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.in_matrix= 'cmatrix.mat'
>>> res = allineate.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dAllineate
        flag: -source %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
autobox: (a boolean)
        Expand the -automask function to enclose a rectangular
         box that holds the irregular mask.
        flag: -autobox
automask: (an integer (int or long))
        Compute a mask function, set a value for dilation or 0.
        flag: -automask+%d
autoweight: (a string)
        Compute a weight function using the 3dAutomask
         algorithm plus some blurring of the base image.
        flag: -autoweight%s
center_of_mass: (a string)
        Use the center-of-mass calculation to bracket the shifts.
        flag: -cmass%s
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.
        flag: -check %s
convergence: (a float)
        Convergence test in millimeters (default 0.05mm).
        flag: -conv %f
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
        flag: -cost %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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.
        flag: -EPI
final_interpolation: ('nearestneighbour' or 'linear' or 'cubic' or
         'quintic' or 'wsinc5')
        Defines interpolation method used to create the output dataset
        flag: -final %s
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]
        flag: -fineblur %f
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
        flag: -1Dmatrix_apply %s, position: -3
in_param_file: (an existing file name)
        Read warp parameters from file and apply them to
         the source dataset, and produce a new dataset
        flag: -1Dparam_apply %s
interpolation: ('nearestneighbour' or 'linear' or 'cubic' or
         'quintic')
        Defines interpolation method to use during matching
        flag: -interp %s
master: (an existing file name)
        Write the output dataset on the same grid as this file
        flag: -master %s
newgrid: (a float)
        Write the output dataset using isotropic grid spacing in mm
        flag: -newgrid %f
nmatch: (an integer (int or long))
        Use at most n scattered points to match the datasets.
        flag: -nmatch %d
no_pad: (a boolean)
        Do not use zero-padding on the base image.
        flag: -nopad
nomask: (a boolean)
        Don't compute the autoweight/mask; if -weight is not
         also used, then every voxel will be counted equally.
        flag: -nomask
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.
        flag: -nwarp %s
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.
        flag: -nwarp_fixdep%s
nwarp_fixmot: (a list of items which are 'X' or 'Y' or 'Z' or 'I' or
         'J' or 'K')
        To fix motion along directions.
        flag: -nwarp_fixmot%s
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.
        flag: -onepass
out_file: (a file name)
        output file from 3dAllineate
        flag: -prefix %s, position: -2
out_matrix: (a file name)
        Save the transformation matrix for each volume.
        flag: -1Dmatrix_save %s
out_param_file: (a file name)
        Save the warp parameters in ASCII (.1D) format.
        flag: -1Dparam_save %s
out_weight_file: (a file name)
        Write the weight volume to disk as a dataset
        flag: -wtprefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
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.
        flag: -base %s
replacebase: (a boolean)
        If the source has more than one volume, then after the first
         volume is aligned to the base
        flag: -replacebase
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'.
        flag: -replacemeth %s
source_automask: (an integer (int or long))
        Automatically mask the source dataset with dilation or 0.
        flag: -source_automask+%d
source_mask: (an existing file name)
        mask the input dataset
        flag: -source_mask %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
two_best: (an integer (int or long))
        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]
        flag: -twobest %d
two_blur: (a float)
        Set the blurring radius for the first pass in mm.
        flag: -twoblur
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.
        flag: -twofirst
two_pass: (a boolean)
        Use a two pass alignment strategy for all volumes, searching
         for a large rotation+shift and then refining the alignment.
        flag: -twopass
usetemp: (a boolean)
        temporary file use
        flag: -usetemp
warp_type: ('shift_only' or 'shift_rotate' or 'shift_rotate_scale' or
         'affine_general')
        Set the warp type.
        flag: -warp %s
warpfreeze: (a boolean)
        Freeze the non-rigid body parameters after first volume.
        flag: -warpfreeze
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
        flag: -weight %s
zclip: (a boolean)
        Replace negative values in the input datasets (source & base) with
        zero.
        flag: -zclip

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.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 functional_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
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
eta2: (a boolean)
        eta^2 similarity
        flag: -eta2
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
        flag: -mask %s
mask_only_targets: (a boolean)
        use mask only on targets voxels
        flag: -mask_only_targets
        mutually_exclusive: mask_source
mask_source: (an existing file name)
        mask for source voxels
        flag: -mask_source %s
        mutually_exclusive: mask_only_targets
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
polort: (an integer (int or long))
        Remove polynomical trend of order m or -1 for no detrending
        flag: -polort %d
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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
        flag: -input %s

