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# interfaces.fsl.preprocess

## ApplyWarp

Link to code

Wraps command applywarp

Use FSL’s applywarp to apply the results of a FNIRT registration

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> aw = fsl.ApplyWarp()
    >>> aw.inputs.in_file = example_data('structural.nii')
    >>> aw.inputs.ref_file = example_data('mni.nii')
    >>> aw.inputs.field_file = 'my_coefficients_filed.nii'
    >>> res = aw.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            image to be warped
            flag: --in=%s, position: 0
    ref_file: (an existing file name)
            reference image
            flag: --ref=%s, position: 1
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    abswarp: (a boolean)
            treat warp field as absolute: x' = w(x)
            flag: --abs
            mutually_exclusive: relwarp
    args: (a string)
            Additional parameters to the command
            flag: %s
    datatype: ('char' or 'short' or 'int' or 'float' or 'double')
            Force output data type [char short int float double].
            flag: --datatype=%s
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    field_file: (an existing file name)
            file containing warp field
            flag: --warp=%s
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    interp: ('nn' or 'trilinear' or 'sinc' or 'spline')
            interpolation method
            flag: --interp=%s, position: -2
    mask_file: (an existing file name)
            filename for mask image (in reference space)
            flag: --mask=%s
    out_file: (a file name)
            output filename
            flag: --out=%s, position: 2
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    postmat: (an existing file name)
            filename for post-transform (affine matrix)
            flag: --postmat=%s
    premat: (an existing file name)
            filename for pre-transform (affine matrix)
            flag: --premat=%s
    relwarp: (a boolean)
            treat warp field as relative: x' = x + w(x)
            flag: --rel, position: -1
            mutually_exclusive: abswarp
    superlevel: ('a' or an integer)
            level of intermediary supersampling, a for 'automatic' or integer
            level. Default = 2
            flag: --superlevel=%s
    supersample: (a boolean)
            intermediary supersampling of output, default is off
            flag: --super

Outputs:

    out_file: (an existing file name)
            Warped output file

## ApplyXfm

Link to code

Wraps command flirt

Currently just a light wrapper around FLIRT, with no modifications

ApplyXfm is used to apply an existing tranform to an image

### Examples

    >>> import nipype.interfaces.fsl as fsl
    >>> from nipype.testing import example_data
    >>> applyxfm = fsl.ApplyXfm()
    >>> applyxfm.inputs.in_file = example_data('structural.nii')
    >>> applyxfm.inputs.in_matrix_file = example_data('trans.mat')
    >>> applyxfm.inputs.out_file = 'newfile.nii'
    >>> applyxfm.inputs.reference = example_data('mni.nii')
    >>> applyxfm.inputs.apply_xfm = True
    >>> result = applyxfm.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file
            flag: -in %s, position: 0
    reference: (an existing file name)
            reference file
            flag: -ref %s, position: 1
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    angle_rep: ('quaternion' or 'euler')
            representation of rotation angles
            flag: -anglerep %s
    apply_isoxfm: (a float)
            as applyxfm but forces isotropic resampling
            flag: -applyisoxfm %f
            mutually_exclusive: apply_xfm
    apply_xfm: (a boolean, nipype default value: True)
            apply transformation supplied by in_matrix_file
            flag: -applyxfm
            requires: in_matrix_file
    args: (a string)
            Additional parameters to the command
            flag: %s
    bbrslope: (a float)
            value of bbr slope
            flag: -bbrslope %f
    bbrtype: ('signed' or 'global_abs' or 'local_abs')
            type of bbr cost function: signed [default], global_abs, local_abs
            flag: -bbrtype %s
    bgvalue: (a float)
            use specified background value for points outside FOV
            flag: -setbackground %f
    bins: (an integer)
            number of histogram bins
            flag: -bins %d
    coarse_search: (an integer)
            coarse search delta angle
            flag: -coarsesearch %d
    cost: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or
             'leastsq' or 'labeldiff' or 'bbr')
            cost function
            flag: -cost %s
    cost_func: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or
             'leastsq' or 'labeldiff' or 'bbr')
            cost function
            flag: -searchcost %s
    datatype: ('char' or 'short' or 'int' or 'float' or 'double')
            force output data type
            flag: -datatype %s
    display_init: (a boolean)
            display initial matrix
            flag: -displayinit
    dof: (an integer)
            number of transform degrees of freedom
            flag: -dof %d
    echospacing: (a float)
            value of EPI echo spacing - units of seconds
            flag: -echospacing %f
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    fieldmap: (a file name)
            fieldmap image in rads/s - must be already registered to the
            reference image
            flag: -fieldmap %s
    fieldmapmask: (a file name)
            mask for fieldmap image
            flag: -fieldmapmask %s
    fine_search: (an integer)
            fine search delta angle
            flag: -finesearch %d
    force_scaling: (a boolean)
            force rescaling even for low-res images
            flag: -forcescaling
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_matrix_file: (a file name)
            input 4x4 affine matrix
            flag: -init %s
    in_weight: (an existing file name)
            File for input weighting volume
            flag: -inweight %s
    interp: ('trilinear' or 'nearestneighbour' or 'sinc' or 'spline')
            final interpolation method used in reslicing
            flag: -interp %s
    min_sampling: (a float)
            set minimum voxel dimension for sampling
            flag: -minsampling %f
    no_clamp: (a boolean)
            do not use intensity clamping
            flag: -noclamp
    no_resample: (a boolean)
            do not change input sampling
            flag: -noresample
    no_resample_blur: (a boolean)
            do not use blurring on downsampling
            flag: -noresampblur
    no_search: (a boolean)
            set all angular searches to ranges 0 to 0
            flag: -nosearch
    out_file: (a file name)
            registered output file
            flag: -out %s, position: 2
    out_log: (a file name)
            output log
            requires: save_log
    out_matrix_file: (a file name)
            output affine matrix in 4x4 asciii format
            flag: -omat %s, position: 3
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    padding_size: (an integer)
            for applyxfm: interpolates outside image by size
            flag: -paddingsize %d
    pedir: (an integer)
            phase encode direction of EPI - 1/2/3=x/y/z & -1/-2/-3=-x/-y/-z
            flag: -pedir %d
    ref_weight: (an existing file name)
            File for reference weighting volume
            flag: -refweight %s
    rigid2D: (a boolean)
            use 2D rigid body mode - ignores dof
            flag: -2D
    save_log: (a boolean)
            save to log file
    schedule: (an existing file name)
            replaces default schedule
            flag: -schedule %s
    searchr_x: (a list of from 2 to 2 items which are an integer)
            search angles along x-axis, in degrees
            flag: -searchrx %s
    searchr_y: (a list of from 2 to 2 items which are an integer)
            search angles along y-axis, in degrees
            flag: -searchry %s
    searchr_z: (a list of from 2 to 2 items which are an integer)
            search angles along z-axis, in degrees
            flag: -searchrz %s
    sinc_width: (an integer)
            full-width in voxels
            flag: -sincwidth %d
    sinc_window: ('rectangular' or 'hanning' or 'blackman')
            sinc window
            flag: -sincwindow %s
    uses_qform: (a boolean)
            initialize using sform or qform
            flag: -usesqform
    verbose: (an integer)
            verbose mode, 0 is least
            flag: -verbose %d
    wm_seg: (a file name)
            white matter segmentation volume needed by BBR cost function
            flag: -wmseg %s
    wmcoords: (a file name)
            white matter boundary coordinates for BBR cost function
            flag: -wmcoords %s
    wmnorms: (a file name)
            white matter boundary normals for BBR cost function
            flag: -wmnorms %s

