Merge pull request #680 from satra/enh/metakeys

MRG: to automate checking of metadata and to write automatic tests for interface change checks

Author: satra

nipype/algorithms/misc.py

@@ -42,7 +42,7 @@ class PickAtlasInputSpec(BaseInterfaceInputSpec):
         traits.Int, traits.List(traits.Int),
         desc=("Labels of regions that will be included in the mask. Must be\
         compatible with the atlas used."),
-        compulsory=True
+        mandatory=True
     )
     hemi = traits.Enum(
         'both', 'left', 'right',
@@ -614,7 +614,7 @@ class CreateNiftiInputSpec(BaseInterfaceInputSpec):
     data_file = File(exists=True, mandatory=True, desc="ANALYZE img file")
     header_file = File(
         exists=True, mandatory=True, desc="corresponding ANALYZE hdr file")
-    affine = traits.Array(exists=True, desc="affine transformation array")
+    affine = traits.Array(desc="affine transformation array")
 
 
 class CreateNiftiOutputSpec(TraitedSpec):
@@ -653,7 +653,7 @@ def _list_outputs(self):
 class TSNRInputSpec(BaseInterfaceInputSpec):
     in_file = InputMultiPath(File(exists=True), mandatory=True,
                              desc='realigned 4D file or a list of 3D files')
-    regress_poly = traits.Int(min=1, desc='Remove polynomials')
+    regress_poly = traits.Range(low=1, desc='Remove polynomials')
 
 
 class TSNROutputSpec(TraitedSpec):

nipype/algorithms/modelgen.py

@@ -173,16 +173,16 @@ class SpecifyModelInputSpec(BaseInterfaceInputSpec):
                 'corresponding to onsets, durations and amplitudes'))
     realignment_parameters = InputMultiPath(File(exists=True),
        desc = "Realignment parameters returned by motion correction algorithm",
-                                         filecopy=False)
+                                         copyfile=False)
     outlier_files = InputMultiPath(File(exists=True),
          desc="Files containing scan outlier indices that should be tossed",
-                                filecopy=False)
+                                copyfile=False)
     functional_runs = InputMultiPath(traits.Either(traits.List(File(exists=True)),
                                                    File(exists=True)),
                                      mandatory=True,
             desc="Data files for model. List of 4D files or list of" \
                                       "list of 3D files per session",
-            filecopy=False)
+            copyfile=False)
     input_units = traits.Enum('secs', 'scans', mandatory=True,
              desc = "Units of event onsets and durations (secs or scans)" \
                     "Output units are always in secs")

nipype/interfaces/afni/preprocess.py

@@ -26,7 +26,8 @@
 
 class To3DInputSpec(AFNICommandInputSpec):
     out_file = File("%s", desc='output image file name',
-                    argstr='-prefix %s', name_source=["in_folder", "infolder"], usedefault=True)
+                    argstr='-prefix %s', name_source=["in_folder", "infolder"],
+                    usedefault=True)
     in_xor = ["infolder", "in_folder"]
     in_folder = Directory(desc='folder with DICOM images to convert',
                           argstr='%s/*.dcm',
@@ -776,7 +777,7 @@ class BandpassInputSpec(AFNICommandInputSpec):
                    ++ You would only want to do this if the dataset
                       had been detrended already in some other program.""")
     tr = traits.Float(
-        arsgtr="-dt %f",
+        argstr="-dt %f",
         desc="set time step (TR) in sec [default=from dataset header]")
     nfft = traits.Int(
         argstr='-nfft %d',
@@ -1113,7 +1114,8 @@ class MaskaveInputSpec(AFNICommandInputSpec):
                    exists=True)
     out_file = File("%s_maskave.1D", desc='output image file name',
                     keep_extension=True,
-                    argstr="> %s", name_source="in_file", usedefault=True, position=-1)
+                    argstr="> %s", name_source="in_file", usedefault=True,
+                    position=-1)
     mask = File(desc='matrix to align input file',
                 argstr='-mask %s',
                 position=1,
@@ -1685,6 +1687,7 @@ class TCorrMap(AFNICommand):
     _cmd = '3dTcorrMap'
     input_spec = TCorrMapInputSpec
     output_spec = TCorrMapOutputSpec
+    _additional_metadata = ['suffix']
 
     def _format_arg(self, name, trait_spec, value):
         if name in self.inputs._thresh_opts:

nipype/interfaces/ants/resampling.py

@@ -224,7 +224,7 @@ class ApplyTransformsInputSpec(ANTSCommandInputSpec):
                        exists=True)
     output_image = traits.Str(argstr='--output %s',
                               desc=('output file name'), genfile=True,
-                              hash_file=False)
+                              hash_files=False)
     out_postfix = traits.Str("_trans", usedefault=True,
                              desc=('Postfix that is appended to all output '
                                    'files (default = _trans)'))

