.. AUTO-GENERATED FILE -- DO NOT EDIT!

interfaces.fsl.preprocess
=========================


.. _nipype.interfaces.fsl.preprocess.ApplyWarp:


.. index:: ApplyWarp

ApplyWarp
---------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L913>`_

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' #doctest: +SKIP
>>> res = aw.run() #doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                image to be warped
        ref_file: (an existing file name)
                reference image

        [Optional]
        abswarp: (a boolean)
                treat warp field as absolute: x' = w(x)
                mutually_exclusive: relwarp
        args: (a string)
                Additional parameters to the command
        datatype: ('char' or 'short' or 'int' or 'float' or 'double')
                Force output data type [char short int float double].
        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
        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
        mask_file: (an existing file name)
                filename for mask image (in reference space)
        out_file: (a file name)
                output filename
        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)
        premat: (an existing file name)
                filename for pre-transform (affine matrix)
        relwarp: (a boolean)
                treat warp field as relative: x' = x + w(x)
                mutually_exclusive: abswarp
        superlevel: ('a' or an integer)
                level of intermediary supersampling, a for 'automatic' or integer level. Default = 2
        supersample: (a boolean)
                intermediary supersampling of output, default is off

Outputs::

        out_file: (an existing file name)
                Warped output file

.. _nipype.interfaces.fsl.preprocess.ApplyXfm:


.. index:: ApplyXfm

ApplyXfm
--------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L530>`_

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() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                input file
        reference: (an existing file name)
                reference file

        [Optional]
        angle_rep: ('quaternion' or 'euler')
                representation of rotation angles
        apply_xfm: (a boolean, nipype default value: True)
                apply transformation supplied by in_matrix_file
                requires: in_matrix_file
        args: (a string)
                Additional parameters to the command
        bins: (an integer)
                number of histogram bins
        coarse_search: (an integer)
                coarse search delta angle
        cost: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or 'labeldiff')
                cost function
        cost_func: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or
                 'labeldiff')
                cost function
        datatype: ('char' or 'short' or 'int' or 'float' or 'double')
                force output data type
        display_init: (a boolean)
                display initial matrix
        dof: (an integer)
                number of transform degrees of freedom
        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
        fine_search: (an integer)
                fine search delta angle
        force_scaling: (a boolean)
                force rescaling even for low-res images
        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
        in_weight: (an existing file name)
                File for input weighting volume
        interp: ('trilinear' or 'nearestneighbour' or 'sinc')
                final interpolation method used in reslicing
        min_sampling: (a float)
                set minimum voxel dimension for sampling
        no_clamp: (a boolean)
                do not use intensity clamping
        no_resample: (a boolean)
                do not change input sampling
        no_resample_blur: (a boolean)
                do not use blurring on downsampling
        no_search: (a boolean)
                set all angular searches to ranges 0 to 0
        out_file: (a file name)
                registered output file
        out_matrix_file: (a file name)
                output affine matrix in 4x4 asciii format
        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
        ref_weight: (an existing file name)
                File for reference weighting volume
        rigid2D: (a boolean)
                use 2D rigid body mode - ignores dof
        schedule: (an existing file name)
                replaces default schedule
        searchr_x: (a list of from 2 to 2 items which are an integer)
                search angles along x-axis, in degrees
        searchr_y: (a list of from 2 to 2 items which are an integer)
                search angles along y-axis, in degrees
        searchr_z: (a list of from 2 to 2 items which are an integer)
                search angles along z-axis, in degrees
        sinc_width: (an integer)
                full-width in voxels
        sinc_window: ('rectangular' or 'hanning' or 'blackman')
                sinc window
        uses_qform: (a boolean)
                initialize using sform or qform
        verbose: (an integer)
                verbose mode, 0 is least

Outputs::

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

.. _nipype.interfaces.fsl.preprocess.BET:


.. index:: BET

BET
---

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L116>`_

Wraps command **bet**

Use FSL BET command for skull stripping.