Outputs:

    out_file: (an existing file name)
            path/name of registered file (if generated)
    out_log: (a file name)
            path/name of output log (if generated)
    out_matrix_file: (an existing file name)
            path/name of calculated affine transform (if generated)

## BET

Link to code

Wraps command bet

Use FSL BET command for skull stripping.

For complete details, see the BET Documentation.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import  example_data
    >>> btr = fsl.BET()
    >>> btr.inputs.in_file = example_data('structural.nii')
    >>> btr.inputs.frac = 0.7
    >>> res = btr.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to skull strip
            flag: %s, position: 0
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    center: (a list of at most 3 items which are an integer)
            center of gravity in voxels
            flag: -c %s
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    frac: (a float)
            fractional intensity threshold
            flag: -f %.2f
    functional: (a boolean)
            apply to 4D fMRI data
            flag: -F
            mutually_exclusive: functional, reduce_bias, robust, padding,
             remove_eyes, surfaces, t2_guided
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mask: (a boolean)
            create binary mask image
            flag: -m
    mesh: (a boolean)
            generate a vtk mesh brain surface
            flag: -e
    no_output: (a boolean)
            Don't generate segmented output
            flag: -n
    out_file: (a file name)
            name of output skull stripped image
            flag: %s, position: 1
    outline: (a boolean)
            create surface outline image
            flag: -o
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    padding: (a boolean)
            improve BET if FOV is very small in Z (by temporarily padding end
            slices)
            flag: -Z
            mutually_exclusive: functional, reduce_bias, robust, padding,
             remove_eyes, surfaces, t2_guided
    radius: (an integer)
            head radius
            flag: -r %d
    reduce_bias: (a boolean)
            bias field and neck cleanup
            flag: -B
            mutually_exclusive: functional, reduce_bias, robust, padding,
             remove_eyes, surfaces, t2_guided
    remove_eyes: (a boolean)
            eye & optic nerve cleanup (can be useful in SIENA)
            flag: -S
            mutually_exclusive: functional, reduce_bias, robust, padding,
             remove_eyes, surfaces, t2_guided
    robust: (a boolean)
            robust brain centre estimation (iterates BET several times)
            flag: -R
            mutually_exclusive: functional, reduce_bias, robust, padding,
             remove_eyes, surfaces, t2_guided
    skull: (a boolean)
            create skull image
            flag: -s
    surfaces: (a boolean)
            run bet2 and then betsurf to get additional skull and scalp surfaces
            (includes registrations)
            flag: -A
            mutually_exclusive: functional, reduce_bias, robust, padding,
             remove_eyes, surfaces, t2_guided
    t2_guided: (a file name)
            as with creating surfaces, when also feeding in non-brain-extracted
            T2 (includes registrations)
            flag: -A2 %s
            mutually_exclusive: functional, reduce_bias, robust, padding,
             remove_eyes, surfaces, t2_guided
    threshold: (a boolean)
            apply thresholding to segmented brain image and mask
            flag: -t
    vertical_gradient: (a float)
            vertical gradient in fractional intensity threshold (-1, 1)
            flag: -g %.2f

Outputs:

    inskull_mask_file: (a file name)
            path/name of inskull mask (if generated)
    inskull_mesh_file: (a file name)
            path/name of inskull mesh outline (if generated)
    mask_file: (a file name)
            path/name of binary brain mask (if generated)
    meshfile: (a file name)
            path/name of vtk mesh file (if generated)
    out_file: (a file name)
            path/name of skullstripped file (if generated)
    outline_file: (a file name)
            path/name of outline file (if generated)
    outskin_mask_file: (a file name)
            path/name of outskin mask (if generated)
    outskin_mesh_file: (a file name)
            path/name of outskin mesh outline (if generated)
    outskull_mask_file: (a file name)
            path/name of outskull mask (if generated)
    outskull_mesh_file: (a file name)
            path/name of outskull mesh outline (if generated)
    skull_mask_file: (a file name)
            path/name of skull mask (if generated)

## FAST

Link to code

Wraps command fast

Use FSL FAST for segmenting and bias correction.

