Merge pull request #940 from satra/fix/misc

Fix/misc

Author: satra

doc/conf.py

@@ -39,7 +39,7 @@
               'numpy_ext.numpydoc',
               'matplotlib.sphinxext.plot_directive',
               'matplotlib.sphinxext.only_directives',
-              'matplotlib.sphinxext.ipython_directive',
+              'IPython.sphinxext.ipython_directive',
               'ipython_console_highlighting'
               ]
 

nipype/algorithms/misc.py

@@ -1164,8 +1164,8 @@ def normalize_tpms(in_files, in_mask=None, out_files=[]):
 
     return out_files
 
+# Deprecated interfaces ------------------------------------------------------
 
-# Deprecated interfaces ---------------------------------------------------------
 class Distance(nam.Distance):
     """Calculates distance between two volumes.
 

nipype/algorithms/tests/test_auto_Overlap.py

@@ -1935,7 +1935,7 @@ def _overload_extension(self, value):
 
     def _gen_filename(self, name):
         return os.path.abspath(super(AFNItoNIFTI, self)._gen_filename(name))
-        
+
 class EvalInputSpec(AFNICommandInputSpec):
     in_file_a = File(desc='input file to 1deval',
                      argstr='-a %s', position=0, mandatory=True, exists=True)
@@ -1955,15 +1955,15 @@ class EvalInputSpec(AFNICommandInputSpec):
                           requires=['start_idx'])
     single_idx = traits.Int(desc='volume index for in_file_a')
     other = File(desc='other options', argstr='')
-    
+
 class Eval(AFNICommand):
     """Evaluates an expression that may include columns of data from one or more text files
 
     see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/1deval.html>
-    
+
     Examples
     ========
-    
+
     >>> from nipype.interfaces import afni as afni
     >>> eval = afni.Eval()
     >>> eval.inputs.in_file_a = 'seed.1D'
@@ -1996,7 +1996,7 @@ def _parse_inputs(self, skip=None):
         """
         return super(Eval, self)._parse_inputs(
             skip=('start_idx', 'stop_idx', 'out1D', 'other'))
-            
+
 class MeansInputSpec(AFNICommandInputSpec):
     in_file_a = File(desc='input file to 3dMean',
         argstr='%s',

nipype/interfaces/afni/preprocess.py

@@ -24,28 +24,28 @@
 
 warn = warnings.warn
 warnings.filterwarnings('always', category=UserWarning)
-    
+
 class SVMTrainInputSpec(AFNICommandInputSpec):
     #training options
-    ttype = traits.Str(desc='tname: classification or regression', 
+    ttype = traits.Str(desc='tname: classification or regression',
                 argstr='-type %s',
                 mandatory=True)
     in_file = File(desc='A 3D+t AFNI brik dataset to be used for training.',
                 argstr='-trainvol %s',
                 mandatory=True,
                 exists=True,
                 copyfile=False)
-    out_file = File(name_template="%s_vectors", 
+    out_file = File(name_template="%s_vectors",
                 desc='output sum of weighted linear support vectors file name',
                 argstr='-bucket %s',
-                suffix='_bucket', 
+                suffix='_bucket',
                 name_source="in_file")
     model = File(name_template="%s_model",
                 desc='basename for the brik containing the SVM model',
                 argstr='-model %s',
-                suffix='_model', 
-                name_source="in_file")    
-    alphas = File(name_template="%s_alphas", 
+                suffix='_model',
+                name_source="in_file")
+    alphas = File(name_template="%s_alphas",
                 desc='output alphas file name',
                 argstr='-alpha %s',
                 suffix='_alphas',
@@ -62,20 +62,20 @@ class SVMTrainInputSpec(AFNICommandInputSpec):
                 exists=True)
     censor = File(desc='.1D censor file that allows the user to ignore certain samples in the training data.',
                 argstr='-censor %s',
-                exists=True)   
+                exists=True)
     kernel = traits.Str(desc='string specifying type of kernel function:linear, polynomial, rbf, sigmoid',
                 argstr='-kernel %s')
     max_iterations = traits.Int(desc='Specify the maximum number of iterations for the optimization.',
                 argstr='-max_iterations %d')
     w_out = traits.Bool(desc='output sum of weighted linear support vectors',
                 argstr='-wout')
     options = traits.Str(desc='additional options for SVM-light', argstr='%s')
-                
+
 class SVMTrainOutputSpec(TraitedSpec):
     out_file = File(desc='sum of weighted linear support vectors file name')
     model = File(desc='brik containing the SVM model file name')
     alphas = File(desc='output alphas file name')
-                
+
 class SVMTrain(AFNICommand):
     """Temporally predictive modeling with the support vector machine
     SVM Train Only
@@ -109,7 +109,6 @@ class SVMTestInputSpec(AFNICommandInputSpec):
     #testing options
     model = traits.Str(desc='modname is the basename for the brik containing the SVM model',
                 argstr='-model %s',
-                exists=True,
                 mandatory=True)
     in_file = File(desc='A 3D or 3D+t AFNI brik dataset to be used for testing.',
                 argstr='-testvol %s',
@@ -130,7 +129,7 @@ class SVMTestInputSpec(AFNICommandInputSpec):
     multiclass = traits.Bool(desc='Specifies multiclass algorithm for classification',
                 argstr='-multiclass %s')
     options = traits.Str(desc='additional options for SVM-light', argstr='%s')
-    
+
 class SVMTest(AFNICommand):
     """Temporally predictive modeling with the support vector machine
     SVM Test Only

