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fixed auto tests and style
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README.rst

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@@ -78,8 +78,8 @@ Support and Communication
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-------------------------
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If you have a problem or would like to ask a question about how to do something in Nipype please open an issue to
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`NeuroStars.org <http://neurostars.org>`_ with a *nipype* tag. `NeuroStars.org <http://neurostars.org>`_ is a
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platform similar to StackOverflow but dedicated to neuroinformatics.
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`NeuroStars.org <http://neurostars.org>`_ with a *nipype* tag. `NeuroStars.org <http://neurostars.org>`_ is a
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platform similar to StackOverflow but dedicated to neuroinformatics.
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To participate in the Nipype development related discussions please use the following mailing list::
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nipype/algorithms/tests/test_auto_CompCor.py

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nipype/algorithms/tests/test_auto_ErrorMap.py

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nipype/algorithms/tests/test_auto_Overlap.py

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nipype/algorithms/tests/test_auto_TSNR.py

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nipype/interfaces/ants/tests/test_auto_CreateJacobianDeterminantImage.py

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@@ -11,7 +11,6 @@ def test_CreateJacobianDeterminantImage_inputs():
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position=1,
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),
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doLogJacobian=dict(argstr='%d',
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mandatory=False,
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position=3,
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),
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environ=dict(nohash=True,
@@ -35,7 +34,6 @@ def test_CreateJacobianDeterminantImage_inputs():
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terminal_output=dict(nohash=True,
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),
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useGeometric=dict(argstr='%d',
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mandatory=False,
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position=4,
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),
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)

nipype/interfaces/ants/utils.py

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@@ -145,9 +145,9 @@ class CreateJacobianDeterminantImageInputSpec(ANTSCommandInputSpec):
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outputImage = File(argstr='%s', mandatory=True,
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position=2,
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desc='output filename')
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doLogJacobian = traits.Enum(0, 1, argstr='%d', mandatory=False, position=3,
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doLogJacobian = traits.Enum(0, 1, argstr='%d', position=3,
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desc='return the log jacobian')
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useGeometric = traits.Enum(0, 1, argstr='%d', mandatory=False, position=4,
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useGeometric = traits.Enum(0, 1, argstr='%d', position=4,
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desc='return the geometric jacobian')
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class CreateJacobianDeterminantImageOutputSpec(TraitedSpec):

nipype/interfaces/fsl/model.py

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@@ -49,7 +49,6 @@ class Level1DesignInputSpec(BaseInterfaceInputSpec):
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"{'dgamma': {'derivs': True}}"))
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orthogonalization = traits.Dict(traits.Int, traits.Dict(traits.Int,
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traits.Either(traits.Bool,traits.Int)),
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mandatory=False,
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desc=("which regressors to make orthogonal e.g., "
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"{1: {0:0,1:0,2:0}, 2: {0:1,1:1,2:0}} to make the second "
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"regressor in a 2-regressor model orthogonal to the first."),

nipype/interfaces/fsl/preprocess.py

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@@ -751,7 +751,7 @@ class FNIRTInputSpec(FSLCommandInputSpec):
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inwarp_file = File(exists=True, argstr='--inwarp=%s',
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desc='name of file containing initial non-linear warps')
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in_intensitymap_file = traits.List(File(exists=True), argstr='--intin=%s',
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copyfiles=False, minlen=1, maxlen=2,
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copyfile=False, minlen=1, maxlen=2,
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desc=('name of file/files containing '
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'initial intensity mapping '
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'usually generated by previous '

nipype/interfaces/fsl/tests/test_auto_ApplyXfm.py

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# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
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from __future__ import unicode_literals
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from ..preprocess import ApplyXFM
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from ..preprocess import ApplyXfm
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def test_ApplyXFM_inputs():
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def test_ApplyXfm_inputs():
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input_map = dict(angle_rep=dict(argstr='-anglerep %s',
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),
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apply_isoxfm=dict(argstr='-applyisoxfm %f',
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min_ver='5.0.0',
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),
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)
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inputs = ApplyXFM.input_spec()
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inputs = ApplyXfm.input_spec()
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for key, metadata in list(input_map.items()):
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for metakey, value in list(metadata.items()):
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assert getattr(inputs.traits()[key], metakey) == value
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def test_ApplyXFM_outputs():
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def test_ApplyXfm_outputs():
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output_map = dict(out_file=dict(),
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out_log=dict(),
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out_matrix_file=dict(),
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)
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outputs = ApplyXFM.output_spec()
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outputs = ApplyXfm.output_spec()
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for key, metadata in list(output_map.items()):
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for metakey, value in list(metadata.items()):

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