For complete details, see the FAST Documentation.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data

Assign options through the `inputs` attribute:

    >>> fastr = fsl.FAST()
    >>> fastr.inputs.in_files = example_data('structural.nii')
    >>> out = fastr.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            image, or multi-channel set of images, to be segmented
            flag: %s, position: -1
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    bias_iters: (1 <= an integer <= 10)
            number of main-loop iterations during bias-field removal
            flag: -I %d
    bias_lowpass: (4 <= an integer <= 40)
            bias field smoothing extent (FWHM) in mm
            flag: -l %d
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    hyper: (0.0 <= a floating point number <= 1.0)
            segmentation spatial smoothness
            flag: -H %.2f
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    img_type: (1 or 2 or 3)
            int specifying type of image: (1 = T1, 2 = T2, 3 = PD)
            flag: -t %d
    init_seg_smooth: (0.0001 <= a floating point number <= 0.1)
            initial segmentation spatial smoothness (during bias field
            estimation)
            flag: -f %.3f
    init_transform: (an existing file name)
            <standard2input.mat> initialise using priors
            flag: -a %s
    iters_afterbias: (1 <= an integer <= 20)
            number of main-loop iterations after bias-field removal
            flag: -O %d
    manual_seg: (an existing file name)
            Filename containing intensities
            flag: -s %s
    mixel_smooth: (0.0 <= a floating point number <= 1.0)
            spatial smoothness for mixeltype
            flag: -R %.2f
    no_bias: (a boolean)
            do not remove bias field
            flag: -N
    no_pve: (a boolean)
            turn off PVE (partial volume estimation)
            flag: --nopve
    number_classes: (1 <= an integer <= 10)
            number of tissue-type classes
            flag: -n %d
    other_priors: (a list of from 3 to 3 items which are a file name)
            alternative prior images
            flag: -A %s
    out_basename: (a file name)
            base name of output files
            flag: -o %s
    output_biascorrected: (a boolean)
            output restored image (bias-corrected image)
            flag: -B
    output_biasfield: (a boolean)
            output estimated bias field
            flag: -b
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    probability_maps: (a boolean)
            outputs individual probability maps
            flag: -p
    segment_iters: (1 <= an integer <= 50)
            number of segmentation-initialisation iterations
            flag: -W %d
    segments: (a boolean)
            outputs a separate binary image for each tissue type
            flag: -g
    use_priors: (a boolean)
            use priors throughout
            flag: -P
    verbose: (a boolean)
            switch on diagnostic messages
            flag: -v

Outputs:

    bias_field: (a file name)
    mixeltype: (a file name)
            path/name of mixeltype volume file _mixeltype
    partial_volume_files: (a file name)
    partial_volume_map: (a file name)
            path/name of partial volume file _pveseg
    probability_maps: (a file name)
    restored_image: (a file name)
    tissue_class_files: (a file name)
    tissue_class_map: (an existing file name)
            path/name of binary segmented volume file one val for each class
            _seg

## FIRST

Link to code

Wraps command run_first_all

Use FSL’s run_first_all command to segment subcortical volumes

http://www.fmrib.ox.ac.uk/fsl/first/index.html

### Examples

    >>> from nipype.interfaces import fsl
    >>> first = fsl.FIRST()
    >>> first.inputs.in_file = 'structural.nii'
    >>> first.inputs.out_file = 'segmented.nii'
    >>> res = first.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input data file
            flag: -i %s, position: -2
    out_file: (a file name, nipype default value: segmented)
            output data file
            flag: -o %s, position: -1
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    affine_file: (an existing file name)
            Affine matrix to use (e.g. img2std.mat) (does not re-run
            registration)
            flag: -a %s, position: 6
    args: (a string)
            Additional parameters to the command
            flag: %s
    brain_extracted: (a boolean)
            Input structural image is already brain-extracted
            flag: -b, position: 2
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    list_of_specific_structures: (a list of at least 1 items which are a
             string)
            Runs only on the specified structures (e.g. L_Hipp, R_HippL_Accu,
            R_Accu, L_Amyg, R_AmygL_Caud, R_Caud, L_Pall, R_PallL_Puta, R_Puta,
            L_Thal, R_Thal, BrStem
            flag: -s %s, position: 5
    method: ('auto' or 'fast' or 'none')
            Method must be one of auto, fast, none, or it can be entered using
            the 'method_as_numerical_threshold' input
            flag: -m, position: 4
            mutually_exclusive: method_as_numerical_threshold
    method_as_numerical_threshold: (a float)
            Specify a numerical threshold value or use the 'method' input to
            choose auto, fast, or none
            flag: -m, position: 4
    no_cleanup: (a boolean)
            Input structural image is already brain-extracted
            flag: -d, position: 3
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    verbose: (a boolean)
            Use verbose logging.
            flag: -v, position: 1

Outputs:

    bvars: (an existing file name)
            bvars for each subcortical region
    original_segmentations: (an existing file name)
            3D image file containing the segmented regions as integer values.
            Uses CMA labelling
    segmentation_file: (an existing file name)
            4D image file containing a single volume per segmented region
    vtk_surfaces: (an existing file name)
            VTK format meshes for each subcortical region

## FLIRT

Link to code

Wraps command flirt

Use FSL FLIRT for coregistration.