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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.
        flag: -noclust
out_file: (a file name)
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
padding: (an integer (int or long))
        Number of extra voxels to pad on each side of box
        flag: -npad %d
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

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

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
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
brain_file: (a file name)
        output file from 3dAutomask
        flag: -apply_prefix %s
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].
        flag: -clfrac %s
dilate: (an integer (int or long))
        dilate the mask outwards
        flag: -dilate %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
erode: (an integer (int or long))
        erode the mask inwards
        flag: -erode %s
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

brain_file: (an existing 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
        flag: %f, position: -3
in_file: (an existing file name)
        input file to 3dBandpass
        flag: %s, position: -1
lowpass: (a float)
        lowpass
        flag: %f, position: -2

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
automask: (a boolean)
        Create a mask from the input dataset
        flag: -automask
blur: (a float)
        Blur (inside the mask only) with a filter
         width (FWHM) of 'fff' millimeters.
        flag: -blur %f
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.
        flag: -despike
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.
        flag: -localPV %f
mask: (an existing file name)
        mask file
        flag: -mask %s, position: 2
nfft: (an integer (int or long))
        set the FFT length [must be a legal value]
        flag: -nfft %d
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.
        flag: -nodetrend
normalize: (a boolean)
        Make all output time series have L2 norm = 1
         ++ i.e., sum of squares = 1
        flag: -norm
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.
        flag: -notrans
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.
        flag: -dsort %s
orthogonalize_file: (a list of items which are an existing file name)
        Also orthogonalize input to columns in f.1D
         ++ Multiple '-ort' options are allowed.
        flag: -ort %s
out_file: (a file name)
        output file from 3dBandpass
        flag: -prefix %s, position: 1
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
tr: (a float)
        set time step (TR) in sec [default=from dataset header]
        flag: -dt %f

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
>>> bim.cmdline 
'3dBlurInMask -input functional.nii -FWHM 5.000000 -mask mask.nii -prefix functional_blur'
>>> res = bim.run()   

Inputs:

[Mandatory]
fwhm: (a float)
        fwhm kernel size
        flag: -FWHM %f
in_file: (an existing file name)
        input file to 3dSkullStrip
        flag: -input %s, position: 1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
automask: (a boolean)
        Create an automask from the input dataset.
        flag: -automask
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.
        flag: -float
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.
        flag: -mask %s
multimask: (a file name)
        Multi-mask dataset -- each distinct nonzero value in dataset will be
        treated as a separate mask for blurring purposes.
        flag: -Mmask %s
options: (a string)
        options
        flag: %s, position: 2
out_file: (a file name)
        output to the file
        flag: -prefix %s, position: -1
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
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.
        flag: -preserve
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

BlurToFWHM

Link to code

Wraps command 3dBlurToFWHM

Blurs a ‘master’ dataset until it reaches a specified FWHM smoothness (approximately).

For complete details, see the to3d Documentation

Examples

>>> from nipype.interfaces import afni
>>> blur = afni.preprocess.BlurToFWHM()
>>> blur.inputs.in_file = 'epi.nii'
>>> blur.inputs.fwhm = 2.5
>>> blur.cmdline 
'3dBlurToFWHM -FWHM 2.500000 -input epi.nii -prefix epi_afni'

Inputs:

[Mandatory]
in_file: (an existing file name)
        The dataset that will be smoothed
        flag: -input %s

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
automask: (a boolean)
        Create an automask from the input dataset.
        flag: -automask
blurmaster: (an existing file name)
        The dataset whose smoothness controls the process.
        flag: -blurmaster %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
fwhm: (a float)
        Blur until the 3D FWHM reaches this value (in mm)
        flag: -FWHM %f
fwhmxy: (a float)
        Blur until the 2D (x,y)-plane FWHM reaches this value (in mm)
        flag: -FWHMxy %f
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 dataset, if desired. Voxels NOT in mask will be set to zero in
        output.
        flag: -blurmaster %s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag: -mask %s, position: 2
min: (a boolean)
        print the minimum value in dataset
        flag: -min, position: 1
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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
        flag: -expr "%s", position: 3
in_file_a: (an existing file name)
        input file to 3dcalc
        flag: -a %s, position: 0

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag:  -b %s, position: 1
in_file_c: (an existing file name)
        operand file to 3dcalc
        flag:  -c %s, position: 2
other: (a file name)
        other options
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
single_idx: (an integer (int or long))
        volume index for in_file_a
start_idx: (an integer (int or long))
        start index for in_file_a
        requires: stop_idx
stop_idx: (an integer (int or long))
        stop index for in_file_a
        requires: start_idx
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

ClipLevel

Link to code

Wraps command 3dClipLevel

Estimates the value at which to clip the anatomical dataset so

that background regions are set to zero.

