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# interfaces.afni.preprocess

## Allineate

Link to code

Wraps command 3dAllineate

Program to align one dataset (the ‘source’) to a base dataset

For complete details, see the 3dAllineate Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> allineate = afni.Allineate()
    >>> allineate.inputs.in_file = 'functional.nii'
    >>> allineate.inputs.out_file= 'functional_allineate.nii'
    >>> allineate.inputs.matrix= 'cmatrix.mat'
    >>> res = allineate.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dAllineate
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    autobox: (a boolean)
            Expand the -automask function to enclose a rectangular
                            box that holds the irregular mask.
    automask: (an integer)
            Compute a mask function, set a value for dilation or 0.
    autoweight: (a string)
            Compute a weight function using the 3dAutomask
                           algorithm plus some blurring of the base image.
    center_of_mass: (a string)
            Use the center-of-mass calculation to bracket the shifts.
    check: (a list of items which are 'leastsq' or 'ls' or 'mutualinfo' or 'mi' or
             'corratio_mul' or 'crM' or 'norm_mutualinfo' or 'nmi' or 'hellinger' or 'hel' or
             'corratio_add' or 'crA' or 'corratio_uns' or 'crU')
            After cost functional optimization is done, start at the
                            final parameters and RE-optimize using this new cost functions.
                            If the results are too different, a warning message will be
                            printed.  However, the final parameters from the original
                            optimization will be used to create the output dataset.
    convergence: (a float)
            Convergence test in millimeters (default 0.05mm).
    cost: ('leastsq' or 'ls' or 'mutualinfo' or 'mi' or 'corratio_mul' or 'crM' or
             'norm_mutualinfo' or 'nmi' or 'hellinger' or 'hel' or 'corratio_add' or 'crA' or
             'corratio_uns' or 'crU')
            Defines the 'cost' function that defines the matching
                            between the source and the base
    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
    epi: (a boolean)
            Treat the source dataset as being composed of warped
                            EPI slices, and the base as comprising anatomically
                            'true' images.  Only phase-encoding direction image
                            shearing and scaling will be allowed with this option.
    final_interpolation: ('nearestneighbour' or 'linear' or 'cubic' or 'quintic' or 'wsinc5')
            Defines interpolation method used to create the output dataset
    fine_blur: (a float)
            Set the blurring radius to use in the fine resolution
                           pass to 'x' mm.  A small amount (1-2 mm?) of blurring at
                           the fine step may help with convergence, if there is
                           some problem, especially if the base volume is very noisy.
                           [Default == 0 mm = no blurring at the final alignment pass]
    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: (a file name)
            matrix to align input file
    in_param_file: (an existing file name)
            Read warp parameters from file and apply them to
                              the source dataset, and produce a new dataset
    interpolation: ('nearestneighbour' or 'linear' or 'cubic' or 'quintic')
            Defines interpolation method to use during matching
    master: (an existing file name)
            Write the output dataset on the same grid as this file
    newgrid: (a float)
            Write the output dataset using isotropic grid spacing in mm
    nmatch: (an integer)
            Use at most n scattered points to match the datasets.
    no_pad: (a boolean)
            Do not use zero-padding on the base image.
    nomask: (a boolean)
            Don't compute the autoweight/mask; if -weight is not
                            also used, then every voxel will be counted equally.
    nwarp: ('bilinear' or 'cubic' or 'quintic' or 'heptic' or 'nonic' or 'poly3' or 'poly5'
             or 'poly7' or 'poly9')
            Experimental nonlinear warping: bilinear or legendre poly.
    nwarp_fixdep: (a list of items which are 'X' or 'Y' or 'Z' or 'I' or 'J' or 'K')
            To fix non-linear warp dependency along directions.
    nwarp_fixmot: (a list of items which are 'X' or 'Y' or 'Z' or 'I' or 'J' or 'K')
            To fix motion along directions.
    one_pass: (a boolean)
            Use only the refining pass -- do not try a coarse
                            resolution pass first.  Useful if you know that only
                            small amounts of image alignment are needed.
    out_file: (a file name)
            output file from 3dAllineate
    out_matrix: (a file name)
            Save the transformation matrix for each volume.
    out_param_file: (a file name)
            Save the warp parameters in ASCII (.1D) format.
    out_weight_file: (a file name)
            Write the weight volume to disk as a dataset
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    reference: (an existing file name)
            file to be used as reference, the first volume will be used
            if not given the reference will be the first volume of in_file.
    replacebase: (a boolean)
            If the source has more than one volume, then after the first
                            volume is aligned to the base
    replacemeth: ('leastsq' or 'ls' or 'mutualinfo' or 'mi' or 'corratio_mul' or 'crM' or
             'norm_mutualinfo' or 'nmi' or 'hellinger' or 'hel' or 'corratio_add' or 'crA' or
             'corratio_uns' or 'crU')
            After first volume is aligned, switch method for later volumes.
                            For use with '-replacebase'.
    source_automask: (an integer)
            Automatically mask the source dataset with dilation or 0.
    source_mask: (an existing file name)
            mask the input dataset
    suffix: (a string, nipype default value: _allineate)
            out_file suffix
    two_best: (an integer)
            In the coarse pass, use the best 'bb' set of initial
                           points to search for the starting point for the fine
                           pass.  If bb==0, then no search is made for the best
                           starting point, and the identity transformation is
                           used as the starting point.  [Default=5; min=0 max=11]
    two_blur: (a float)
            Set the blurring radius for the first pass in mm.
    two_first: (a boolean)
            Use -twopass on the first image to be registered, and
                           then on all subsequent images from the source dataset,
                           use results from the first image's coarse pass to start
                           the fine pass.
    two_pass: (a boolean)
            Use a two pass alignment strategy for all volumes, searching
                          for a large rotation+shift and then refining the alignment.
    usetemp: (a boolean)
            temporary file use
    warp_type: ('shift_only' or 'shift_rotate' or 'shift_rotate_scale' or 'affine_general')
            Set the warp type.
    warpfreeze: (a boolean)
            Freeze the non-rigid body parameters after first volume.
    weight_file: (an existing file name)
            Set the weighting for each voxel in the base dataset;
                            larger weights mean that voxel count more in the cost function.
                            Must be defined on the same grid as the base dataset
    zclip: (a boolean)
            Replace negative values in the input datasets (source & base) with zero.