For complete details, see the FLIRT Documentation.

To print out the command line help, use:
  fsl.FLIRT().inputs_help()

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> flt = fsl.FLIRT(bins=640, cost_func='mutualinfo')
    >>> flt.inputs.in_file = 'structural.nii'
    >>> flt.inputs.reference = 'mni.nii'
    >>> flt.inputs.output_type = "NIFTI_GZ"
    >>> flt.cmdline
    'flirt -in structural.nii -ref mni.nii -out structural_flirt.nii.gz -omat structural_flirt.mat -bins 640 -searchcost mutualinfo'
    >>> res = flt.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file
            flag: -in %s, position: 0
    reference: (an existing file name)
            reference file
            flag: -ref %s, position: 1
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    angle_rep: ('quaternion' or 'euler')
            representation of rotation angles
            flag: -anglerep %s
    apply_isoxfm: (a float)
            as applyxfm but forces isotropic resampling
            flag: -applyisoxfm %f
            mutually_exclusive: apply_xfm
    apply_xfm: (a boolean)
            apply transformation supplied by in_matrix_file
            flag: -applyxfm
            requires: in_matrix_file
    args: (a string)
            Additional parameters to the command
            flag: %s
    bbrslope: (a float)
            value of bbr slope
            flag: -bbrslope %f
    bbrtype: ('signed' or 'global_abs' or 'local_abs')
            type of bbr cost function: signed [default], global_abs, local_abs
            flag: -bbrtype %s
    bgvalue: (a float)
            use specified background value for points outside FOV
            flag: -setbackground %f
    bins: (an integer)
            number of histogram bins
            flag: -bins %d
    coarse_search: (an integer)
            coarse search delta angle
            flag: -coarsesearch %d
    cost: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or
             'leastsq' or 'labeldiff' or 'bbr')
            cost function
            flag: -cost %s
    cost_func: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or
             'leastsq' or 'labeldiff' or 'bbr')
            cost function
            flag: -searchcost %s
    datatype: ('char' or 'short' or 'int' or 'float' or 'double')
            force output data type
            flag: -datatype %s
    display_init: (a boolean)
            display initial matrix
            flag: -displayinit
    dof: (an integer)
            number of transform degrees of freedom
            flag: -dof %d
    echospacing: (a float)
            value of EPI echo spacing - units of seconds
            flag: -echospacing %f
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    fieldmap: (a file name)
            fieldmap image in rads/s - must be already registered to the
            reference image
            flag: -fieldmap %s
    fieldmapmask: (a file name)
            mask for fieldmap image
            flag: -fieldmapmask %s
    fine_search: (an integer)
            fine search delta angle
            flag: -finesearch %d
    force_scaling: (a boolean)
            force rescaling even for low-res images
            flag: -forcescaling
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_matrix_file: (a file name)
            input 4x4 affine matrix
            flag: -init %s
    in_weight: (an existing file name)
            File for input weighting volume
            flag: -inweight %s
    interp: ('trilinear' or 'nearestneighbour' or 'sinc' or 'spline')
            final interpolation method used in reslicing
            flag: -interp %s
    min_sampling: (a float)
            set minimum voxel dimension for sampling
            flag: -minsampling %f
    no_clamp: (a boolean)
            do not use intensity clamping
            flag: -noclamp
    no_resample: (a boolean)
            do not change input sampling
            flag: -noresample
    no_resample_blur: (a boolean)
            do not use blurring on downsampling
            flag: -noresampblur
    no_search: (a boolean)
            set all angular searches to ranges 0 to 0
            flag: -nosearch
    out_file: (a file name)
            registered output file
            flag: -out %s, position: 2
    out_log: (a file name)
            output log
            requires: save_log
    out_matrix_file: (a file name)
            output affine matrix in 4x4 asciii format
            flag: -omat %s, position: 3
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    padding_size: (an integer)
            for applyxfm: interpolates outside image by size
            flag: -paddingsize %d
    pedir: (an integer)
            phase encode direction of EPI - 1/2/3=x/y/z & -1/-2/-3=-x/-y/-z
            flag: -pedir %d
    ref_weight: (an existing file name)
            File for reference weighting volume
            flag: -refweight %s
    rigid2D: (a boolean)
            use 2D rigid body mode - ignores dof
            flag: -2D
    save_log: (a boolean)
            save to log file
    schedule: (an existing file name)
            replaces default schedule
            flag: -schedule %s
    searchr_x: (a list of from 2 to 2 items which are an integer)
            search angles along x-axis, in degrees
            flag: -searchrx %s
    searchr_y: (a list of from 2 to 2 items which are an integer)
            search angles along y-axis, in degrees
            flag: -searchry %s
    searchr_z: (a list of from 2 to 2 items which are an integer)
            search angles along z-axis, in degrees
            flag: -searchrz %s
    sinc_width: (an integer)
            full-width in voxels
            flag: -sincwidth %d
    sinc_window: ('rectangular' or 'hanning' or 'blackman')
            sinc window
            flag: -sincwindow %s
    uses_qform: (a boolean)
            initialize using sform or qform
            flag: -usesqform
    verbose: (an integer)
            verbose mode, 0 is least
            flag: -verbose %d
    wm_seg: (a file name)
            white matter segmentation volume needed by BBR cost function
            flag: -wmseg %s
    wmcoords: (a file name)
            white matter boundary coordinates for BBR cost function
            flag: -wmcoords %s
    wmnorms: (a file name)
            white matter boundary normals for BBR cost function
            flag: -wmnorms %s

