make check changes

Author: satra

examples/fmri_ants_openfmri.py

@@ -175,7 +175,7 @@ def create_reg_workflow(name='registration'):
     """
     Create a mask of the median image coregistered to the anatomical image
     """
-    
+
     mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
     register.connect(mean2anatbbr, 'out_file', mean2anat_mask, 'in_file')
 

nipype/interfaces/dipy/tensors.py

@@ -32,7 +32,7 @@
 def tensor_fitting(data, bvals, bvecs, mask_file=None):
     """
     Use dipy to fit DTI
-    
+
     Parameters
     ----------
     in_file : str
@@ -58,7 +58,7 @@ def tensor_fitting(data, bvals, bvecs, mask_file=None):
 
     # Load information about the gradients:
     gtab = grad.gradient_table(self.inputs.bvals, self.inputs.bvecs)
-        
+
     # Fit it
     tenmodel = dti.TensorModel(gtab)
     return tenmodel.fit(data, mask), affine
@@ -84,7 +84,7 @@ class DTIOutputSpec(TraitedSpec):
 class DTI(BaseInterface):
     """
     Calculates the diffusion tensor model parameters
-    
+
     Example
     -------
 
@@ -99,7 +99,7 @@ class DTI(BaseInterface):
     output_spec = DTIOutputSpec
 
     def _run_interface(self, runtime):
-        ten_fit, affine = tensor_fitting(self.inputs.in_file, 
+        ten_fit, affine = tensor_fitting(self.inputs.in_file,
                                          self.inputs.bvals,
                                          self.inputs.bvecs,
                                          self.inputs.mask_file)
@@ -124,7 +124,7 @@ def _gen_filename(self, name):
     def _gen_outfilename(self):
         _, name, _ = split_filename(self.inputs.in_file)
         return name + '_dti.nii'
-    
+
 
 class TensorModeInputSpec(TraitedSpec):
     in_file = File(exists=True, mandatory=True,

nipype/interfaces/dipy/tests/test_auto_SimulateMultiTensor.py

@@ -7,6 +7,10 @@ def test_SimulateMultiTensor_inputs():
     ),
     bvalues=dict(usedefault=True,
     ),
+    diff_iso=dict(usedefault=True,
+    ),
+    diff_sf=dict(usedefault=True,
+    ),
     gradients=dict(),
     ignore_exception=dict(nohash=True,
     usedefault=True,

nipype/interfaces/fsl/__init__.py

@@ -16,7 +16,7 @@
                     ImageStats, FilterRegressor, Overlay, Slicer,
                     PlotTimeSeries, PlotMotionParams, ConvertXFM,
                     SwapDimensions, PowerSpectrum, Reorient2Std,
-                    Complex, InvWarp, WarpUtils, ConvertWarp, WarpPoints, 
+                    Complex, InvWarp, WarpUtils, ConvertWarp, WarpPoints,
                     WarpPointsToStd, RobustFOV)
 
 from .epi import (PrepareFieldmap, TOPUP, ApplyTOPUP, Eddy, EPIDeWarp,

nipype/interfaces/fsl/utils.py

@@ -36,10 +36,10 @@ class RobustFOVInputSpec(FSLCommandInputSpec):
                    desc='input filename',
                    argstr='-i %s', position=0, mandatory=True)
     out_roi = File(desc="ROI volume output name", argstr="-r %s",
-                   name_source=['in_file'], hash_files=False, 
+                   name_source=['in_file'], hash_files=False,
                    name_template='%s_ROI')
-    
-    
+
+
 class RobustFOVOutputSpec(TraitedSpec):
     out_roi = File(exists=True, desc="ROI volume output name")
 
