suggestions from chris

Author: djarecka

nipype/algorithms/confounds.py

@@ -390,8 +390,6 @@ class CompCorInputSpec(BaseInterfaceInputSpec):
         desc='Detrend time series prior to component '
         'extraction')
     use_regress_poly = traits.Bool(
-        True,
-        usedefault=True,
         deprecated='0.15.0',
         new_name='pre_filter',
         desc=('use polynomial regression '

nipype/algorithms/tests/test_auto_ACompCor.py

@@ -32,7 +32,6 @@ def test_ACompCor_inputs():
         use_regress_poly=dict(
             deprecated='0.15.0',
             new_name='pre_filter',
-            usedefault=True,
         ),
     )
     inputs = ACompCor.input_spec()

nipype/algorithms/tests/test_auto_TCompCor.py

@@ -33,7 +33,6 @@ def test_TCompCor_inputs():
         use_regress_poly=dict(
             deprecated='0.15.0',
             new_name='pre_filter',
-            usedefault=True,
         ),
     )
     inputs = TCompCor.input_spec()

nipype/interfaces/afni/tests/test_auto_NwarpApply.py

@@ -5,10 +5,7 @@
 
 def test_NwarpApply_inputs():
     input_map = dict(
-        ainterp=dict(
-            argstr='-ainterp %s',
-            usedefault=True,
-        ),
+        ainterp=dict(argstr='-ainterp %s', ),
         args=dict(argstr='%s', ),
         environ=dict(
             nohash=True,

nipype/interfaces/afni/tests/test_auto_OneDToolPy.py

@@ -34,7 +34,6 @@ def test_OneDToolPy_inputs():
         show_cormat_warnings=dict(
             argstr='-show_cormat_warnings |& tee %s',
             position=-1,
-            usedefault=False,
             xor=['out_file'],
         ),
         show_indices_interest=dict(argstr='-show_indices_interest', ),

nipype/interfaces/afni/utils.py

@@ -1625,7 +1625,6 @@ class NwarpApplyInputSpec(CommandLineInputSpec):
         argstr='-interp %s',
         usedefault=True)
     ainterp = traits.Enum(
-        'wsinc5',
         'NN',
         'nearestneighbour',
         'nearestneighbor',
@@ -1635,10 +1634,10 @@ class NwarpApplyInputSpec(CommandLineInputSpec):
         'tricubic',
         'quintic',
         'triquintic',
+        'wsinc5',
         desc='specify a different interpolation method than might '
         'be used for the warp',
-        argstr='-ainterp %s',
-        usedefault=True)
+        argstr='-ainterp %s')
     out_file = File(
         name_template='%s_Nwarp',
         desc='output image file name',
@@ -1671,7 +1670,7 @@ class NwarpApply(AFNICommandBase):
     >>> nwarp.inputs.master = 'NWARP'
     >>> nwarp.inputs.warp = "'Fred_WARP+tlrc Fred.Xaff12.1D'"
     >>> nwarp.cmdline
-    "3dNwarpApply -ainterp wsinc5 -source Fred+orig -interp wsinc5 -master NWARP -prefix Fred+orig_Nwarp -nwarp \'Fred_WARP+tlrc Fred.Xaff12.1D\'"
+    "3dNwarpApply -source Fred+orig -interp wsinc5 -master NWARP -prefix Fred+orig_Nwarp -nwarp \'Fred_WARP+tlrc Fred.Xaff12.1D\'"
     >>> res = nwarp.run()  # doctest: +SKIP
 
     """
@@ -1842,7 +1841,6 @@ class OneDToolPyInputSpec(AFNIPythonCommandInputSpec):
     show_cormat_warnings = traits.File(
         desc='Write cormat warnings to a file',
         argstr="-show_cormat_warnings |& tee %s",
-        usedefault=False,
         position=-1,
         xor=['out_file'])
     show_indices_interest = traits.Bool(

nipype/interfaces/fsl/model.py

@@ -56,7 +56,7 @@ class Level1DesignInputSpec(BaseInterfaceInputSpec):
         desc=("which regressors to make orthogonal e.g., "
               "{1: {0:0,1:0,2:0}, 2: {0:1,1:1,2:0}} to make the second "
               "regressor in a 2-regressor model orthogonal to the first."),
-        value={}, usedefault=True)
+        usedefault=True)
     model_serial_correlations = traits.Bool(
         desc="Option to model serial correlations using an \
 autoregressive estimator (order 1). Setting this option is only \

nipype/interfaces/mrtrix3/tests/test_auto_ComputeTDI.py

@@ -8,10 +8,7 @@ def test_ComputeTDI_inputs():
         args=dict(argstr='%s', ),
         contrast=dict(argstr='-constrast %s', ),
         data_type=dict(argstr='-datatype %s', ),
-        dixel=dict(
-            argstr='-dixel %s',
-            usedefault=True,
-        ),
+        dixel=dict(argstr='-dixel %s', ),
         ends_only=dict(argstr='-ends_only', ),
         environ=dict(
             nohash=True,

nipype/interfaces/mrtrix3/utils.py

@@ -182,7 +182,7 @@ class TensorMetricsInputSpec(CommandLineInputSpec):
     out_eval = File(
         argstr='-value %s', desc='output selected eigenvalue(s) file')
     component = traits.List(
-        [1, 2, 3],
+        [1],
         usedefault=True,
         argstr='-num %s',
         sep=',',
@@ -222,7 +222,7 @@ class TensorMetrics(CommandLine):
     >>> comp.inputs.in_file = 'dti.mif'
     >>> comp.inputs.out_fa = 'fa.mif'
     >>> comp.cmdline                               # doctest: +ELLIPSIS
-    'tensor2metric -num 1,2,3 -fa fa.mif dti.mif'
+    'tensor2metric -num 1 -fa fa.mif dti.mif'
     >>> comp.run()                                 # doctest: +SKIP
     """
 
@@ -267,7 +267,6 @@ class ComputeTDIInputSpec(CommandLineInputSpec):
         desc='specify output image data type')
     use_dec = traits.Bool(argstr='-dec', desc='perform mapping in DEC space')
     dixel = File(
-        'dixels.txt', usedefault=True,
         argstr='-dixel %s',
         desc='map streamlines to'
         'dixels within each voxel. Directions are stored as'
@@ -416,7 +415,7 @@ class ComputeTDI(MRTrix3Base):
     >>> tdi = mrt.ComputeTDI()
     >>> tdi.inputs.in_file = 'dti.mif'
     >>> tdi.cmdline                               # doctest: +ELLIPSIS
-    'tckmap -dixel dixels.txt dti.mif tdi.mif'
+    'tckmap dti.mif tdi.mif'
     >>> tdi.run()                                 # doctest: +SKIP
     """
 

nipype/interfaces/niftyseg/em.py

@@ -62,7 +62,7 @@ class EMInputSpec(CommandLineInputSpec):
         argstr='-min_iter %s',
         default_value=0,
         usedefault=True,
-        desc='Minimun number of iterations')
+        desc='Minimum number of iterations')
 
     # other options
     bc_order_val = traits.Int(