Outputs:

    out_file: (an existing file name)
            path/name of registered file (if generated)
    out_log: (a file name)
            path/name of output log (if generated)
    out_matrix_file: (an existing file name)
            path/name of calculated affine transform (if generated)

## FNIRT

Link to code

Wraps command fnirt

Use FSL FNIRT for non-linear registration.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> fnt = fsl.FNIRT(affine_file=example_data('trans.mat'))
    >>> res = fnt.run(ref_file=example_data('mni.nii', in_file=example_data('structural.nii'))

T1 -> Mni153

    >>> from nipype.interfaces import fsl
    >>> fnirt_mprage = fsl.FNIRT()
    >>> fnirt_mprage.inputs.in_fwhm = [8, 4, 2, 2]
    >>> fnirt_mprage.inputs.subsampling_scheme = [4, 2, 1, 1]

Specify the resolution of the warps

    >>> fnirt_mprage.inputs.warp_resolution = (6, 6, 6)
    >>> res = fnirt_mprage.run(in_file='structural.nii', ref_file='mni.nii', warped_file='warped.nii', fieldcoeff_file='fieldcoeff.nii')

We can check the command line and confirm that it’s what we expect.

    >>> fnirt_mprage.cmdline
    'fnirt --cout=fieldcoeff.nii --in=structural.nii --infwhm=8,4,2,2 --ref=mni.nii --subsamp=4,2,1,1 --warpres=6,6,6 --iout=warped.nii'

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            name of input image
            flag: --in=%s
    ref_file: (an existing file name)
            name of reference image
            flag: --ref=%s
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    affine_file: (an existing file name)
            name of file containing affine transform
            flag: --aff=%s
    apply_inmask: (a list of items which are 0 or 1)
            list of iterations to use input mask on (1 to use, 0 to skip)
            flag: --applyinmask=%s
            mutually_exclusive: skip_inmask
    apply_intensity_mapping: (a list of items which are 0 or 1)
            List of subsampling levels to apply intensity mapping for (0 to
            skip, 1 to apply)
            flag: --estint=%s
            mutually_exclusive: skip_intensity_mapping
    apply_refmask: (a list of items which are 0 or 1)
            list of iterations to use reference mask on (1 to use, 0 to skip)
            flag: --applyrefmask=%s
            mutually_exclusive: skip_refmask
    args: (a string)
            Additional parameters to the command
            flag: %s
    bias_regularization_lambda: (a float)
            Weight of regularisation for bias-field, default 10000
            flag: --biaslambda=%f
    biasfield_resolution: (a tuple of the form: (an integer, an integer,
             an integer))
            Resolution (in mm) of bias-field modelling local intensities,
            default 50, 50, 50
            flag: --biasres=%d,%d,%d
    config_file: ('T1_2_MNI152_2mm' or 'FA_2_FMRIB58_1mm' or an existing
             file name)
            Name of config file specifying command line arguments
            flag: --config=%s
    derive_from_ref: (a boolean)
            If true, ref image is used to calculate derivatives. Default false
            flag: --refderiv
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    field_file: (a boolean or a file name)
            name of output file with field or true
            flag: --fout=%s
    fieldcoeff_file: (a boolean or a file name)
            name of output file with field coefficients or true
            flag: --cout=%s
    hessian_precision: ('double' or 'float')
            Precision for representing Hessian, double or float. Default double
            flag: --numprec=%s
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_fwhm: (a list of items which are an integer)
            FWHM (in mm) of gaussian smoothing kernel for input volume, default
            [6, 4, 2, 2]
            flag: --infwhm=%s
    in_intensitymap_file: (an existing file name)
            name of file/files containing initial intensity mapingusually
            generated by previos fnirt run
            flag: --intin=%s
    inmask_file: (an existing file name)
            name of file with mask in input image space
            flag: --inmask=%s
    inmask_val: (a float)
            Value to mask out in --in image. Default =0.0
            flag: --impinval=%f
    intensity_mapping_model: ('none' or 'global_linear' or
             'global_non_linearlocal_linear' or 'global_non_linear_with_bias' or
             'local_non_linear')
            Model for intensity-mapping
            flag: --intmod=%s
    intensity_mapping_order: (an integer)
            Order of poynomial for mapping intensities, default 5
            flag: --intorder=%d
    inwarp_file: (an existing file name)
            name of file containing initial non-linear warps
            flag: --inwarp=%s
    jacobian_file: (a boolean or a file name)
            name of file for writing out the Jacobianof the field (for
            diagnostic or VBM purposes)
            flag: --jout=%s
    jacobian_range: (a tuple of the form: (a float, a float))
            Allowed range of Jacobian determinants, default 0.01, 100.0
            flag: --jacrange=%f,%f
    log_file: (a file name)
            Name of log-file
            flag: --logout=%s
    max_nonlin_iter: (a list of items which are an integer)
            Max # of non-linear iterations list, default [5, 5, 5, 5]
            flag: --miter=%s
    modulatedref_file: (a boolean or a file name)
            name of file for writing out intensity modulated--ref (for
            diagnostic purposes)
            flag: --refout=%s
    out_intensitymap_file: (a boolean or a file name)
            name of files for writing information pertaining to intensity
            mapping
            flag: --intout=%s
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    ref_fwhm: (a list of items which are an integer)
            FWHM (in mm) of gaussian smoothing kernel for ref volume, default
            [4, 2, 0, 0]
            flag: --reffwhm=%s
    refmask_file: (an existing file name)
            name of file with mask in reference space
            flag: --refmask=%s
    refmask_val: (a float)
            Value to mask out in --ref image. Default =0.0
            flag: --imprefval=%f
    regularization_lambda: (a list of items which are a float)
            Weight of regularisation, default depending on --ssqlambda and
            --regmod switches. See user documetation.
            flag: --lambda=%s
    regularization_model: ('membrane_energy' or 'bending_energy')
            Model for regularisation of warp-field [membrane_energy
            bending_energy], default bending_energy
            flag: --regmod=%s
    skip_implicit_in_masking: (a boolean)
            skip implicit masking based on valuein --in image. Default = 0
            flag: --impinm=0
    skip_implicit_ref_masking: (a boolean)
            skip implicit masking based on valuein --ref image. Default = 0
            flag: --imprefm=0
    skip_inmask: (a boolean)
            skip specified inmask if set, default false
            flag: --applyinmask=0
            mutually_exclusive: apply_inmask
    skip_intensity_mapping: (a boolean)
            Skip estimate intensity-mapping default false
            flag: --estint=0
            mutually_exclusive: apply_intensity_mapping
    skip_lambda_ssq: (a boolean)
            If true, lambda is not weighted by current ssq, default false
            flag: --ssqlambda=0
    skip_refmask: (a boolean)
            Skip specified refmask if set, default false
            flag: --applyrefmask=0
            mutually_exclusive: apply_refmask
    spline_order: (an integer)
            Order of spline, 2->Qadratic spline, 3->Cubic spline. Default=3
            flag: --splineorder=%d
    subsampling_scheme: (a list of items which are an integer)
            sub-sampling scheme, list, default [4, 2, 1, 1]
            flag: --subsamp=%s
    warp_resolution: (a tuple of the form: (an integer, an integer, an
             integer))
            (approximate) resolution (in mm) of warp basis in x-, y- and
            z-direction, default 10, 10, 10
            flag: --warpres=%d,%d,%d
    warped_file: (a file name)
            name of output image
            flag: --iout=%s