Outputs:

    matrix: (a file name)
            matrix to align input file
    out_file: (a file name)
            output image file name

## AutoTcorrelate

Link to code

Wraps command 3dAutoTcorrelate

Computes the correlation coefficient between the time series of each pair of voxels in the input dataset, and stores the output into a new anatomical bucket dataset [scaled to shorts to save memory space].

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> corr = afni.AutoTcorrelate()
    >>> corr.inputs.in_file = 'functional.nii'
    >>> corr.inputs.out_file = 'my_similarity_matrix.1D'
    >>> corr.inputs.polort = -1
    >>> corr.inputs.eta2 = True
    >>> corr.inputs.mask = 'mask.nii'
    >>> corr.inputs.mask_only_targets = True
    >>> corr.cmdline
    '3dAutoTcorrelate -eta2 -mask mask.nii -mask_only_targets -prefix ...my_similarity_matrix.1D -polort -1 functional.nii'
    >>> res = corr.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            timeseries x space (volume or surface) file
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    eta2: (a boolean)
            eta^2 similarity
    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: (an existing file name)
            mask of voxels
    mask_only_targets: (a boolean)
            use mask only on targets voxels
            mutually_exclusive: mask_source
    mask_source: (an existing file name)
            mask for source voxels
            mutually_exclusive: mask_only_targets
    out_file: (a file name, nipype default value: %s_similarity_matrix.1D)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    polort: (an integer)
            Remove polynomical trend of order m or -1 for no detrending
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## Autobox

Link to code

Wraps command 3dAutobox

Computes size of a box that fits around the volume. Also can be used to crop the volume to that box.

