BF: more lgtm fixes

Author: mgxd

nipype/algorithms/misc.py

@@ -1202,7 +1202,7 @@ def normalize_tpms(in_files, in_mask=None, out_files=None):
 
     in_files = np.atleast_1d(in_files).tolist()
 
-    if not out_files:
+    if out_files is None:
         out_files = []
 
     if len(out_files) != len(in_files):

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

@@ -1118,17 +1118,13 @@ 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

@@ -700,12 +700,6 @@ 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-        'This class has been replaced by CorticalThickness and will be removed in version 0.13'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)')
@@ -924,12 +918,6 @@ 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-        'This class has been replaced by BrainExtraction and will be removed in version 0.13'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,

nipype/interfaces/base/specs.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/cmtk/cmtk.py

@@ -222,7 +222,8 @@ 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/fsl/tests/test_preprocess.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/nipy/preprocess.py

@@ -85,146 +85,7 @@ def _list_outputs(self):
         return outputs
 
 
-class FmriRealign4dInputSpec(BaseInterfaceInputSpec):
-
-    in_file = InputMultiPath(
-        File(exists=True), mandatory=True, desc="File to realign")
-    tr = traits.Float(desc="TR in seconds", mandatory=True)
-    slice_order = traits.List(
-        traits.Int(),
-        desc=('0 based slice order. This would be equivalent to entering'
-              'np.argsort(spm_slice_order) for this field. This effects'
-              'interleaved acquisition. This field will be deprecated in'
-              'future Nipy releases and be replaced by actual slice'
-              'acquisition times.'),
-        requires=["time_interp"])
-    tr_slices = traits.Float(desc="TR slices", requires=['time_interp'])
-    start = traits.Float(
-        0.0, usedefault=True, desc="time offset into TR to align slices to")
-    time_interp = traits.Enum(
-        True,
-        requires=["slice_order"],
-        desc="Assume smooth changes across time e.g.,\
-                     fmri series. If you don't want slice timing \
-                     correction set this to undefined")
-    loops = InputMultiPath(
-        [5], traits.Int, usedefault=True, desc="loops within each run")
-    between_loops = InputMultiPath(
-        [5],
-        traits.Int,
-        usedefault=True,
-        desc="loops used to \
-                                                          realign different \
-                                                          runs")
-    speedup = InputMultiPath(
-        [5],
-        traits.Int,
-        usedefault=True,
-        desc="successive image \
-                                  sub-sampling factors \
-                                  for acceleration")
-
-
-class FmriRealign4dOutputSpec(TraitedSpec):
-
-    out_file = OutputMultiPath(File(exists=True), desc="Realigned files")
-    par_file = OutputMultiPath(
-        File(exists=True), desc="Motion parameter files")
-
-
-class FmriRealign4d(BaseInterface):
-    """Simultaneous motion and slice timing correction algorithm
-
-    This interface wraps nipy's FmriRealign4d algorithm [1]_.
-
-    Examples
-    --------
-    >>> from nipype.interfaces.nipy.preprocess import FmriRealign4d
-    >>> realigner = FmriRealign4d()
-    >>> realigner.inputs.in_file = ['functional.nii']
-    >>> realigner.inputs.tr = 2
-    >>> realigner.inputs.slice_order = list(range(0,67))
-    >>> res = realigner.run() # doctest: +SKIP
-
-    References
-    ----------
-    .. [1] Roche A. A four-dimensional registration algorithm with \
-       application to joint correction of motion and slice timing \
-       in fMRI. IEEE Trans Med Imaging. 2011 Aug;30(8):1546-54. DOI_.
-
-    .. _DOI: http://dx.doi.org/10.1109/TMI.2011.2131152
-
-    """
-
-    input_spec = FmriRealign4dInputSpec
-    output_spec = FmriRealign4dOutputSpec
-    keywords = ['slice timing', 'motion correction']
-
-    def __init__(self, **inputs):
-        DeprecationWarning(('Will be deprecated in release 0.13. Please use'
-                            'SpaceTimeRealigner'))
-        BaseInterface.__init__(self, **inputs)
-
-    def _run_interface(self, runtime):
-        from nipy.algorithms.registration import FmriRealign4d as FR4d
-        all_ims = [load_image(fname) for fname in self.inputs.in_file]
-
-        if not isdefined(self.inputs.tr_slices):
-            TR_slices = None
-        else:
-            TR_slices = self.inputs.tr_slices
-
-        R = FR4d(
-            all_ims,
-            tr=self.inputs.tr,
-            slice_order=self.inputs.slice_order,
-            tr_slices=TR_slices,
-            time_interp=self.inputs.time_interp,
-            start=self.inputs.start)
-
-        R.estimate(
-            loops=list(self.inputs.loops),
-            between_loops=list(self.inputs.between_loops),
-            speedup=list(self.inputs.speedup))
-
-        corr_run = R.resample()
-        self._out_file_path = []
-        self._par_file_path = []
-
-        for j, corr in enumerate(corr_run):
-            self._out_file_path.append(
-                os.path.abspath('corr_%s.nii.gz' %
-                                (split_filename(self.inputs.in_file[j])[1])))
-            save_image(corr, self._out_file_path[j])
-
-            self._par_file_path.append(
-                os.path.abspath('%s.par' %
-                                (os.path.split(self.inputs.in_file[j])[1])))
-            mfile = open(self._par_file_path[j], 'w')
-            motion = R._transforms[j]
-            # nipy does not encode euler angles. return in original form of
-            # translation followed by rotation vector see:
-            # http://en.wikipedia.org/wiki/Rodrigues'_rotation_formula
-            for i, mo in enumerate(motion):
-                params = [
-                    '%.10f' % item
-                    for item in np.hstack((mo.translation, mo.rotation))
-                ]
-                string = ' '.join(params) + '\n'
-                mfile.write(string)
-            mfile.close()
-
-        return runtime
-
-    def _list_outputs(self):
-        outputs = self._outputs().get()
-        outputs['out_file'] = self._out_file_path
-        outputs['par_file'] = self._par_file_path
-        return outputs
-
-
 class SpaceTimeRealignerInputSpec(BaseInterfaceInputSpec):
-
     in_file = InputMultiPath(
         File(exists=True),
         mandatory=True,