Outputs:

    field_file: (a file name)
            file with warp field
    fieldcoeff_file: (an existing file name)
            file with field coefficients
    jacobian_file: (a file name)
            file containing Jacobian of the field
    log_file: (a file name)
            Name of log-file
    modulatedref_file: (a file name)
            file containing intensity modulated --ref
    out_intensitymap_file: (a file name)
            file containing info pertaining to intensity mapping
    warped_file: (an existing file name)
            warped image

## FUGUE

Link to code

Wraps command fugue

FUGUE is, most generally, a set of tools for EPI distortion correction.

Distortions may be corrected for
  1. improving registration with non-distorted images (e.g. structurals), or
  2. dealing with motion-dependent changes.

FUGUE is designed to deal only with the first case - improving registration.

### Examples

Unwarping an input image (shift map is known)

    >>> from nipype.interfaces.fsl.preprocess import FUGUE
    >>> fugue = FUGUE()
    >>> fugue.inputs.in_file = 'epi.nii'
    >>> fugue.inputs.mask_file = 'epi_mask.nii'
    >>> fugue.inputs.shift_in_file = 'vsm.nii'  # Previously computed with fugue as well
    >>> fugue.inputs.unwarp_direction = 'y'
    >>> fugue.inputs.output_type = "NIFTI_GZ"
    >>> fugue.cmdline

‘fugue –in=epi.nii –mask=epi_mask.nii –loadshift=vsm.nii –unwarpdir=y –unwarp=epi_unwarped.nii.gz’ >>> fugue.run() #doctest: +SKIP

Warping an input image (shift map is known)

    >>> from nipype.interfaces.fsl.preprocess import FUGUE
    >>> fugue = FUGUE()
    >>> fugue.inputs.in_file = 'epi.nii'
    >>> fugue.inputs.forward_warping = True
    >>> fugue.inputs.mask_file = 'epi_mask.nii'
    >>> fugue.inputs.shift_in_file = 'vsm.nii'  # Previously computed with fugue as well
    >>> fugue.inputs.unwarp_direction = 'y'
    >>> fugue.inputs.output_type = "NIFTI_GZ"
    >>> fugue.cmdline

‘fugue –in=epi.nii –mask=epi_mask.nii –loadshift=vsm.nii –unwarpdir=y –warp=epi_warped.nii.gz’ >>> fugue.run() #doctest: +SKIP

Computing the vsm (unwrapped phase map is known)

    >>> from nipype.interfaces.fsl.preprocess import FUGUE
    >>> fugue = FUGUE()
    >>> fugue.inputs.phasemap_in_file = 'epi_phasediff.nii'
    >>> fugue.inputs.mask_file = 'epi_mask.nii'
    >>> fugue.inputs.dwell_to_asym_ratio = (0.77e-3 * 3) / 2.46e-3
    >>> fugue.inputs.unwarp_direction = 'y'
    >>> fugue.inputs.save_shift = True
    >>> fugue.inputs.output_type = "NIFTI_GZ"
    >>> fugue.cmdline

‘fugue –dwelltoasym=0.9390243902 –mask=epi_mask.nii –phasemap=epi_phasediff.nii –saveshift=epi_phasediff_vsm.nii.gz –unwarpdir=y’ >>> fugue.run() #doctest: +SKIP

Inputs:

