing steps in bash scripts.
 
-To run Nipype Interafces you need to use the nipype_cmd tool that should already be installed.
+To run Nipype Interfaces you need to use the nipype_cmd tool that should already be installed.
 The tool allows you to list Interfaces available in a certain package:
 
 .. testcode::
@@ -24,7 +24,6 @@ The tool allows you to list Interfaces available in a certain package:
 	    ComputeMask
 	    FitGLM
 	    EstimateContrast
-	    FmriRealign4d
 
 After selecting a particular Interface you can learn what inputs it requires:
 

doc/users/plugins.rst

@@ -103,10 +103,6 @@ machinery.
 
 .. note::
 
-  We provide backward compatibility with IPython_ versions earlier than
-  0.10.1 using the IPythonX plugin. This plugin will be deprecated as of
-  version 0.13 of Nipype.
-
   Please read the IPython_ documentation to determine how to setup your cluster
   for distributed processing. This typically involves calling ipcluster.
 

nipype/algorithms/misc.py

@@ -1184,7 +1184,7 @@ def _list_outputs(self):
         return outputs
 
 
-def normalize_tpms(in_files, in_mask=None, out_files=[]):
+def normalize_tpms(in_files, in_mask=None, out_files=None):
     """
     Returns the input tissue probability maps (tpms, aka volume fractions)
     normalized to sum up 1.0 at each voxel within the mask.
@@ -1195,6 +1195,9 @@ def normalize_tpms(in_files, in_mask=None, out_files=[]):
 
     in_files = np.atleast_1d(in_files).tolist()
 
+    if out_files is None:
+        out_files = []
+
     if len(out_files) != len(in_files):
         for i, finname in enumerate(in_files):
             fname, fext = op.splitext(op.basename(finname))

nipype/info.py

@@ -120,7 +120,6 @@ def get_nipype_gitversion():
 URL = 'http://nipy.org/nipype'
 DOWNLOAD_URL = 'http://github.com/nipy/nipype/archives/master'
 LICENSE = 'Apache License, 2.0'
-CLASSIFIERS = CLASSIFIERS
 AUTHOR = 'nipype developers'
 AUTHOR_EMAIL = 'neuroimaging@python.org'
 PLATFORMS = 'OS Independent'

nipype/interfaces/ants/registration.py

@@ -1112,17 +1112,13 @@ def _get_outputfilenames(self, inverse=False):
                 output_filename = self.inputs.output_warped_image
                 if isinstance(output_filename, bool):
                     output_filename = '%s_Warped.nii.gz' % self.inputs.output_transform_prefix
-                else:
-                    output_filename = output_filename
             return output_filename
         inv_output_filename = None
         if isdefined(self.inputs.output_inverse_warped_image) and \
                 self.inputs.output_inverse_warped_image:
             inv_output_filename = self.inputs.output_inverse_warped_image
             if isinstance(inv_output_filename, bool):
                 inv_output_filename = '%s_InverseWarped.nii.gz' % self.inputs.output_transform_prefix
-            else:
-                inv_output_filename = inv_output_filename
         return inv_output_filename
 
     def _format_convergence(self, ii):

nipype/interfaces/ants/segmentation.py

@@ -143,7 +143,7 @@ def _format_arg(self, opt, spec, val):
             if isdefined(self.inputs.save_posteriors):
                 retval += ",%s" % self.inputs.output_posteriors_name_template
             return retval + "]"
-        return super(ANTSCommand, self)._format_arg(opt, spec, val)
+        return super(Atropos, self)._format_arg(opt, spec, val)
 
     def _run_interface(self, runtime, correct_return_codes=[0]):
         if self.inputs.initialization == "PriorProbabilityImages":
@@ -633,7 +633,7 @@ def _format_arg(self, opt, spec, val):
             _, _, ext = split_filename(self.inputs.segmentation_priors[0])
             retval = "-p nipype_priors/BrainSegmentationPrior%02d" + ext
             return retval
-        return super(ANTSCommand, self)._format_arg(opt, spec, val)
+        return super(CorticalThickness, self)._format_arg(opt, spec, val)
 
     def _run_interface(self, runtime, correct_return_codes=[0]):
         priors_directory = os.path.join(os.getcwd(), "nipype_priors")
@@ -693,12 +693,6 @@ def _list_outputs(self):
         return outputs
 
 
-class antsCorticalThickness(CorticalThickness):
-    DeprecationWarning(
-        'This class has been replaced by CorticalThickness and will be removed in version 0.13'
-    )
-
-
 class BrainExtractionInputSpec(ANTSCommandInputSpec):
     dimension = traits.Enum(
         3, 2, argstr='-d %d', usedefault=True, desc='image dimension (2 or 3)')
@@ -917,12 +911,6 @@ def _list_outputs(self):
         return outputs
 