@@ -48,7 +48,7 @@ class RobustFOV(FSLCommand):
     _cmd = 'robustfov'
     input_spec = RobustFOVInputSpec
     output_spec = RobustFOVOutputSpec
-    
+
 
 class ImageMeantsInputSpec(FSLCommandInputSpec):
     in_file = File(exists=True,

nipype/interfaces/semtools/diffusion/diffusion.py

@@ -23,7 +23,7 @@ class dtiaverage(SEMLikeCommandLine):
 
 category: Diffusion.Diffusion Tensor Images.CommandLineOnly
 
-description: dtiaverage is a program that allows to compute the average of an arbitrary number of tensor fields (listed after the --inputs option) This program is used in our pipeline as the last step of the atlas building processing. When all the tensor fields have been deformed in the same space, to create the average tensor field (--tensor_output) we use dtiaverage. 
+description: dtiaverage is a program that allows to compute the average of an arbitrary number of tensor fields (listed after the --inputs option) This program is used in our pipeline as the last step of the atlas building processing. When all the tensor fields have been deformed in the same space, to create the average tensor field (--tensor_output) we use dtiaverage.
  Several average method can be used (specified by the --method option): euclidian, log-euclidian and pga. The default being euclidian.
 
 version: 1.0.0
@@ -84,14 +84,14 @@ class dtiestim(SEMLikeCommandLine):
 
 category: Diffusion.Diffusion Weighted Images
 
-description: dtiestim is a tool that takes in a set of DWIs (with --dwi_image option) in nrrd format and estimates a tensor field out of it. The output tensor file name is specified with the --tensor_output option 
-There are several methods to estimate the tensors which you can specify with the option --method lls|wls|nls|ml . Here is a short description of the different methods: 
+description: dtiestim is a tool that takes in a set of DWIs (with --dwi_image option) in nrrd format and estimates a tensor field out of it. The output tensor file name is specified with the --tensor_output option
+There are several methods to estimate the tensors which you can specify with the option --method lls|wls|nls|ml . Here is a short description of the different methods:
         lls Linear least squares. Standard estimation technique that recovers the tensor parameters by multiplying the log of the normalized signal intensities by the pseudo-inverse of the gradient matrix. Default option.
         wls Weighted least squares. This method is similar to the linear least squares method except that the gradient matrix is weighted by the original lls estimate. (See Salvador, R., Pena, A., Menon, D. K., Carpenter, T. A., Pickard, J. D., and Bullmore, E. T. Formal characterization and extension of the linearized diffusion tensor model. Human Brain Mapping 24, 2 (Feb. 2005), 144-155. for more information on this method). This method is recommended for most applications. The weight for each iteration can be specified with the --weight_iterations.  It is not currently the default due to occasional matrix singularities.
-        nls Non-linear least squares. This method does not take the log of the signal and requires an optimization based on levenberg-marquadt to optimize the parameters of the signal. The lls estimate is used as an initialization. For this method the step size can be specified with the --step option. 
+        nls Non-linear least squares. This method does not take the log of the signal and requires an optimization based on levenberg-marquadt to optimize the parameters of the signal. The lls estimate is used as an initialization. For this method the step size can be specified with the --step option.
         ml Maximum likelihood estimation. This method is experimental and is not currently recommended. For this ml method the sigma can be specified with the option --sigma and the step size can be specified with the --step option.
 You can set a threshold (--threshold) to have the tensor estimated to only a subset of voxels. All the baseline voxel value higher than the threshold define the voxels where the tensors are computed. If not specified the threshold is calculated using an OTSU threshold on the baseline image.The masked generated by the -t option or by the otsu value can be saved with the --B0_mask_output option.
-dtiestim also can extract a few scalar images out of the DWI set of images: 
+dtiestim also can extract a few scalar images out of the DWI set of images:
         the average baseline image (--B0) which is the average of all the B0s.
         the IDWI (--idwi)which is the geometric mean of the diffusion images.
 You can also load a mask if you want to compute the tensors only where the voxels are non-zero (--brain_mask) or a negative mask and the tensors will be estimated where the negative mask has zero values (--bad_region_mask)
@@ -176,13 +176,13 @@ class dtiprocess(SEMLikeCommandLine):
 category: Diffusion.Diffusion Tensor Images
 