nipype/pipeline/engine/base.py

@@ -109,8 +109,4 @@ def save(self, filename=None):
         savepkl(filename, self)
 
     def load(self, filename):
-        if '.npz' in filename:
-            DeprecationWarning(('npz files will be deprecated in the next '
-                                'release. you can use numpy to open them.'))
-            return np.load(filename)
         return loadpkl(filename)

nipype/pipeline/engine/utils.py

@@ -280,7 +280,7 @@ def load_resultfile(path, name):
         except UnicodeDecodeError:
             # Was this pickle created with Python 2.x?
             pickle.load(pkl_file, fix_imports=True, encoding='utf-8')
-            logger.warning('Successfully loaded pickle in compatibility mode')
+            logger.warning('Successfully loaded pkl in compatibility mode')
         except (traits.TraitError, AttributeError, ImportError,
                 EOFError) as err:
             if isinstance(err, (AttributeError, ImportError)):
@@ -439,6 +439,8 @@ def modify_paths(object, relative=True, basedir=None):
                     raise IOError('File %s not found' % out)
             else:
                 out = object
+        else:
+            raise TypeError("Object {} is undefined".format(object))
     return out
 
 

nipype/pipeline/engine/workflows.py

@@ -496,6 +496,7 @@ def export(self,
         flatgraph = self._create_flat_graph()
         nodes = nx.topological_sort(flatgraph)
 
+        all_lines = None
         lines = ['# Workflow']
         importlines = [
             'from nipype.pipeline.engine import Workflow, '

nipype/pipeline/plugins/ipython.py

@@ -39,6 +39,8 @@ def execute_task(pckld_task, node_config, updatehash):
         result = task.run(updatehash=updatehash)
     except:
         traceback = format_exc()
+        from pickle import loads
+        task = loads(pckld_task)
         result = task.result
     os.chdir(cwd)
     return result, traceback, gethostname()