 
-class antsBrainExtraction(BrainExtraction):
-    DeprecationWarning(
-        'This class has been replaced by BrainExtraction and will be removed in version 0.13'
-    )
-
-
 class JointFusionInputSpec(ANTSCommandInputSpec):
     dimension = traits.Enum(
         3,
@@ -1059,7 +1047,7 @@ def _format_arg(self, opt, spec, val):
                 assert len(val) == self.inputs.modalities * len(self.inputs.warped_label_images), \
                     "Number of intensity images and label maps must be the same {0}!={1}".format(
                     len(val), len(self.inputs.warped_label_images))
-            return super(ANTSCommand, self)._format_arg(opt, spec, val)
+            return super(JointFusion, self)._format_arg(opt, spec, val)
         return retval
 
     def _list_outputs(self):

nipype/interfaces/ants/tests/test_auto_antsBrainExtraction.py

@@ -197,6 +197,7 @@ def _clean_container(self, objekt, undefinedval=None, skipundefined=False):
             if isinstance(objekt, tuple):
                 out = tuple(out)
         else:
+            out = None
             if isdefined(objekt):
                 out = objekt
             else:
@@ -283,6 +284,7 @@ def _get_sorteddict(self,
             if isinstance(objekt, tuple):
                 out = tuple(out)
         else:
+            out = None
             if isdefined(objekt):
                 if (hash_files and isinstance(objekt, (str, bytes))
                         and os.path.isfile(objekt)):

nipype/interfaces/ants/tests/test_auto_antsCorticalThickness.py

@@ -214,7 +214,8 @@ def cmat(track_file,
         gp = nx.read_gpickle(resolution_network_file)
     elif ext == '.graphml':
         gp = nx.read_graphml(resolution_network_file)
-
+    else:
+        raise TypeError("Unable to read file:", resolution_network_file)
     nROIs = len(gp.nodes())
 
     # add node information from parcellation

nipype/interfaces/base/specs.py

@@ -131,7 +131,7 @@ def average_networks(in_files, ntwk_res_file, group_id):
                     current = ntwk.edge[edge[0]][edge[1]]
                     data = add_dicts_by_key(current, data)
                 ntwk.add_edge(edge[0], edge[1], **data)
-            nodes = list(nodes())
+            nodes = list(tmp.nodes())
             for node in nodes:
                 data = {}
                 data = ntwk.nodes[node]

nipype/interfaces/cmtk/cmtk.py

@@ -2019,7 +2019,7 @@ def _list_outputs(self):
         outputs['bvars'] = self._gen_mesh_names('bvars', structures)
         return outputs
 
-    def _gen_fname(self, name):
+    def _gen_fname(self, basename):
         path, outname, ext = split_filename(self.inputs.out_file)
 
         method = 'none'
@@ -2033,9 +2033,9 @@ def _gen_fname(self, name):
             thres = '%.4f' % self.inputs.method_as_numerical_threshold
             method = thres.replace('.', '')
 
-        if name == 'original_segmentations':
+        if basename == 'original_segmentations':
             return op.abspath('%s_all_%s_origsegs.nii.gz' % (outname, method))
-        if name == 'segmentation_file':
+        if basename == 'segmentation_file':
             return op.abspath('%s_all_%s_firstseg.nii.gz' % (outname, method))
 
         return None

nipype/interfaces/cmtk/nx.py

@@ -615,15 +615,15 @@ def test_first_genfname():
     first.inputs.out_file = 'segment.nii'
     first.inputs.output_type = "NIFTI_GZ"
 
-    value = first._gen_fname(name='original_segmentations')
+    value = first._gen_fname(basename='original_segmentations')
     expected_value = os.path.abspath('segment_all_fast_origsegs.nii.gz')
     assert value == expected_value
     first.inputs.method = 'none'
-    value = first._gen_fname(name='original_segmentations')
+    value = first._gen_fname(basename='original_segmentations')
     expected_value = os.path.abspath('segment_all_none_origsegs.nii.gz')
     assert value == expected_value
     first.inputs.method = 'auto'
     first.inputs.list_of_specific_structures = ['L_Hipp', 'R_Hipp']
-    value = first._gen_fname(name='original_segmentations')
+    value = first._gen_fname(basename='original_segmentations')
     expected_value = os.path.abspath('segment_all_none_origsegs.nii.gz')
     assert value == expected_value

nipype/interfaces/fsl/preprocess.py

@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 from .model import FitGLM, EstimateContrast
-from .preprocess import ComputeMask, FmriRealign4d, SpaceTimeRealigner
+from .preprocess import ComputeMask, SpaceTimeRealigner
 from .utils import Similarity