For complete details, see the `BET Documentation.
<http://www.fmrib.ox.ac.uk/fsl/bet2/index.html>`_

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() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                input file to skull strip

        [Optional]
        args: (a string)
                Additional parameters to the command
        center: (a list of at most 3 items which are an integer)
                center of gravity in voxels
        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
        functional: (a boolean)
                apply to 4D fMRI data
                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
        mesh: (a boolean)
                generate a vtk mesh brain surface
        no_output: (a boolean)
                Don't generate segmented output
        out_file: (a file name)
                name of output skull stripped image
        outline: (a boolean)
                create surface outline image
        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)
                mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
                 t2_guided
        radius: (an integer)
                head radius
        reduce_bias: (a boolean)
                bias field and neck cleanup
                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)
                mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
                 t2_guided
        robust: (a boolean)
                robust brain centre estimation (iterates BET several times)
                mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
                 t2_guided
        skull: (a boolean)
                create skull image
        surfaces: (a boolean)
                run bet2 and then betsurf to get additional skull and scalp surfaces (includes
                registrations)
                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)
                mutually_exclusive: functional, reduce_bias, robust, padding, remove_eyes, surfaces,
                 t2_guided
        threshold: (a boolean)
                apply thresholding to segmented brain image and mask
        vertical_gradient: (a float)
                vertical gradient in fractional intensity threshold (-1, 1)

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)

.. _nipype.interfaces.fsl.preprocess.FAST:


.. index:: FAST

FAST
----

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L286>`_

Wraps command **fast**

Use FSL FAST for segmenting and bias correction.

For complete details, see the `FAST Documentation.
<http://www.fmrib.ox.ac.uk/fsl/fast4/index.html>`_

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() #doctest: +SKIP

Inputs::

        [Mandatory]
        in_files: (an existing file name)
                image, or multi-channel set of images, to be segmented

        [Optional]
        args: (a string)
                Additional parameters to the command
        bias_iters: (1 <= an integer <= 10)
                number of main-loop iterations during bias-field removal
        bias_lowpass: (4 <= an integer <= 40)
                bias field smoothing extent (FWHM) in mm
        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
        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)
        init_seg_smooth: (0.0001 <= a floating point number <= 0.1)
                initial segmentation spatial smoothness (during bias field estimation)
        init_transform: (an existing file name)
                <standard2input.mat> initialise using priors
        iters_afterbias: (an integer >= 1)
                number of main-loop iterations after bias-field removal
        manual_seg: (an existing file name)
                Filename containing intensities
        mixel_smooth: (0.0 <= a floating point number <= 1.0)
                spatial smoothness for mixeltype
        no_bias: (a boolean)
                do not remove bias field
        no_pve: (a boolean)
                turn off PVE (partial volume estimation)
        number_classes: (1 <= an integer <= 10)
                number of tissue-type classes
        other_priors: (a list of from 3 to 3 items which are a file name)
                alternative prior images
        out_basename: (a file name)
                base name of output files
        output_biascorrected: (a boolean)
                output restored image (bias-corrected image)
        output_biasfield: (a boolean)
                output estimated bias field
        output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
                FSL output type
        probability_maps: (a boolean)
                outputs individual probability maps
        segment_iters: (1 <= an integer <= 50)
                number of segmentation-initialisation iterations
        segments: (a boolean)
                outputs a separate binary image for each tissue type
        use_priors: (a boolean)
                use priors throughout
        verbose: (a boolean)
                switch on diagnostic messages

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

.. _nipype.interfaces.fsl.preprocess.FIRST:


.. index:: FIRST

FIRST
-----

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L1321>`_

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() #doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                input data file
        out_file: (a file name, nipype default value: segmented)
                output data file

        [Optional]
        affine_file: (an existing file name)
                Affine matrix to use (e.g. img2std.mat) (does not re-run registration)
        args: (a string)
                Additional parameters to the command
        brain_extracted: (a boolean)
                Input structural image is already brain-extracted
        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
        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
                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
        no_cleanup: (a boolean)
                Input structural image is already brain-extracted
        output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
                FSL output type
        verbose: (a boolean)
                Use verbose logging.

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

.. _nipype.interfaces.fsl.preprocess.FLIRT:


.. index:: FLIRT

FLIRT
-----

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L476>`_

Wraps command **flirt**

Use FSL FLIRT for coregistration.

For complete details, see the `FLIRT Documentation.
<http://www.fmrib.ox.ac.uk/fsl/flirt/index.html>`_

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 = example_data('structural.nii')
>>> flt.inputs.reference = example_data('mni.nii')
>>> flt.inputs.out_file = 'moved_subject.nii'
>>> flt.inputs.out_matrix_file = 'subject_to_template.mat'
>>> res = flt.run() #doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                input file
        reference: (an existing file name)
                reference file