    [Mandatory]
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    asym_se_time: (a float)
            set the fieldmap asymmetric spin echo time (sec)
            flag: --asym=%.10f
    despike_2dfilter: (a boolean)
            apply a 2D de-spiking filter
            flag: --despike
    despike_threshold: (a float)
            specify the threshold for de-spiking (default=3.0)
            flag: --despikethreshold=%s
    dwell_time: (a float)
            set the EPI dwell time per phase-encode line - same as echo spacing
            - (sec)
            flag: --dwell=%.10f
    dwell_to_asym_ratio: (a float)
            set the dwell to asym time ratio
            flag: --dwelltoasym=%.10f
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    fmap_in_file: (an existing file name)
            filename for loading fieldmap (rad/s)
            flag: --loadfmap=%s
    fmap_out_file: (a file name)
            filename for saving fieldmap (rad/s)
            flag: --savefmap=%s
    forward_warping: (a boolean, nipype default value: False)
            apply forward warping instead of unwarping
    fourier_order: (an integer)
            apply Fourier (sinusoidal) fitting of order N
            flag: --fourier=%d
    icorr: (a boolean)
            apply intensity correction to unwarping (pixel shift method only)
            flag: --icorr
            requires: shift_in_file
    icorr_only: (a boolean)
            apply intensity correction only
            flag: --icorronly
            requires: unwarped_file
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    in_file: (an existing file name)
            filename of input volume
            flag: --in=%s
    mask_file: (an existing file name)
            filename for loading valid mask
            flag: --mask=%s
    median_2dfilter: (a boolean)
            apply 2D median filtering
            flag: --median
    no_extend: (a boolean)
            do not apply rigid-body extrapolation to the fieldmap
            flag: --noextend
    no_gap_fill: (a boolean)
            do not apply gap-filling measure to the fieldmap
            flag: --nofill
    nokspace: (a boolean)
            do not use k-space forward warping
            flag: --nokspace
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    pava: (a boolean)
            apply monotonic enforcement via PAVA
            flag: --pava
    phase_conjugate: (a boolean)
            apply phase conjugate method of unwarping
            flag: --phaseconj
    phasemap_in_file: (an existing file name)
            filename for input phase image
            flag: --phasemap=%s
    poly_order: (an integer)
            apply polynomial fitting of order N
            flag: --poly=%d
    save_fmap: (a boolean)
            write field map volume
            mutually_exclusive: save_unmasked_fmap
    save_shift: (a boolean)
            write pixel shift volume
            mutually_exclusive: save_unmasked_shift
    save_unmasked_fmap: (a boolean)
            saves the unmasked fieldmap when using --savefmap
            flag: --unmaskfmap
            mutually_exclusive: save_fmap
    save_unmasked_shift: (a boolean)
            saves the unmasked shiftmap when using --saveshift
            flag: --unmaskshift
            mutually_exclusive: save_shift
    shift_in_file: (an existing file name)
            filename for reading pixel shift volume
            flag: --loadshift=%s
    shift_out_file: (a file name)
            filename for saving pixel shift volume
            flag: --saveshift=%s
    smooth2d: (a float)
            apply 2D Gaussian smoothing of sigma N (in mm)
            flag: --smooth2=%.2f
    smooth3d: (a float)
            apply 3D Gaussian smoothing of sigma N (in mm)
            flag: --smooth3=%.2f
    unwarp_direction: ('x' or 'y' or 'z' or 'x-' or 'y-' or 'z-')
            specifies direction of warping (default y)
            flag: --unwarpdir=%s
    unwarped_file: (a file name)
            apply unwarping and save as filename
            flag: --unwarp=%s
            mutually_exclusive: warped_file
            requires: in_file
    warped_file: (a file name)
            apply forward warping and save as filename
            flag: --warp=%s
            mutually_exclusive: unwarped_file
            requires: in_file

Outputs:

    fmap_out_file: (a file name)
            fieldmap file
    shift_out_file: (a file name)
            voxel shift map file
    unwarped_file: (a file name)
            unwarped file
    warped_file: (a file name)
            forward warped file

## MCFLIRT

Link to code

Wraps command mcflirt

Use FSL MCFLIRT to do within-modality motion correction.

For complete details, see the MCFLIRT Documentation.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> mcflt = fsl.MCFLIRT(in_file=example_data('functional.nii'), cost='mutualinfo')
    >>> res = mcflt.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            timeseries to motion-correct
            flag: -in %s, position: 0
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    bins: (an integer)
            number of histogram bins
            flag: -bins %d
    cost: ('mutualinfo' or 'woods' or 'corratio' or 'normcorr' or
             'normmi' or 'leastsquares')
            cost function to optimize
            flag: -cost %s
    dof: (an integer)
            degrees of freedom for the transformation
            flag: -dof %d
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    init: (an existing file name)
            inital transformation matrix
            flag: -init %s
    interpolation: ('spline' or 'nn' or 'sinc')
            interpolation method for transformation
            flag: -%s_final
    mean_vol: (a boolean)
            register to mean volume
            flag: -meanvol
    out_file: (a file name)
            file to write
            flag: -out %s
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    ref_file: (an existing file name)
            target image for motion correction
            flag: -reffile %s
    ref_vol: (an integer)
            volume to align frames to
            flag: -refvol %d
    rotation: (an integer)
            scaling factor for rotation tolerances
            flag: -rotation %d
    save_mats: (a boolean)
            save transformation matrices
            flag: -mats
    save_plots: (a boolean)
            save transformation parameters
            flag: -plots
    save_rms: (a boolean)
            save rms displacement parameters
            flag: -rmsabs -rmsrel
    scaling: (a float)
            scaling factor to use
            flag: -scaling %.2f
    smooth: (a float)
            smoothing factor for the cost function
            flag: -smooth %.2f
    stages: (an integer)
            stages (if 4, perform final search with sinc interpolation
            flag: -stages %d
    stats_imgs: (a boolean)
            produce variance and std. dev. images
            flag: -stats
    use_contour: (a boolean)
            run search on contour images
            flag: -edge
    use_gradient: (a boolean)
            run search on gradient images
            flag: -gdt