 description: dtiprocess is a tool that handles tensor fields. It takes as an input a tensor field in nrrd format.
-It can generate diffusion scalar properties out of the tensor field such as : FA (--fa_output), Gradient FA image (--fa_gradient_output), color FA (--color_fa_output), MD (--md_output), Frobenius norm (--frobenius_norm_output), lbd1, lbd2, lbd3 (--lambda{1,2,3}_output), binary map of voxel where if any of the eigenvalue is negative, the voxel is set to 1 (--negative_eigenvector_output) 
+It can generate diffusion scalar properties out of the tensor field such as : FA (--fa_output), Gradient FA image (--fa_gradient_output), color FA (--color_fa_output), MD (--md_output), Frobenius norm (--frobenius_norm_output), lbd1, lbd2, lbd3 (--lambda{1,2,3}_output), binary map of voxel where if any of the eigenvalue is negative, the voxel is set to 1 (--negative_eigenvector_output)
 It also creates 4D images out of the tensor field such as: Highest eigenvector map (highest eigenvector at each voxel) (--principal_eigenvector_output)
  Masking capabilities: For any of the processing done with dtiprocess, it's possible to apply it on a masked region of the tensor field. You need to use the --mask option for any of the option to be applied on that tensor field sub-region only. If you want to save the masked tensor field use the option --outmask and specify the new masked tensor field file name.
 dtiprocess also allows a range of transformations on the tensor fields. The transformed tensor field file name is specified with the option --deformation_output. There are 3 resampling interpolation methods specified with the tag --interpolation followed by the type to use (nearestneighbor, linear, cubic) Then you have several transformations possible to apply:
-        - Affine transformations using as an input 
+        - Affine transformations using as an input
         - itk affine transformation file (based on the itkAffineTransform class)
-        - Affine transformations using rview (details and download at http://www.doc.ic.ac.uk/~dr/software/). There are 2 versions of rview both creating transformation files called dof files. The old version of rview outputs text files containing the transformation parameters. It can be read in with the --dof_file option. The new version outputs binary dof files. These dof files can be transformed into human readable file with the dof2mat tool which is part of the rview package. So you need to save the output of dof2mat into a text file which can then be used with the -- newdof_file option. Usage example: dof2mat mynewdoffile.dof >> mynewdoffile.txt 	 dtiprocess --dti_image mytensorfield.nhdr --newdof_file mynewdoffile.txt --rot_output myaffinetensorfield.nhdr 
+        - Affine transformations using rview (details and download at http://www.doc.ic.ac.uk/~dr/software/). There are 2 versions of rview both creating transformation files called dof files. The old version of rview outputs text files containing the transformation parameters. It can be read in with the --dof_file option. The new version outputs binary dof files. These dof files can be transformed into human readable file with the dof2mat tool which is part of the rview package. So you need to save the output of dof2mat into a text file which can then be used with the -- newdof_file option. Usage example: dof2mat mynewdoffile.dof >> mynewdoffile.txt 	 dtiprocess --dti_image mytensorfield.nhdr --newdof_file mynewdoffile.txt --rot_output myaffinetensorfield.nhdr
 Non linear transformations as an input: The default transformation file type is d-field (displacement field) in nrrd format. The option to use is --forward with the name of the file. If the transformation file is a h-field you have to add the option --hField.
 
 version: 1.0.1

nipype/interfaces/semtools/diffusion/tractography/fiberprocess.py

@@ -35,8 +35,8 @@ class fiberprocess(SEMLikeCommandLine):
 