        [Optional]
        angle_rep: ('quaternion' or 'euler')
                representation of rotation angles
        apply_xfm: (a boolean)
                apply transformation supplied by in_matrix_file
                requires: in_matrix_file
        args: (a string)
                Additional parameters to the command
        bins: (an integer)
                number of histogram bins
        coarse_search: (an integer)
                coarse search delta angle
        cost: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or 'labeldiff')
                cost function
        cost_func: ('mutualinfo' or 'corratio' or 'normcorr' or 'normmi' or 'leastsq' or
                 'labeldiff')
                cost function
        datatype: ('char' or 'short' or 'int' or 'float' or 'double')
                force output data type
        display_init: (a boolean)
                display initial matrix
        dof: (an integer)
                number of transform degrees of freedom
        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
        fine_search: (an integer)
                fine search delta angle
        force_scaling: (a boolean)
                force rescaling even for low-res images
        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
        in_weight: (an existing file name)
                File for input weighting volume
        interp: ('trilinear' or 'nearestneighbour' or 'sinc')
                final interpolation method used in reslicing
        min_sampling: (a float)
                set minimum voxel dimension for sampling
        no_clamp: (a boolean)
                do not use intensity clamping
        no_resample: (a boolean)
                do not change input sampling
        no_resample_blur: (a boolean)
                do not use blurring on downsampling
        no_search: (a boolean)
                set all angular searches to ranges 0 to 0
        out_file: (a file name)
                registered output file
        out_matrix_file: (a file name)
                output affine matrix in 4x4 asciii format
        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
        ref_weight: (an existing file name)
                File for reference weighting volume
        rigid2D: (a boolean)
                use 2D rigid body mode - ignores dof
        schedule: (an existing file name)
                replaces default schedule
        searchr_x: (a list of from 2 to 2 items which are an integer)
                search angles along x-axis, in degrees
        searchr_y: (a list of from 2 to 2 items which are an integer)
                search angles along y-axis, in degrees
        searchr_z: (a list of from 2 to 2 items which are an integer)
                search angles along z-axis, in degrees
        sinc_width: (an integer)
                full-width in voxels
        sinc_window: ('rectangular' or 'hanning' or 'blackman')
                sinc window
        uses_qform: (a boolean)
                initialize using sform or qform
        verbose: (an integer)
                verbose mode, 0 is least

Outputs::

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

.. _nipype.interfaces.fsl.preprocess.FNIRT:


.. index:: FNIRT

FNIRT
-----

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L782>`_

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')) #doctest: +SKIP

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')#doctest: +SKIP

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

>>> fnirt_mprage.cmdline  #doctest: +SKIP
'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
        ref_file: (an existing file name)
                name of reference image

        [Optional]
        affine_file: (an existing file name)
                name of file containing affine transform
        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)
                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)
                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)
                mutually_exclusive: skip_refmask
        args: (a string)
                Additional parameters to the command
        bias_regularization_lambda: (a float)
                Weight of regularisation for bias-field, default 10000
        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
        config_file: ('T1_2_MNI152_2mm' or 'FA_2_FMRIB58_1mm' or an existing file name)
                Name of config file specifying command line arguments
        derive_from_ref: (a boolean)
                If true, ref image is used to calculate derivatives. Default false
        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
        fieldcoeff_file: (a boolean or a file name)
                name of output file with field coefficients or true
        hessian_precision: ('double' or 'float')
                Precision for representing Hessian, double or float. Default double
        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]
        in_intensitymap_file: (an existing file name)
                name of file/files containing initial intensity mapingusually generated by previos fnirt
                run
        inmask_file: (an existing file name)
                name of file with mask in input image space
        inmask_val: (a float)
                Value to mask out in --in image. Default =0.0
        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
        intensity_mapping_order: (an integer)
                Order of poynomial for mapping intensities, default 5
        inwarp_file: (an existing file name)
                name of file containing initial non-linear warps
        jacobian_file: (a boolean or a file name)
                name of file for writing out the Jacobianof the field (for diagnostic or VBM purposes)
        jacobian_range: (a tuple of the form: (a float, a float))
                Allowed range of Jacobian determinants, default 0.01, 100.0
        log_file: (a file name)
                Name of log-file
        max_nonlin_iter: (a list of items which are an integer)
                Max # of non-linear iterations list, default [5, 5, 5, 5]
        modulatedref_file: (a boolean or a file name)
                name of file for writing out intensity modulated--ref (for diagnostic purposes)
        out_intensitymap_file: (a boolean or a file name)
                name of files for writing information pertaining to intensity mapping
        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]
        refmask_file: (an existing file name)
                name of file with mask in reference space
        refmask_val: (a float)
                Value to mask out in --ref image. Default =0.0
        regularization_lambda: (a list of items which are a float)
                Weight of regularisation, default depending on --ssqlambda and --regmod switches. See
                user documetation.
        regularization_model: ('membrane_energy' or 'bending_energy')
                Model for regularisation of warp-field [membrane_energy bending_energy], default
                bending_energy
        skip_implicit_in_masking: (a boolean)
                skip implicit masking  based on valuein --in image. Default = 0
        skip_implicit_ref_masking: (a boolean)
                skip implicit masking  based on valuein --ref image. Default = 0
        skip_inmask: (a boolean)
                skip specified inmask if set, default false
                mutually_exclusive: apply_inmask
        skip_intensity_mapping: (a boolean)
                Skip estimate intensity-mapping default false
                mutually_exclusive: apply_intensity_mapping
        skip_lambda_ssq: (a boolean)
                If true, lambda is not weighted by current ssq, default false
        skip_refmask: (a boolean)
                Skip specified refmask if set, default false
                mutually_exclusive: apply_refmask
        spline_order: (an integer)
                Order of spline, 2->Qadratic spline, 3->Cubic spline. Default=3
        subsampling_scheme: (a list of items which are an integer)
                sub-sampling scheme, list, default [4, 2, 1, 1]
        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
        warped_file: (a file name)
                name of output image