For complete details, see the `3dAutobox Documentation. <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dAutobox.html>

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> abox = afni.Autobox()
    >>> abox.inputs.in_file = 'structural.nii'
    >>> abox.inputs.padding = 5
    >>> res = abox.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    no_clustering: (a boolean)
            Don't do any clustering to find box. Any non-zero
                            voxel will be preserved in the cropped volume.
                            The default method uses some clustering to find the
                            cropping box, and will clip off small isolated blobs.
    out_file: (a file name)
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    padding: (an integer)
            Number of extra voxels to pad on each side of box
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (a file name)
            output file
    x_max: (an integer)
    x_min: (an integer)
    y_max: (an integer)
    y_min: (an integer)
    z_max: (an integer)
    z_min: (an integer)

## Automask

Link to code

Wraps command 3dAutomask

Create a brain-only mask of the image using AFNI 3dAutomask command

For complete details, see the 3dAutomask Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> automask = afni.Automask()
    >>> automask.inputs.in_file = 'functional.nii'
    >>> automask.inputs.dilate = 1
    >>> automask.inputs.outputtype = "NIFTI"
    >>> automask.cmdline
    '3dAutomask -apply_prefix functional_masked.nii -dilate 1 -prefix functional_mask.nii functional.nii'
    >>> res = automask.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dAutomask
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    apply_mask: (a file name)
            output file from 3dAutomask
    apply_suffix: (a string)
            out_file suffix
    args: (a string)
            Additional parameters to the command
    brain_file: (a file name, nipype default value: %s_masked)
            output file from 3dAutomask
    clfrac: (a float)
            sets the clip level fraction (must be 0.1-0.9). A small value will tend to make the mask
            larger [default = 0.5].
    dilate: (an integer)
            dilate the mask outwards
    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
    erode: (an integer)
            erode the mask inwards
    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_suffix: (a string)
            out_file suffix
    out_file: (a file name, nipype default value: %s_mask)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    brain_file: (a file name)
            brain file (skull stripped)
    out_file: (an existing file name)
            mask file

## Bandpass

Link to code

Wraps command 3dBandpass

Program to lowpass and/or highpass each voxel time series in a dataset, offering more/different options than Fourier

For complete details, see the 3dBandpass Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> from nipype.testing import  example_data
    >>> bandpass = afni.Bandpass()
    >>> bandpass.inputs.in_file = example_data('functional.nii')
    >>> bandpass.inputs.highpass = 0.005
    >>> bandpass.inputs.lowpass = 0.1
    >>> res = bandpass.run()

Inputs:

    [Mandatory]
    highpass: (a float)
            highpass
    in_file: (an existing file name)
            input file to 3dBandpass
    lowpass: (a float)
            lowpass
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    automask: (a boolean)
            Create a mask from the input dataset
    blur: (a float)
            Blur (inside the mask only) with a filter
                                width (FWHM) of 'fff' millimeters.
    despike: (a boolean)
            Despike each time series before other processing.
                              ++ Hopefully, you don't actually need to do this,
                                 which is why it is optional.
    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
    localPV: (a float)
            Replace each vector by the local Principal Vector
                                (AKA first singular vector) from a neighborhood
                                of radius 'rrr' millimiters.
                               ++ Note that the PV time series is L2 normalized.
                               ++ This option is mostly for Bob Cox to have fun with.
    mask: (an existing file name)
            mask file
    nfft: (an integer)
            set the FFT length [must be a legal value]
    no_detrend: (a boolean)
            Skip the quadratic detrending of the input that
                                occurs before the FFT-based bandpassing.
                               ++ You would only want to do this if the dataset
                                  had been detrended already in some other program.
    normalize: (a boolean)
            Make all output time series have L2 norm = 1
                               ++ i.e., sum of squares = 1
    notrans: (a boolean)
            Don't check for initial positive transients in the data:
                               ++ The test is a little slow, so skipping it is OK,
                               if you KNOW the data time series are transient-free.
    orthogonalize_dset: (an existing file name)
            Orthogonalize each voxel to the corresponding
                               voxel time series in dataset 'fset', which must
                               have the same spatial and temporal grid structure
                               as the main input dataset.
                               ++ At present, only one '-dsort' option is allowed.
    orthogonalize_file: (an existing file name)
            Also orthogonalize input to columns in f.1D
                               ++ Multiple '-ort' options are allowed.
    out_file: (a file name, nipype default value: %s_bp)
            output file from 3dBandpass
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix
    tr: (a float)
            set time step (TR) in sec [default=from dataset header]

Outputs:

    out_file: (an existing file name)
            output file

## BlurInMask

Link to code

Wraps command 3dBlurInMask

Blurs a dataset spatially inside a mask. That’s all. Experimental.