For complete details, see the 3dClipLevel Documentation.

Examples

>>> from nipype.interfaces.afni import preprocess
>>> cliplevel = preprocess.ClipLevel()
>>> cliplevel.inputs.in_file = 'anatomical.nii'
>>> res = cliplevel.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dClipLevel
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
doall: (a boolean)
        Apply the algorithm to each sub-brick separately
        flag: -doall, position: 3
        mutually_exclusive: g, r, a, d
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
grad: (a file name)
        also compute a 'gradual' clip level as a function of voxel position,
        and output that to a dataset
        flag: -grad %s, position: 3
        mutually_exclusive: d, o, a, l, l
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
mfrac: (a float)
        Use the number ff instead of 0.50 in the algorithm
        flag: -mfrac %s, position: 2
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

clip_val: (a float)
        output

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
>>> copy3d = afni.Copy()
>>> copy3d.inputs.in_file = 'functional.nii'
>>> copy3d.cmdline
'3dcopy functional.nii functional_copy'

>>> from copy import deepcopy
>>> copy3d_2 = deepcopy(copy3d)
>>> copy3d_2.inputs.outputtype = 'NIFTI'
>>> copy3d_2.cmdline
'3dcopy functional.nii functional_copy.nii'

>>> copy3d_3 = deepcopy(copy3d)
>>> copy3d_3.inputs.outputtype = 'NIFTI_GZ'
>>> copy3d_3.cmdline
'3dcopy functional.nii functional_copy.nii.gz'

>>> copy3d_4 = deepcopy(copy3d)
>>> copy3d_4.inputs.out_file = 'new_func.nii'
>>> copy3d_4.cmdline
'3dcopy functional.nii new_func.nii'

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dcopy
        flag: %s, position: -2

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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 image file name
        flag: %s, position: -1
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

DegreeCentrality

Link to code

Wraps command 3dDegreeCentrality

Performs degree centrality on a dataset using a given maskfile via 3dDegreeCentrality

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

Examples

>>> from nipype.interfaces import afni as afni
>>> degree = afni.DegreeCentrality()
>>> degree.inputs.in_file = 'functional.nii'
>>> degree.inputs.mask = 'mask.nii'
>>> degree.inputs.sparsity = 1 # keep the top one percent of connections
>>> degree.inputs.out_file = 'out.nii'
>>> degree.cmdline
'3dDegreeCentrality -mask mask.nii -prefix out.nii -sparsity 1.000000 functional.nii'
>>> res = degree.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dDegreeCentrality
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
autoclip: (a boolean)
        Clip off low-intensity regions in the dataset
        flag: -autoclip
automask: (a boolean)
        Mask the dataset to target brain-only voxels
        flag: -automask
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 to mask input data
        flag: -mask %s
oned_file: (a string)
        output filepath to text dump of correlation matrix
        flag: -out1D %s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
polort: (an integer (int or long))
        flag: -polort %d
sparsity: (a float)
        only take the top percent of connections
        flag: -sparsity %f
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
thresh: (a float)
        threshold to exclude connections where corr <= thresh
        flag: -thresh %f

Outputs:

oned_file: (a file name)
        The text output of the similarity matrix computedafter thresholding
        with one-dimensional and ijk voxel indices, correlations, image
        extents, and affine matrix
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'
>>> despike.cmdline
'3dDespike -prefix functional_despike functional.nii'
>>> res = despike.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dDespike
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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 image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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'
>>> detrend.inputs.outputtype = "AFNI"
>>> detrend.cmdline
'3dDetrend -polort 2 -prefix functional_detrend functional.nii'
>>> res = detrend.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dDetrend
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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 image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

ECM

Link to code

Wraps command 3dECM

Performs degree centrality on a dataset using a given maskfile via the 3dLFCD command