nipype/interfaces/ants/segmentation.py

@@ -17,9 +17,12 @@
 
 
 class AtroposInputSpec(ANTSCommandInputSpec):
-    dimension = traits.Enum(3, 2, 4, argstr='--image-dimensionality %d', usedefault=True,
+    dimension = traits.Enum(3, 2, 4, argstr='--image-dimensionality %d',
+                            usedefault=True,
                             desc='image dimension (2, 3, or 4)')
-    intensity_images = InputMultiPath(File(exists=True), argstr="--intensity-image %s...", madatory=True)
+    intensity_images = InputMultiPath(File(exists=True),
+                                      argstr="--intensity-image %s...",
+                                      mandatory=True)
     mask_image = File(exists=True, argstr='--mask-image %s', mandatory=True)
     initialization = traits.Enum('Random', 'Otsu', 'KMeans',
                                  'PriorProbabilityImages', 'PriorLabelImage',
@@ -40,9 +43,10 @@ class AtroposInputSpec(ANTSCommandInputSpec):
     posterior_formulation = traits.Str(argstr="%s")
     use_mixture_model_proportions = traits.Bool(requires=['posterior_formulation'])
     out_classified_image_name = File(argstr="%s", genfile=True,
-                                     hash_file=False)
+                                     hash_files=False)
     save_posteriors = traits.Bool()
-    output_posteriors_name_template = traits.Str('POSTERIOR_%02d.nii.gz', usedefault=True)
+    output_posteriors_name_template = traits.Str('POSTERIOR_%02d.nii.gz',
+                                                 usedefault=True)
 
 
 class AtroposOutputSpec(TraitedSpec):
@@ -157,19 +161,24 @@ def _list_outputs(self):
         return outputs
 
 class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
-    dimension = traits.Enum(3, 2, argstr='--image-dimension %d', usedefault=True,
+    dimension = traits.Enum(3, 2, argstr='--image-dimension %d',
+                            usedefault=True,
                             desc='image dimension (2 or 3)')
     input_image = File(argstr='--input-image %s', mandatory=True,
                         desc=('image to apply transformation to (generally a '
                               'coregistered functional)'))
     mask_image = File(argstr='--mask-image %s')
     output_image = traits.Str(argstr='--output %s',
                              desc=('output file name'), genfile=True,
-                             hash_file=False)
+                             hash_files=False)
     bspline_fitting_distance = traits.Float(argstr="--bsline-fitting [%g]")
     shrink_factor = traits.Int(argstr="--shrink-factor %d")
-    n_iterations = traits.List(traits.Int(), argstr="--convergence [ %s", sep="x", requires=['convergence_threshold'], position=1)
-    convergence_threshold = traits.Float(argstr=",%g]", requires=['n_iterations'], position=2)
+    n_iterations = traits.List(traits.Int(), argstr="--convergence [ %s",
+                               sep="x", requires=['convergence_threshold'],
+                               position=1)
+    convergence_threshold = traits.Float(argstr=",%g]",
+                                         requires=['n_iterations'],
+                                         position=2)
 
 
 class N4BiasFieldCorrectionOutputSpec(TraitedSpec):

nipype/interfaces/base.py

@@ -699,6 +699,7 @@ class BaseInterface(Interface):
     """
     input_spec = BaseInterfaceInputSpec
     _version = None
+    _additional_metadata = []
 
     def __init__(self, **inputs):
         if not self.input_spec:

nipype/interfaces/camino/dti.py

@@ -411,9 +411,13 @@ def __init__(self, command=None, **inputs):
         return super(TrackPICo, self).__init__(command, **inputs)
 
 class TrackBayesDiracInputSpec(TrackInputSpec):
-    scheme_file = File(argstr='-schemefile %s', mandatory=True, exist=True, desc='The scheme file corresponding to the data being processed.')
+    scheme_file = File(argstr='-schemefile %s', mandatory=True, exists=True,
+                       desc=('The scheme file corresponding to the data being '
+                             'processed.'))
 
-    iterations = traits.Int(argstr='-iterations %d', units='NA', desc="Number of streamlines to generate at each seed point. The default is 5000.")
+    iterations = traits.Int(argstr='-iterations %d', units='NA',
+                            desc=("Number of streamlines to generate at each "
+                                  "seed point. The default is 5000."))
 
     pdf = traits.Enum('bingham', 'watson', 'acg', argstr='-pdf %s', desc='Specifies the model for PICo priors (not the curvature priors). The default is "bingham".')
 
@@ -469,13 +473,13 @@ def __init__(self, command=None, **inputs):
         return super(TrackBallStick, self).__init__(command, **inputs)
 
 class TrackBootstrapInputSpec(TrackInputSpec):
-    scheme_file = File(argstr='-schemefile %s', mandatory=True, exist=True, desc='The scheme file corresponding to the data being processed.')
+    scheme_file = File(argstr='-schemefile %s', mandatory=True, exists=True, desc='The scheme file corresponding to the data being processed.')
 
     iterations = traits.Int(argstr='-iterations %d', units='NA', desc="Number of streamlines to generate at each seed point.")
 
     inversion = traits.Int(argstr='-inversion %s', desc = 'Tensor reconstruction algorithm for repetition bootstrapping. Default is 1 (linear reconstruction, single tensor).')
 