For complete details, see the `3dBlurInMask Documentation. <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dBlurInMask.html>

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> bim = afni.BlurInMask()
    >>> bim.inputs.in_file = 'functional.nii'
    >>> bim.inputs.mask = 'mask.nii'
    >>> bim.inputs.fwhm = 5.0
    >>> res = bim.run()

Inputs:

    [Mandatory]
    fwhm: (a float)
            fwhm kernel size
    in_file: (an existing file name)
            input file to 3dSkullStrip
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    automask: (a boolean)
            Create an automask from the input dataset.
    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
    float_out: (a boolean)
            Save dataset as floats, no matter what the input data type is.
    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 file name)
            Mask dataset, if desired.  Blurring will occur only within the mask.  Voxels NOT in the
            mask will be set to zero in the output.
    multimask: (a file name)
            Multi-mask dataset -- each distinct nonzero value in dataset will be treated as a
            separate mask for blurring purposes.
    options: (a string)
            options
    out_file: (a file name)
            output to the file
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    preserve: (a boolean)
            Normally, voxels not in the mask will be set to zero in the output.  If you want the
            original values in the dataset to be preserved in the output, use this option.
    suffix: (a string, nipype default value: _blurmask)
            out_file suffix

Outputs:

    out_file: (an existing file name)
            output file

## BrickStat

Link to code

Wraps command 3dBrickStat

Compute maximum and/or minimum voxel values of an input dataset

For complete details, see the 3dBrickStat Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> brickstat = afni.BrickStat()
    >>> brickstat.inputs.in_file = 'functional.nii'
    >>> brickstat.inputs.mask = 'skeleton_mask.nii.gz'
    >>> brickstat.inputs.min = True
    >>> res = brickstat.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dmaskave
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    mask: (an existing file name)
            -mask dset = use dset as mask to include/exclude voxels
    min: (a boolean)
            print the minimum value in dataset
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype

Outputs:

    min_val: (a float)
            output

## Calc

Link to code

Wraps command 3dcalc

This program does voxel-by-voxel arithmetic on 3D datasets

For complete details, see the 3dcalc Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> calc = afni.Calc()
    >>> calc.inputs.in_file_a = 'functional.nii'
    >>> calc.inputs.in_file_b = 'functional2.nii'
    >>> calc.inputs.expr='a*b'
    >>> calc.inputs.out_file =  'functional_calc.nii.gz'
    >>> calc.inputs.outputtype = "NIFTI"
    >>> calc.cmdline
    '3dcalc -a functional.nii  -b functional2.nii -expr "a*b" -prefix functional_calc.nii.gz'

Inputs:

    [Mandatory]
    expr: (a string)
            expr
    in_file_a: (an existing file name)
            input file to 3dcalc
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    in_file_b: (an existing file name)
            operand file to 3dcalc
    other: (a file name)
            other options
    out_file: (a file name, nipype default value: %s_calc)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    single_idx: (an integer)
            volume index for in_file_a
    start_idx: (an integer)
            start index for in_file_a
            requires: stop_idx
    stop_idx: (an integer)
            stop index for in_file_a
            requires: start_idx
    suffix: (a string, nipype default value: _calc)
            out_file suffix

Outputs:

    out_file: (an existing file name)
            output file

## Copy

Link to code

Wraps command 3dcopy

Copies an image of one type to an image of the same or different type using 3dcopy command

For complete details, see the 3dcopy Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> copy = afni.Copy()
    >>> copy.inputs.in_file = 'functional.nii'
    >>> copy.inputs.out_file = 'new_func.nii'
    >>> res = copy.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dcopy
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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, nipype default value: %s_copy)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## Despike

Link to code

Wraps command 3dDespike

Removes ‘spikes’ from the 3D+time input dataset

For complete details, see the 3dDespike Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> despike = afni.Despike()
    >>> despike.inputs.in_file = 'functional.nii'
    >>> res = despike.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dDespike
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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, nipype default value: %s_despike)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## Detrend

Link to code

Wraps command 3dDetrend

This program removes components from voxel time series using linear least squares

For complete details, see the 3dDetrend Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> detrend = afni.Detrend()
    >>> detrend.inputs.in_file = 'functional.nii'
    >>> detrend.inputs.args = '-polort 2'
    >>> res = detrend.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dDetrend
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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, nipype default value: %s_detrend)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## Fim