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

Examples

>>> from nipype.interfaces import afni as afni
>>> ecm = afni.ECM()
>>> ecm.inputs.in_file = 'functional.nii'
>>> ecm.inputs.mask = 'mask.nii'
>>> ecm.inputs.sparsity = 0.1 # keep top 0.1% of connections
>>> ecm.inputs.out_file = 'out.nii'
>>> ecm.cmdline
'3dECM -mask mask.nii -prefix out.nii -sparsity 0.100000 functional.nii'
>>> res = ecm.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dECM
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
autoclip: (a boolean)
        Clip off low-intensity regions in the dataset
        flag: -autoclip
automask: (a boolean)
        Mask the dataset to target brain-only voxels
        flag: -automask
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
eps: (a float)
        sets the stopping criterion for the power iteration; l2|v_old -
        v_new| < eps*|v_old|; default = 0.001
        flag: -eps %f
fecm: (a boolean)
        Fast centrality method; substantial speed increase but cannot
        accomodate thresholding; automatically selected if -thresh or
        -sparsity are not set
        flag: -fecm
full: (a boolean)
        Full power method; enables thresholding; automatically selected if
        -thresh or -sparsity are set
        flag: -full
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 to mask input data
        flag: -mask %s
max_iter: (an integer (int or long))
        sets the maximum number of iterations to use in the power iteration;
        default = 1000
        flag: -max_iter %d
memory: (a float)
        Limit memory consumption on system by setting the amount of GB to
        limit the algorithm to; default = 2GB
        flag: -memory %f
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
polort: (an integer (int or long))
        flag: -polort %d
scale: (a float)
        scale correlation coefficients in similarity matrix to after
        shifting, x >= 0.0; default = 1.0 for -full, 0.5 for -fecm
        flag: -scale %f
shift: (a float)
        shift correlation coefficients in similarity matrix to enforce non-
        negativity, s >= 0.0; default = 0.0 for -full, 1.0 for -fecm
        flag: -shift %f
sparsity: (a float)
        only take the top percent of connections
        flag: -sparsity %f
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
thresh: (a float)
        threshold to exclude connections where corr <= thresh
        flag: -thresh %f

Outputs:

out_file: (an existing file name)
        output file

Eval

Link to code

Wraps command 1deval

Evaluates an expression that may include columns of data from one or more text files

see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/1deval.html>

Examples

>>> from nipype.interfaces import afni as afni
>>> eval = afni.Eval()
>>> eval.inputs.in_file_a = 'seed.1D'
>>> eval.inputs.in_file_b = 'resp.1D'
>>> eval.inputs.expr='a*b'
>>> eval.inputs.out1D = True
>>> eval.inputs.out_file =  'data_calc.1D'
>>> calc.cmdline 
'3deval -a timeseries1.1D  -b timeseries2.1D -expr "a*b" -1D -prefix data_calc.1D'

Inputs:

[Mandatory]
expr: (a string)
        expr
        flag: -expr "%s", position: 3
in_file_a: (an existing file name)
        input file to 1deval
        flag: -a %s, position: 0

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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 1deval
        flag:  -b %s, position: 1
in_file_c: (an existing file name)
        operand file to 1deval
        flag:  -c %s, position: 2
other: (a file name)
        other options
out1D: (a boolean)
        output in 1D
        flag: -1D
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
single_idx: (an integer (int or long))
        volume index for in_file_a
start_idx: (an integer (int or long))
        start index for in_file_a
        requires: stop_idx
stop_idx: (an integer (int or long))
        stop index for in_file_a
        requires: start_idx
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

FWHMx

Link to code

Wraps command 3dFWHMx

Unlike the older 3dFWHM, this program computes FWHMs for all sub-bricks in the input dataset, each one separately. The output for each one is written to the file specified by ‘-out’. The mean (arithmetic or geometric) of all the FWHMs along each axis is written to stdout. (A non-positive output value indicates something bad happened; e.g., FWHM in z is meaningless for a 2D dataset; the estimation method computed incoherent intermediate results.)

Examples

>>> from nipype.interfaces import afni as afp
>>> fwhm = afp.FWHMx()
>>> fwhm.inputs.in_file = 'functional.nii'
>>> fwhm.cmdline
'3dFWHMx -input functional.nii -out functional_subbricks.out > functional_fwhmx.out'

(Classic) METHOD:

• Calculate ratio of variance of first differences to data variance.
• Should be the same as 3dFWHM for a 1-brick dataset. (But the output format is simpler to use in a script.)

Note

IMPORTANT NOTE [AFNI > 16]

A completely new method for estimating and using noise smoothness values is now available in 3dFWHMx and 3dClustSim. This method is implemented in the ‘-acf’ options to both programs. ‘ACF’ stands for (spatial) AutoCorrelation Function, and it is estimated by calculating moments of differences out to a larger radius than before.