 category: Diffusion.Tractography
 
-description: fiberprocess is a tool that manage fiber files extracted from the fibertrack tool or any fiber tracking algorithm. It takes as an input .fib and .vtk files (--fiber_file) and saves the changed fibers (--fiber_output) into the 2 same formats. The main purpose of this tool is to deform the fiber file with a transformation field as an input (--displacement_field or --h_field depending if you deal with dfield or hfield). To use that option you need to specify the tensor field from which the fiber file was extracted with the option --tensor_volume. The transformation applied on the fiber file is the inverse of the one input. If the transformation is from one case to an atlas, fiberprocess assumes that the fiber file is in the atlas space and you want it in the original case space, so it's the inverse of the transformation which has been computed. 
-You have 2 options for fiber modification. You can either deform the fibers (their geometry) into the space OR you can keep the same geometry but map the diffusion properties (fa, md, lbd's...) of the original tensor field along the fibers at the corresponding locations. This is triggered by the --no_warp option. To use the previous example: when you have a tensor field in the original space and the deformed tensor field in the atlas space, you want to track the fibers in the atlas space, keeping this geometry but with the original case diffusion properties. Then you can specify the transformations field (from original case -> atlas) and the original tensor field with the --tensor_volume option. 
+description: fiberprocess is a tool that manage fiber files extracted from the fibertrack tool or any fiber tracking algorithm. It takes as an input .fib and .vtk files (--fiber_file) and saves the changed fibers (--fiber_output) into the 2 same formats. The main purpose of this tool is to deform the fiber file with a transformation field as an input (--displacement_field or --h_field depending if you deal with dfield or hfield). To use that option you need to specify the tensor field from which the fiber file was extracted with the option --tensor_volume. The transformation applied on the fiber file is the inverse of the one input. If the transformation is from one case to an atlas, fiberprocess assumes that the fiber file is in the atlas space and you want it in the original case space, so it's the inverse of the transformation which has been computed.
+You have 2 options for fiber modification. You can either deform the fibers (their geometry) into the space OR you can keep the same geometry but map the diffusion properties (fa, md, lbd's...) of the original tensor field along the fibers at the corresponding locations. This is triggered by the --no_warp option. To use the previous example: when you have a tensor field in the original space and the deformed tensor field in the atlas space, you want to track the fibers in the atlas space, keeping this geometry but with the original case diffusion properties. Then you can specify the transformations field (from original case -> atlas) and the original tensor field with the --tensor_volume option.
 With fiberprocess you can also binarize a fiber file. Using the --voxelize option will create an image where each voxel through which a fiber is passing is set to 1. The output is going to be a binary image with the values 0 or 1 by default but the 1 value voxel can be set to any number with the --voxel_label option. Finally you can create an image where the value at the voxel is the number of fiber passing through. (--voxelize_count_fibers)
 
 version: 1.0.0

nipype/interfaces/semtools/diffusion/tractography/fibertrack.py

@@ -33,7 +33,7 @@ class fibertrack(SEMLikeCommandLine):
 category: Diffusion.Tractography
 
 description: This program implements a simple streamline tractography method based on the principal eigenvector of the tensor field. A fourth order Runge-Kutta integration rule used to advance the streamlines.
-As a first parameter you have to input the tensor field (with the --input_tensor_file option). Then the region of interest image file is set with the --input_roi_file. Next you want to set the output fiber file name after the --output_fiber_file option. 
+As a first parameter you have to input the tensor field (with the --input_tensor_file option). Then the region of interest image file is set with the --input_roi_file. Next you want to set the output fiber file name after the --output_fiber_file option.
 You can specify the label value in the input_roi_file with the --target_label, --source_label and  --fobidden_label options. By default target label is 1, source label is 2 and forbidden label is 0. The source label is where the streamlines are seeded, the target label defines the voxels through which the fibers must pass by to be kept in the final fiber file and the forbidden label defines the voxels where the streamlines are stopped if they pass through it. There is also a --whole_brain option which, if enabled, consider both target and source labels of the roi image as target labels and all the voxels of the image are considered as sources.
 During the tractography, the --fa_min parameter is used as the minimum value needed at different voxel for the tracking to keep going along a streamline. The --step_size parameter is used for each iteration of the tracking algorithm and defines the length of each step. The --max_angle option defines the maximum angle allowed between two successive segments along the tracked fiber.