Link to code

Wraps command 3dfim+

Program to calculate the cross-correlation of an ideal reference waveform with the measured FMRI time series for each voxel

For complete details, see the 3dfim+ Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> fim = afni.Fim()
    >>> fim.inputs.in_file = 'functional.nii'
    >>> fim.inputs.ideal_file= 'seed.1D'
    >>> fim.inputs.out_file = 'functional_corr.nii'
    >>> fim.inputs.out = 'Correlation'
    >>> fim.inputs.fim_thr = 0.0009
    >>> res = fim.run()

Inputs:

    [Mandatory]
    ideal_file: (an existing file name)
            ideal time series file name
    in_file: (an existing file name)
            input file to 3dfim+
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    fim_thr: (a float)
            fim internal mask threshold value
    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: (a string)
            Flag to output the specified parameter
    out_file: (a file name, nipype default value: %s_fim)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## Fourier

Link to code

Wraps command 3dFourier

Program to lowpass and/or highpass each voxel time series in a dataset, via the FFT

For complete details, see the 3dFourier Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> fourier = afni.Fourier()
    >>> fourier.inputs.in_file = 'functional.nii'
    >>> fourier.inputs.args = '-retrend'
    >>> fourier.inputs.highpass = 0.005
    >>> fourier.inputs.lowpass = 0.1
    >>> res = fourier.run()

Inputs:

    [Mandatory]
    highpass: (a float)
            highpass
    in_file: (an existing file name)
            input file to 3dFourier
    lowpass: (a float)
            lowpass
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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, nipype default value: %s_fourier)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## Maskave

Link to code

Wraps command 3dmaskave

Computes average of all voxels in the input dataset which satisfy the criterion in the options list

For complete details, see the 3dmaskave Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> maskave = afni.Maskave()
    >>> maskave.inputs.in_file = 'functional.nii'
    >>> maskave.inputs.mask= 'seed_mask.nii'
    >>> maskave.inputs.quiet= True
    >>> maskave.cmdline
    '3dmaskave -mask seed_mask.nii -quiet functional.nii > functional_maskave.1D'
    >>> res = maskave.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dmaskave
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    mask: (an existing file name)
            matrix to align input file
    out_file: (a file name, nipype default value: %s_maskave.1D)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    quiet: (a boolean)
            matrix to align input file
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## Merge

Link to code

Wraps command 3dmerge

Merge or edit volumes using AFNI 3dmerge command

For complete details, see the 3dmerge Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> merge = afni.Merge()
    >>> merge.inputs.in_files = ['functional.nii', 'functional2.nii']
    >>> merge.inputs.blurfwhm = 4
    >>> merge.inputs.doall = True
    >>> merge.inputs.out_file = 'e7.nii'
    >>> res = merge.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    blurfwhm: (an integer)
            FWHM blur value (mm)
    doall: (a boolean)
            apply options to all sub-bricks in dataset
    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, nipype default value: %s_merge)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## ROIStats

Link to code

Wraps command 3dROIstats

Display statistics over masked regions

For complete details, see the 3dROIstats Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> roistats = afni.ROIStats()
    >>> roistats.inputs.in_file = 'functional.nii'
    >>> roistats.inputs.mask = 'skeleton_mask.nii.gz'
    >>> roistats.inputs.quiet=True
    >>> res = roistats.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dROIstats
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    mask: (an existing file name)
            input mask
    mask_f2short: (a boolean)
            Tells the program to convert a float mask to short integers, by simple rounding.
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    quiet: (a boolean)
            execute quietly

Outputs:

    stats: (an existing file name)
            output

## Refit

Link to code

Wraps command 3drefit

Changes some of the information inside a 3D dataset’s header

For complete details, see the `3drefit Documentation. <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3drefit.html>

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> refit = afni.Refit()
    >>> refit.inputs.in_file = 'structural.nii'
    >>> refit.inputs.deoblique=True
    >>> res = refit.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3drefit
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    deoblique: (a boolean)
            replace current transformation matrix with cardinal matrix
    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, nipype default value: %s_refit)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string, nipype default value: _refit)
            out_file suffix
    xorigin: (a string)
            x distance for edge voxel offset
    yorigin: (a string)
            y distance for edge voxel offset
    zorigin: (a string)
            z distance for edge voxel offset