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

.. _nipype.interfaces.fsl.preprocess.FUGUE:


.. index:: FUGUE

FUGUE
-----

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L1159>`_

Wraps command **fugue**

Use FSL FUGUE to unwarp epi's with fieldmaps

Examples
~~~~~~~~

Please insert examples for use of this command

Inputs::

        [Mandatory]

        [Optional]
        args: (a string)
                Additional parameters to the command
        asym_se_time: (a float)
                set the fieldmap asymmetric spin echo time (sec)
        despike_2dfilter: (a boolean)
                apply a 2D de-spiking filter
        despike_theshold: (a float)
                specify the threshold for de-spiking (default=3.0)
        dwell_time: (a float)
                set the EPI dwell time per phase-encode line - same as echo spacing - (sec)
        dwell_to_asym_ratio: (a float)
                set the dwell to asym time ratio
        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)
        fmap_out_file: (a file name)
                filename for saving fieldmap (rad/s)
        fourier_order: (an integer)
                apply Fourier (sinusoidal) fitting of order N
        icorr: (a boolean)
                apply intensity correction to unwarping (pixel shift method only)
                requires: shift_in_file
        icorr_only: (a boolean)
                apply intensity correction only
                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
        mask_file: (an existing file name)
                filename for loading valid mask
        median_2dfilter: (a boolean)
                apply 2D median filtering
        no_extend: (a boolean)
                do not apply rigid-body extrapolation to the fieldmap
        no_gap_fill: (a boolean)
                do not apply gap-filling measure to the fieldmap
        nokspace: (a boolean)
                do not use k-space forward warping
        output_type: ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
                FSL output type
        pava: (a boolean)
                apply monotonic enforcement via PAVA
        phase_conjugate: (a boolean)
                apply phase conjugate method of unwarping
        phasemap_file: (an existing file name)
                filename for input phase image
        poly_order: (an integer)
                apply polynomial fitting of order N
        save_unmasked_fmap: (a boolean or a file name)
                saves the unmasked fieldmap when using --savefmap
                requires: fmap_out_file
        save_unmasked_shift: (a boolean or a file name)
                saves the unmasked shiftmap when using --saveshift
                requires: shift_out_file
        shift_in_file: (an existing file name)
                filename for reading pixel shift volume
        shift_out_file: (a file name)
                filename for saving pixel shift volume
        smooth2d: (a float)
                apply 2D Gaussian smoothing of sigma N (in mm)
        smooth3d: (a float)
                apply 3D Gaussian smoothing of sigma N (in mm)
        unwarp_direction: ('x' or 'y' or 'z' or 'x-' or 'y-' or 'z-')
                specifies direction of warping (default y)
        unwarped_file: (a file name)
                apply unwarping and save as filename

Outputs::

        unwarped_file: (an existing file name)
                unwarped file

.. _nipype.interfaces.fsl.preprocess.MCFLIRT:


.. index:: MCFLIRT

MCFLIRT
-------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L593>`_

Wraps command **mcflirt**

Use FSL MCFLIRT to do within-modality motion correction.

For complete details, see the `MCFLIRT Documentation.
<http://www.fmrib.ox.ac.uk/fsl/mcflirt/index.html>`_

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() # doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                timeseries to motion-correct

        [Optional]
        args: (a string)
                Additional parameters to the command
        bins: (an integer)
                number of histogram bins
        cost: ('mutualinfo' or 'woods' or 'corratio' or 'normcorr' or 'normmi' or 'leastsquares')
                cost function to optimize
        dof: (an integer)
                degrees of freedom for the transformation
        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
        interpolation: ('spline' or 'nn' or 'sinc')
                interpolation method for transformation
        mean_vol: (a boolean)
                register to mean volume
        out_file: (a file name)
                file to write
        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
        ref_vol: (an integer)
                volume to align frames to
        rotation: (an integer)
                scaling factor for rotation tolerances
        save_mats: (a boolean)
                save transformation matrices
        save_plots: (a boolean)
                save transformation parameters
        save_rms: (a boolean)
                save rms displacement parameters
        scaling: (a float)
                scaling factor to use
        smooth: (a float)
                smoothing factor for the cost function
        stages: (an integer)
                stages (if 4, perform final search with sinc interpolation
        stats_imgs: (a boolean)
                produce variance and std. dev. images
        use_contour: (a boolean)
                run search on contour images
        use_gradient: (a boolean)
                run search on gradient images

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

.. _nipype.interfaces.fsl.preprocess.PRELUDE:


.. index:: PRELUDE

PRELUDE
-------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L1240>`_

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
                mutually_exclusive: magnitude_file, phase_file
        magnitude_file: (an existing file name)
                file containing magnitude image
                mutually_exclusive: complex_phase_file
        phase_file: (an existing file name)
                raw phase file
                mutually_exclusive: complex_phase_file

        [Optional]
        args: (a string)
                Additional parameters to the command
        end: (an integer)
                final image number to process (default Inf)
        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
        labelprocess2d: (a boolean)
                does label processing in 2D (slice at a time)
        mask_file: (an existing file name)
                filename of mask input volume
        num_partitions: (an integer)
                number of phase partitions to use
        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)
                mutually_exclusive: labelprocess2d
        process3d: (a boolean)
                forces all processing to be full 3D
                mutually_exclusive: labelprocess2d, process2d
        rawphase_file: (a file name)
                saving the raw phase output
        removeramps: (a boolean)
                remove phase ramps during unwrapping
        savemask_file: (a file name)
                saving the mask volume
        start: (an integer)
                first image number to process (default 0)
        threshold: (a float)
                intensity threshold for masking
        unwrapped_phase_file: (a file name)
                file containing unwrapepd phase

Outputs::

        unwrapped_phase_file: (an existing file name)
                unwrapped phase file

.. _nipype.interfaces.fsl.preprocess.SUSAN:


.. index:: SUSAN

SUSAN
-----

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L1042>`_

Wraps command **susan**

use FSL SUSAN to perform smoothing

Examples
~~~~~~~~

