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Camino update
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  • nipype/interfaces/camino

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nipype/interfaces/camino/dti.py

Lines changed: 14 additions & 14 deletions
Original file line numberDiff line numberDiff line change
@@ -66,56 +66,56 @@ def _gen_outfilename(self):
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return name + '_DT.Bdouble'
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class DTMetricInputSpec(CommandLineInputSpec):
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eigen_data = File(exists=True, argstr='-inputfile %s', mandatory=True,
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eigen_data = File(exists=True, argstr='-inputfile %s', mandatory=True,
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desc='voxel-order data filename')
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metric = traits.Enum('fa','md','rd','l1', 'l2', 'l3', 'tr', 'ra', '2dfa','cl','cp','cs',
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argstr='-stat %s', mandatory=True,
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desc=('Specifies the metric to compute. Possible choices are: '
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'"fa", "md", "rd", "l1", "l2", "l3", "tr", "ra", "2dfa", "cl", "cp" or "cs".'))
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inputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
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inputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
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argstr='-inputdatatype %s', usedefault=True,
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desc=('Specifies the data type of the input data. '
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'The data type can be any of the following strings: '
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'"char", "short", "int", "long", "float" or "double".'
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'Default is double data type'))
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outputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
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outputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
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argstr='-outputdatatype %s', usedefault=True,
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desc=('Specifies the data type of the output data. '
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'The data type can be any of the following strings: '
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'"char", "short", "int", "long", "float" or "double".'
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'Default is double data type'))
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data_header = File(argstr='-header %s', exists=True,
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desc=('A Nifti .nii or .nii.gz file containing the header information. '
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'Usually this will be the header of the raw data file from which '
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'the diffusion tensors were reconstructed.'))
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outputfile = File(argstr='-outputfile %s', genfile=True,
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desc=('Output name. Output will be a .nii.gz file if data_header is provided and'
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'in voxel order with outputdatatype datatype (default: double) otherwise.'))
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class DTMetricOutputSpec(TraitedSpec):
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metric_stats = File(exists=True, desc='Diffusion Tensor statistics of the chosen metric')
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class DTMetric(CommandLine):
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"""
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Computes tensor metric statistics based on the eigenvalues l1 >= l2 >= l3
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typically obtained from ComputeEigensystem.
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The full list of statistics is:
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<cl> = (l1 - l2) / l1 , a measure of linearity
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<cp> = (l2 - l3) / l1 , a measure of planarity
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<cs> = l3 / l1 , a measure of isotropy
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with: cl + cp + cs = 1
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with: cl + cp + cs = 1
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<l1> = first eigenvalue
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<l2> = second eigenvalue
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<l3> = third eigenvalue
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<tr> = l1 + l2 + l3
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<md> = tr / 3
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<rd> = (l2 + l3) / 2
@@ -129,7 +129,7 @@ class DTMetric(CommandLine):
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Example
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-------
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Compute the CP planar metric as float data type.
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>>> import nipype.interfaces.camino as cam
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>>> dtmetric = cam.DTMetric()
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>>> dtmetric.inputs.eigen_data = 'dteig.Bdouble'
@@ -793,14 +793,14 @@ class ComputeEigensystemInputSpec(StdOutCommandLineInputSpec):
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maxcomponents = traits.Int(argstr='-maxcomponents %d', desc='The maximum number of tensor components in a voxel of the input data.')
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796-
inputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
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inputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
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argstr='-inputdatatype %s', usedefault=True,
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desc=('Specifies the data type of the input data. '
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'The data type can be any of the following strings: '
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'"char", "short", "int", "long", "float" or "double".'
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'Default is double data type'))
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outputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
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outputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
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argstr='-outputdatatype %s', usedefault=True,
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desc=('Specifies the data type of the output data. '
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'The data type can be any of the following strings: '

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