Outputs:

    out_file: (an existing file name)
            output file

## Resample

Link to code

Wraps command 3dresample

Resample or reorient an image using AFNI 3dresample command

For complete details, see the 3dresample Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> resample = afni.Resample()
    >>> resample.inputs.in_file = 'functional.nii'
    >>> resample.inputs.orientation= 'RPI'
    >>> res = resample.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dresample
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    master: (a file name)
            align dataset grid to a reference file
    orientation: (a string)
            new orientation code
    out_file: (a file name, nipype default value: %s_resample)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    resample_mode: ('NN' or 'Li' or 'Cu' or 'Bk')
            resampling method from set {'NN', 'Li', 'Cu', 'Bk'}.  These are for 'Nearest Neighbor',
            'Linear', 'Cubic' and 'Blocky' interpolation, respectively. Default is NN.
    suffix: (a string)
            output image suffix
    voxel_size: (a tuple of the form: (a float, a float, a float))
            resample to new dx, dy and dz

Outputs:

    out_file: (an existing file name)
            output file

## SkullStrip

Link to code

Wraps command 3dSkullStrip

A program to extract the brain from surrounding tissue from MRI T1-weighted images

For complete details, see the 3dSkullStrip Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> skullstrip = afni.SkullStrip()
    >>> skullstrip.inputs.in_file = 'functional.nii'
    >>> skullstrip.inputs.args = '-o_ply'
    >>> res = skullstrip.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dSkullStrip
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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, nipype default value: %s_skullstrip)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## TCat

Link to code

Wraps command 3dTcat

Concatenate sub-bricks from input datasets into one big 3D+time dataset

For complete details, see the 3dTcat Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> tcat = afni.TCat()
    >>> tcat.inputs.in_files = ['functional.nii', 'functional2.nii']
    >>> tcat.inputs.out_file= 'functional_tcat.nii'
    >>> tcat.inputs.rlt = '+'
    >>> res = tcat.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            input file to 3dTcat
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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, nipype default value: %s_tcat)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    rlt: (a string)
            options
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## TCorrMap

Link to code

Wraps command 3dTcorrMap

For each voxel time series, computes the correlation between it and all other voxels, and combines this set of values into the output dataset(s) in some way.

For complete details, see the `3dTcorrMap Documentation. <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTcorrMap.html>

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> tcm = afni.TcorrMap()
    >>> tcm.inputs.in_file = 'functional.nii'
    >>> tcm.inputs.mask = 'mask.nii'
    >>> tcm.mean_file = '%s_meancorr.nii'
    >>> res = tcm.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    absolute_threshold: (a file name)
            mutually_exclusive: absolute_threshold, var_absolute_threshold,
             var_absolute_threshold_normalize
    args: (a string)
            Additional parameters to the command
    automask: (a boolean)
    average_expr: (a file name)
            mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
    average_expr_nonzero: (a file name)
            mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
    bandpass: (a tuple of the form: (a float, a float))
    blur_fwhm: (a float)
    correlation_maps: (a file name)
    correlation_maps_masked: (a file name)
    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
    expr: (a string)
    histogram: (a file name)
    histogram_bin_numbers: (an integer)
    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: (an existing file name)
    mean_file: (a file name)
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    pmean: (a file name)
    polort: (an integer)
    qmean: (a file name)
    regress_out_timeseries: (a file name)
    seeds: (an existing file name)
            mutually_exclusive: s, e, e, d, s, _, w, i, d, t, h
    seeds_width: (a float)
            mutually_exclusive: s, e, e, d, s
    sum_expr: (a file name)
            mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
    thresholds: (a list of items which are an integer)
    var_absolute_threshold: (a file name)
            mutually_exclusive: absolute_threshold, var_absolute_threshold,
             var_absolute_threshold_normalize
    var_absolute_threshold_normalize: (a file name)
            mutually_exclusive: absolute_threshold, var_absolute_threshold,
             var_absolute_threshold_normalize
    zmean: (a file name)

Outputs:

    absolute_threshold: (a file name)
    average_expr: (a file name)
    average_expr_nonzero: (a file name)
    correlation_maps: (a file name)
    correlation_maps_masked: (a file name)
    histogram: (a file name)
    mean_file: (a file name)
    pmean: (a file name)
    qmean: (a file name)
    sum_expr: (a file name)
    var_absolute_threshold: (a file name)
    var_absolute_threshold_normalize: (a file name)
    zmean: (a file name)