>>> from nipype.interfaces import fsl
>>> from nipype.testing import example_data
>>> print anatfile #doctest: +SKIP
anatomical.nii #doctest: +SKIP
>>> 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() #doctest: +SKIP

Inputs::

        [Mandatory]
        brightness_threshold: (a float)
                brightness threshold and should be greater than noise level and less than contrast of
                edges to be preserved.
        fwhm: (a float)
                fwhm of smoothing, in mm, gets converted using sqrt(8*log(2))
        in_file: (an existing file name)
                filename of input timeseries

        [Optional]
        args: (a string)
                Additional parameters to the command
        dimension: (3 or 2, nipype default value: 3)
                within-plane (2) or fully 3D (3)
        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
        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

Outputs::

        smoothed_file: (an existing file name)
                smoothed output file

.. _nipype.interfaces.fsl.preprocess.SliceTimer:


.. index:: SliceTimer

SliceTimer
----------

`Link to code <http://github.com/nipy/nipype/tree/99796c15f2e157774a3f54f878fdd06ad981a80b/nipype/interfaces/fsl/preprocess.py#L979>`_

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() #doctest: +SKIP

Inputs::

        [Mandatory]
        in_file: (an existing file name)
                filename of input timeseries

        [Optional]
        args: (a string)
                Additional parameters to the command
        custom_order: (an existing file name)
                filename of single-column custom interleave order file (first slice is referred to as 1
                not 0)
        custom_timings: (an existing file name)
                slice timings, in fractions of TR, range 0:1 (default is 0.5 = no shift)
        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)
        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
        interleaved: (a boolean)
                use interleaved acquisition
        out_file: (a file name)
                filename of output timeseries
        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
        time_repetition: (a float)
                Specify TR of data - default is 3s

Outputs::

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