nipype/interfaces/afni/svm.py

@@ -17,7 +17,6 @@ def test_SVMTest_inputs():
     mandatory=True,
     ),
     model=dict(argstr='-model %s',
-    exists=True,
     mandatory=True,
     ),
     multiclass=dict(argstr='-multiclass %s',

nipype/interfaces/afni/tests/test_auto_SVMTest.py

@@ -0,0 +1,97 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.interfaces.ants.segmentation import antsCorticalThickness
+
+def test_antsCorticalThickness_inputs():
+    input_map = dict(anatomical_image=dict(argstr='-a %s',
+    mandatory=True,
+    ),
+    args=dict(argstr='%s',
+    ),
+    b_spline_smoothing=dict(argstr='-v',
+    ),
+    brain_probability_mask=dict(argstr='-m %s',
+    copyfile=False,
+    mandatory=True,
+    ),
+    brain_template=dict(argstr='-e %s',
+    mandatory=True,
+    ),
+    cortical_label_image=dict(),
+    debug=dict(argstr='-z 1',
+    ),
+    dimension=dict(argstr='-d %d',
+    usedefault=True,
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    extraction_registration_mask=dict(argstr='-f %s',
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    image_suffix=dict(argstr='-s %s',
+    usedefault=True,
+    ),
+    keep_temporary_files=dict(argstr='-k %d',
+    ),
+    label_propagation=dict(argstr='-l %s',
+    ),
+    max_iterations=dict(argstr='-i %d',
+    ),
+    num_threads=dict(nohash=True,
+    usedefault=True,
+    ),
+    out_prefix=dict(argstr='-o %s',
+    usedefault=True,
+    ),
+    posterior_formulation=dict(argstr='-b %s',
+    ),
+    prior_segmentation_weight=dict(argstr='-w %f',
+    ),
+    quick_registration=dict(argstr='-q 1',
+    ),
+    segmentation_iterations=dict(argstr='-n %d',
+    ),
+    segmentation_priors=dict(argstr='-p %s',
+    mandatory=True,
+    ),
+    t1_registration_template=dict(argstr='-t %s',
+    mandatory=True,
+    ),
+    terminal_output=dict(mandatory=True,
+    nohash=True,
+    ),
+    use_floatingpoint_precision=dict(argstr='-j %d',
+    ),
+    use_random_seeding=dict(argstr='-u %d',
+    ),
+    )
+    inputs = antsCorticalThickness.input_spec()
+
+    for key, metadata in input_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+def test_antsCorticalThickness_outputs():
+    output_map = dict(BrainExtractionMask=dict(),
+    BrainSegmentation=dict(),
+    BrainSegmentationN4=dict(),
+    BrainSegmentationPosteriorsCSF=dict(),
+    BrainSegmentationPosteriorsDGM=dict(),
+    BrainSegmentationPosteriorsGM=dict(),
+    BrainSegmentationPosteriorsWM=dict(),
+    CorticalThickness=dict(),
+    SubjectToTemplate0GenericAffine=dict(),
+    SubjectToTemplate1Warp=dict(),
+    TemplateToSubject0Warp=dict(),
+    TemplateToSubject1GenericAffine=dict(),
+    TemplateToSubjectLogJacobian=dict(),
+    )
+    outputs = antsCorticalThickness.output_spec()
+
+    for key, metadata in output_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value
+

nipype/interfaces/ants/tests/test_auto_antsCorticalThickness.py

@@ -37,7 +37,7 @@ class ConmatInputSpec(CommandLineInputSpec):
                              desc=("Tract statistic to use. See TractStats for other options."),
                              requires=['scalar_file'],xor=['tract_prop'])
 
-    tract_prop = traits.Enum("length", "endpointsep", argstr='-tractstat %s', 
+    tract_prop = traits.Enum("length", "endpointsep", argstr='-tractstat %s',
                              units='NA', xor=['tract_stat'],
                              desc=('Tract property average to compute in the connectivity matrix. '
                                    'See TractStats for details.'))

nipype/interfaces/camino/connectivity.py

@@ -85,7 +85,7 @@ class DcmStackInputSpec(NiftiGeneratorBaseInputSpec):
                                   "any default exclude filters")
     include_regexes = traits.List(desc="Meta data to include, overriding any "
                                   "exclude filters")
-    force_read = traits.Bool(True, usedefault=True, 
+    force_read = traits.Bool(True, usedefault=True,
                              desc=('Force reading files without DICM marker'))
 
 

nipype/interfaces/dcmstack.py

@@ -127,7 +127,7 @@ class Denoise(BaseInterface):
     """
     input_spec = DenoiseInputSpec
     output_spec = DenoiseOutputSpec
-    
+
     def _run_interface(self, runtime):
         out_file = op.abspath(self._gen_outfilename())