## TCorrelate

Link to code

Wraps command 3dTcorrelate

Computes the correlation coefficient between corresponding voxel time series in two input 3D+time datasets ‘xset’ and ‘yset’

For complete details, see the 3dTcorrelate Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> tcorrelate = afni.TCorrelate()
    >>> tcorrelate.inputs.xset= 'u_rc1s1_Template.nii'
    >>> tcorrelate.inputs.yset = 'u_rc1s2_Template.nii'
    >>> tcorrelate.inputs.out_file = 'functional_tcorrelate.nii.gz'
    >>> tcorrelate.inputs.polort = -1
    >>> tcorrelate.inputs.pearson = True
    >>> res = tcarrelate.run()

Inputs:

    [Mandatory]
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output
    xset: (an existing file name)
            input xset
    yset: (an existing file name)
            input yset

    [Optional]
    args: (a string)
            Additional parameters to the command
    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, nipype default value: %s_tcorr)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    pearson: (a boolean)
            Correlation is the normal Pearson correlation coefficient
    polort: (an integer)
            Remove polynomical trend of order m

Outputs:

    out_file: (an existing file name)
            output file

## TShift

Link to code

Wraps command 3dTshift

Shifts voxel time series from input so that seperate slices are aligned to the same temporal origin

For complete details, see the `3dTshift Documentation. <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTshift.html>

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> tshift = afni.TShift()
    >>> tshift.inputs.in_file = 'functional.nii'
    >>> tshift.inputs.tpattern = 'alt+z'
    >>> tshift.inputs.tzero = 0.0
    >>> tshift.cmdline
    '3dTshift -prefix functional_tshift+orig.BRIK -tpattern alt+z -tzero 0.0 functional.nii'
    >>> res = tshift.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dTShift
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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: (an integer)
            ignore the first set of points specified
    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: ('Fourier' or 'linear' or 'cubic' or 'quintic' or 'heptic')
            different interpolation methods (see 3dTShift for details) default = Fourier
    out_file: (a file name, nipype default value: %s_tshift)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    rlt: (a boolean)
            Before shifting, remove the mean and linear trend
    rltplus: (a boolean)
            Before shifting, remove the mean and linear trend and later put back the mean
    suffix: (a string)
            output image suffix
    tpattern: ('alt+z' or 'alt+z2' or 'alt-z' or 'alt-z2' or 'seq+z' or 'seq-z')
            use specified slice time pattern rather than one in header
    tr: (a string)
            manually set the TRYou can attach suffix "s" for seconds or "ms" for milliseconds.
    tslice: (an integer)
            align each slice to time offset of given slice
            mutually_exclusive: tzero
    tzero: (a float)
            align each slice to given time offset
            mutually_exclusive: tslice

Outputs:

    out_file: (an existing file name)
            output file

## TStat

Link to code

Wraps command 3dTstat

Compute voxel-wise statistics using AFNI 3dTstat command

For complete details, see the 3dTstat Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> tstat = afni.TStat()
    >>> tstat.inputs.in_file = 'functional.nii'
    >>> tstat.inputs.args= '-mean'
    >>> res = tstat.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dTstat
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    mask: (an existing file name)
            mask file
    options: (a string)
            selected statistical output
    out_file: (a file name, nipype default value: %s_tstat)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## To3D

Link to code

Wraps command to3d

Create a 3D dataset from 2D image files using AFNI to3d command

For complete details, see the to3d Documentation

### Examples

    >>> from nipype.interfaces import afni
    >>> To3D = afni.To3D()
    >>> To3D.inputs.datatype = 'float'
    >>> To3D.inputs.infolder = 'dicomdir'
    >>> To3D.inputs.filetype = "anat"
    >>> To3D.inputs.outputtype = "NIFTI"
    >>> To3D.cmdline
    'to3d -datum float -anat -prefix dicomdir.nii dicomdir/*.dcm'
    >>> res = To3D.run()

Inputs:

    [Mandatory]
    in_folder: (an existing directory name)
            folder with DICOM images to convert
            mutually_exclusive: infolder, in_folder
    infolder: (an existing directory name)
            folder with DICOM images to convert
            mutually_exclusive: infolder, in_folder
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    assumemosaic: (a boolean)
            assume that Siemens image is mosaic
    datatype: ('short' or 'float' or 'byte' or 'complex')
            set output file datatype
    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
    filetype: ('spgr' or 'fse' or 'epan' or 'anat' or 'ct' or 'spct' or 'pet' or 'mra' or
             'bmap' or 'diff' or 'omri' or 'abuc' or 'fim' or 'fith' or 'fico' or 'fitt' or 'fift'
             or 'fizt' or 'fict' or 'fibt' or 'fibn' or 'figt' or 'fipt' or 'fbuc')
            type of datafile being converted
    funcparams: (a string)
            parameters for functional data
    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, nipype default value: %s)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    skipoutliers: (a boolean)
            skip the outliers check
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file

## Volreg

Link to code

Wraps command 3dvolreg

Register input volumes to a base volume using AFNI 3dvolreg command

For complete details, see the 3dvolreg Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> volreg = afni.Volreg()
    >>> volreg.inputs.in_file = 'functional.nii'
    >>> volreg.inputs.args = '-Fourier -twopass'
    >>> volreg.inputs.zpad = 4
    >>> volreg.inputs.outputtype = "NIFTI"
    >>> volreg.cmdline
    '3dvolreg -Fourier -twopass -1Dfile functional.1D -prefix functional_volreg.nii -zpad 4 functional.nii'
    >>> res = volreg.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dvolreg
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    basefile: (an existing file name)
            base file for registration
    copyorigin: (a boolean)
            copy base file origin coords to output
    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
    md1dfile: (a file name)
            max displacement output file
    oned_file: (a file name, nipype default value: %s.1D)
            1D movement parameters output file
    out_file: (a file name, nipype default value: %s_volreg)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix
    timeshift: (a boolean)
            time shift to mean slice time offset
    verbose: (a boolean)
            more detailed description of the process
    zpad: (an integer)
            Zeropad around the edges by 'n' voxels during rotations

Outputs:

    md1d_file: (an existing file name)
            max displacement info file
    oned_file: (an existing file name)
            movement parameters info file
    out_file: (an existing file name)
            registered file

## Warp

Link to code

Wraps command 3dWarp

Use 3dWarp for spatially transforming a dataset

For complete details, see the 3dWarp Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> warp = afni.Warp()
    >>> warp.inputs.in_file = 'structural.nii'
    >>> warp.inputs.deoblique = True
    >>> res = warp.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dWarp
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    deoblique: (a boolean)
            transform dataset from oblique to cardinal
    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
    gridset: (an existing file name)
            copy grid of specified dataset
    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: ('linear' or 'cubic' or 'NN' or 'quintic')
            spatial interpolation methods [default = linear]
    matparent: (an existing file name)
            apply transformation from 3dWarpDrive
    mni2tta: (a boolean)
            transform dataset from MNI152 to Talaraich
    out_file: (a file name, nipype default value: %s_warp)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string, nipype default value: _warp)
            out_file suffix
    tta2mni: (a boolean)
            transform dataset from Talairach to MNI152
    zpad: (an integer)
            pad input dataset with N planes of zero on all sides.

Outputs:

    out_file: (an existing file name)
            output file

## ZCutUp

Link to code

Wraps command 3dZcutup

Cut z-slices from a volume using AFNI 3dZcutup command

For complete details, see the 3dZcutup Documentation.

### Examples

    >>> from nipype.interfaces import afni as afni
    >>> zcutup = afni.ZCutUp()
    >>> zcutup.inputs.in_file = 'functional.nii'
    >>> zcutup.inputs.out_file = 'functional_zcutup.nii'
    >>> zcutup.inputs.keep= '0 10'
    >>> res = zcutup.run()

Inputs:

    [Mandatory]
    in_file: (an existing file name)
            input file to 3dZcutup
    terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
            Control terminal output

    [Optional]
    args: (a string)
            Additional parameters to the command
    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
    keep: (a string)
            slice range to keep in output
    out_file: (a file name, nipype default value: %s_zcupup)
            output image file name
    outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
            AFNI output filetype
    prefix: (a string)
            output image prefix
    suffix: (a string)
            output image suffix

Outputs:

    out_file: (an existing file name)
            output file