-    bsdatafiles = traits.List(File, mandatory=True, exists=True, argstr='-bsdatafile %s', desc='Specifies files containing raw data for repetition bootstrapping. Use -inputfile for wild bootstrap data.')
+    bsdatafiles = traits.List(File(exists=True), mandatory=True, argstr='-bsdatafile %s', desc='Specifies files containing raw data for repetition bootstrapping. Use -inputfile for wild bootstrap data.')
 
     bgmask = File(argstr='-bgmask %s', exists=True, desc = 'Provides the name of a file containing a background mask computed using, for example, FSL\'s bet2 program. The mask file contains zero in background voxels and non-zero in foreground.')
 

nipype/interfaces/cmtk/convert.py

@@ -204,7 +204,7 @@ def _list_outputs(self):
         return outputs
 
 class MergeCNetworksInputSpec(BaseInterfaceInputSpec):
-    in_files = InputMultiPath(File, exists=True, mandatory=True, desc='List of CFF files to extract networks from')
+    in_files = InputMultiPath(File(exists=True), mandatory=True, desc='List of CFF files to extract networks from')
     out_file = File('merged_network_connectome.cff', usedefault = True, desc='Output CFF file with all the networks added')
 
 class MergeCNetworksOutputSpec(TraitedSpec):

nipype/interfaces/dcmstack.py

@@ -85,7 +85,7 @@ class DcmStackInputSpec(NiftiGeneratorBaseInputSpec):
                                   "exclude filters")
 
 class DcmStackOutputSpec(TraitedSpec):
-    out_file = traits.File(exists=True)
+    out_file = File(exists=True)
 
 class DcmStack(NiftiGeneratorBase):
     '''Create one Nifti file from a set of DICOM files. Can optionally embed
@@ -240,19 +240,16 @@ def _list_outputs(self):
         return outputs
 
 class CopyMetaInputSpec(TraitedSpec):
-    src_file = traits.File(mandatory=True,
-                         exists=True,
-                        )
-    dest_file = traits.File(mandatory=True,
-                            exists=True)
+    src_file = File(mandatory=True, exists=True)
+    dest_file = File(mandatory=True, exists=True)
     include_classes = traits.List(desc="List of specific meta data "
                                   "classifications to include. If not "
                                   "specified include everything.")
     exclude_classes = traits.List(desc="List of meta data "
                                   "classifications to exclude")
 
 class CopyMetaOutputSpec(TraitedSpec):
-    dest_file = traits.File(exists=True)
+    dest_file = File(exists=True)
 
 class CopyMeta(BaseInterface):
     '''Copy meta data from one Nifti file to another. Useful for preserving
@@ -308,8 +305,7 @@ class MergeNiftiInputSpec(NiftiGeneratorBaseInputSpec):
                            "non-existant dimension is used.")
 
 class MergeNiftiOutputSpec(TraitedSpec):
-    out_file = traits.File(exists=True,
-                           desc="Merged Nifti file")
+    out_file = File(exists=True, desc="Merged Nifti file")
 
 def make_key_func(meta_keys, index=None):
     def key_func(src_nii):
@@ -352,14 +348,12 @@ def _list_outputs(self):
         return outputs
 
 class SplitNiftiInputSpec(NiftiGeneratorBaseInputSpec):
-    in_file = traits.File(exists=True,
-                          mandatory=True,
-                          desc="Nifti file to split")
+    in_file = File(exists=True, mandatory=True, desc="Nifti file to split")
     split_dim = traits.Int(desc="Dimension to split along. If not "
                            "specified, the last dimension is used.")
 
 class SplitNiftiOutputSpec(TraitedSpec):
-    out_list = traits.List(exists=True,
+    out_list = traits.List(File(exists=True),
                            desc="Split Nifti files")
 
 class SplitNifti(NiftiGeneratorBase):

nipype/interfaces/diffusion_toolkit/odf.py

@@ -22,7 +22,7 @@ class HARDIMatInputSpec(CommandLineInputSpec):
                 argstr='%s', position=1, mandatory=True)
     bvals = File(exists=True,desc = 'b values file', mandatory=True)
     out_file = File("recon_mat.dat", desc = 'output matrix file', argstr='%s', usedefault=True, position=2)
-    order = traits.Int(argsstr='-order %s', desc="""maximum order of spherical harmonics. must be even number. default
+    order = traits.Int(argstr='-order %s', desc="""maximum order of spherical harmonics. must be even number. default
         is 4""")
     odf_file = File(exists=True, argstr='-odf %s', desc="""filename that contains the reconstruction points on a HEMI-sphere.
         use the pre-set 181 points by default""")