Outputs:

    mat_file: (an existing file name)
            transformation matrices
    mean_img: (an existing file name)
            mean timeseries image
    out_file: (an existing file name)
            motion-corrected timeseries
    par_file: (an existing file name)
            text-file with motion parameters
    rms_files: (an existing file name)
            absolute and relative displacement parameters
    std_img: (an existing file name)
            standard deviation image
    variance_img: (an existing file name)
            variance image

## PRELUDE

Link to code

Wraps command prelude

Use FSL prelude to do phase unwrapping

### Examples

Please insert examples for use of this command

Inputs:

    [Mandatory]
    complex_phase_file: (an existing file name)
            complex phase input volume
            flag: --complex=%s
            mutually_exclusive: magnitude_file, phase_file
    magnitude_file: (an existing file name)
            file containing magnitude image
            flag: --abs=%s
            mutually_exclusive: complex_phase_file
    phase_file: (an existing file name)
            raw phase file
            flag: --phase=%s
            mutually_exclusive: complex_phase_file
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    end: (an integer)
            final image number to process (default Inf)
            flag: --end=%d
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    label_file: (a file name)
            saving the area labels output
            flag: --labels=%s
    labelprocess2d: (a boolean)
            does label processing in 2D (slice at a time)
            flag: --labelslices
    mask_file: (an existing file name)
            filename of mask input volume
            flag: --mask=%s
    num_partitions: (an integer)
            number of phase partitions to use
            flag: --numphasesplit=%d
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    process2d: (a boolean)
            does all processing in 2D (slice at a time)
            flag: --slices
            mutually_exclusive: labelprocess2d
    process3d: (a boolean)
            forces all processing to be full 3D
            flag: --force3D
            mutually_exclusive: labelprocess2d, process2d
    rawphase_file: (a file name)
            saving the raw phase output
            flag: --rawphase=%s
    removeramps: (a boolean)
            remove phase ramps during unwrapping
            flag: --removeramps
    savemask_file: (a file name)
            saving the mask volume
            flag: --savemask=%s
    start: (an integer)
            first image number to process (default 0)
            flag: --start=%d
    threshold: (a float)
            intensity threshold for masking
            flag: --thresh=%.10f
    unwrapped_phase_file: (a file name)
            file containing unwrapepd phase
            flag: --unwrap=%s

Outputs:

    unwrapped_phase_file: (an existing file name)
            unwrapped phase file

## SUSAN

Link to code

Wraps command susan

use FSL SUSAN to perform smoothing

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> print anatfile
    anatomical.nii
    >>> sus = fsl.SUSAN()
    >>> sus.inputs.in_file = example_data('structural.nii')
    >>> sus.inputs.brightness_threshold = 2000.0
    >>> sus.inputs.fwhm = 8.0
    >>> result = sus.run()

Inputs:

    [Mandatory]
    brightness_threshold: (a float)
            brightness threshold and should be greater than noise level and less
            than contrast of edges to be preserved.
            flag: %.10f, position: 2
    fwhm: (a float)
            fwhm of smoothing, in mm, gets converted using sqrt(8*log(2))
            flag: %.10f, position: 3
    in_file: (an existing file name)
            filename of input timeseries
            flag: %s, position: 1
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    dimension: (3 or 2, nipype default value: 3)
            within-plane (2) or fully 3D (3)
            flag: %d, position: 4
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    out_file: (a file name)
            output file name
            flag: %s, position: -1
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    usans: (a list of at most 2 items which are a tuple of the form: (an
             existing file name, a float), nipype default value: [])
            determines whether the smoothing area (USAN) is to be found from
            secondary images (0, 1 or 2). A negative value for any brightness
            threshold will auto-set the threshold at 10% of the robust range
    use_median: (1 or 0, nipype default value: 1)
            whether to use a local median filter in the cases where single-point
            noise is detected
            flag: %d, position: 5

Outputs:

    smoothed_file: (an existing file name)
            smoothed output file

## SliceTimer

Link to code

Wraps command slicetimer

use FSL slicetimer to perform slice timing correction.

### Examples

    >>> from nipype.interfaces import fsl
    >>> from nipype.testing import example_data
    >>> st = fsl.SliceTimer()
    >>> st.inputs.in_file = example_data('functional.nii')
    >>> st.inputs.interleaved = True
    >>> result = st.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            filename of input timeseries
            flag: --in=%s, position: 0
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output: `stream` - displays to terminal
            immediately, `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

    [Optional]
    args: (a string)
            Additional parameters to the command
            flag: %s
    custom_order: (an existing file name)
            filename of single-column custom interleave order file (first slice
            is referred to as 1 not 0)
            flag: --ocustom=%s
    custom_timings: (an existing file name)
            slice timings, in fractions of TR, range 0:1 (default is 0.5 = no
            shift)
            flag: --tcustom=%s
    environ: (a dictionary with keys which are a value of type 'str' and
             with values which are a value of type 'str', nipype default value:
             {})
            Environment variables
    global_shift: (a float)
            shift in fraction of TR, range 0:1 (default is 0.5 = no shift)
            flag: --tglobal
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    index_dir: (a boolean)
            slice indexing from top to bottom
            flag: --down
    interleaved: (a boolean)
            use interleaved acquisition
            flag: --odd
    out_file: (a file name)
            filename of output timeseries
            flag: --out=%s
    output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or
             'NIFTI')
            FSL output type
    slice_direction: (1 or 2 or 3)
            direction of slice acquisition (x=1, y=2, z=3) - default is z
            flag: --direction=%d
    time_repetition: (a float)
            Specify TR of data - default is 3s
            flag: --repeat=%f

Outputs:

    slice_time_corrected_file: (an existing file name)
            slice time corrected file