Notably, real FMRI data does not actually have a Gaussian-shaped ACF, so the estimated ACF is then fit (in 3dFWHMx) to a mixed model (Gaussian plus mono-exponential) of the form

where is the radius, and are the fitted parameters. The apparent FWHM from this model is usually somewhat larger in real data than the FWHM estimated from just the nearest-neighbor differences used in the ‘classic’ analysis.

The longer tails provided by the mono-exponential are also significant. 3dClustSim has also been modified to use the ACF model given above to generate noise random fields.

Note

TL;DR or summary

The take-awaymessage is that the ‘classic’ 3dFWHMx and 3dClustSim analysis, using a pure Gaussian ACF, is not very correct for FMRI data – I cannot speak for PET or MEG data.

Warning

Do NOT use 3dFWHMx on the statistical results (e.g., ‘-bucket’) from 3dDeconvolve or 3dREMLfit!!! The function of 3dFWHMx is to estimate the smoothness of the time series NOISE, not of the statistics. This proscription is especially true if you plan to use 3dClustSim next!!

Note

Recommendations

• For FMRI statistical purposes, you DO NOT want the FWHM to reflect the spatial structure of the underlying anatomy. Rather, you want the FWHM to reflect the spatial structure of the noise. This means that the input dataset should not have anatomical (spatial) structure.
• One good form of input is the output of ‘3dDeconvolve -errts’, which is the dataset of residuals left over after the GLM fitted signal model is subtracted out from each voxel’s time series.
• If you don’t want to go to that much trouble, use ‘-detrend’ to approximately subtract out the anatomical spatial structure, OR use the output of 3dDetrend for the same purpose.
• If you do not use ‘-detrend’, the program attempts to find non-zero spatial structure in the input, and will print a warning message if it is detected.

Note

Notes on -demend

• I recommend this option, and it is not the default only for historical compatibility reasons. It may become the default someday.
• It is already the default in program 3dBlurToFWHM. This is the same detrending as done in 3dDespike; using 2*q+3 basis functions for q > 0.
• If you don’t use ‘-detrend’, the program now [Aug 2010] checks if a large number of voxels are have significant nonzero means. If so, the program will print a warning message suggesting the use of ‘-detrend’, since inherent spatial structure in the image will bias the estimation of the FWHM of the image time series NOISE (which is usually the point of using 3dFWHMx).

Inputs:

[Mandatory]
in_file: (an existing file name)
        input dataset
        flag: -input %s

[Optional]
acf: (a boolean or a file name or a tuple of the form: (an existing
         file name, a float), nipype default value: False)
        computes the spatial autocorrelation
        flag: -acf
args: (a string)
        Additional parameters to the command
        flag: %s
arith: (a boolean)
        if in_file has more than one sub-brick, compute the final estimate
        asthe arithmetic mean of the individual sub-brick FWHM estimates
        flag: -arith
        mutually_exclusive: geom
automask: (a boolean, nipype default value: False)
        compute a mask from THIS dataset, a la 3dAutomask
        flag: -automask
combine: (a boolean)
        combine the final measurements along each axis
        flag: -combine
compat: (a boolean)
        be compatible with the older 3dFWHM
        flag: -compat
demed: (a boolean)
        If the input dataset has more than one sub-brick (e.g., has a time
        axis), then subtract the median of each voxel's time series before
        processing FWHM. This will tend to remove intrinsic spatial
        structure and leave behind the noise.
        flag: -demed
        mutually_exclusive: detrend
detrend: (a boolean or an integer (int or long), nipype default
         value: False)
        instead of demed (0th order detrending), detrend to the specified
        order. If order is not given, the program picks q=NT/30. -detrend
        disables -demed, and includes -unif.
        flag: -detrend
        mutually_exclusive: demed
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
geom: (a boolean)
        if in_file has more than one sub-brick, compute the final estimate
        asthe geometric mean of the individual sub-brick FWHM estimates
        flag: -geom
        mutually_exclusive: arith
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)
        use only voxels that are nonzero in mask
        flag: -mask %s
out_detrend: (a file name)
        Save the detrended file into a dataset
        flag: -detprefix %s
out_file: (a file name)
        output file
        flag: > %s, position: -1
out_subbricks: (a file name)
        output file listing the subbricks FWHM
        flag: -out %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
unif: (a boolean)
        If the input dataset has more than one sub-brick, then normalize
        each voxel's time series to have the same MAD before processing
        FWHM.
        flag: -unif

Outputs:

acf_param: (a tuple of the form: (a float, a float, a float) or a
         tuple of the form: (a float, a float, a float, a float))
        fitted ACF model parameters
fwhm: (a tuple of the form: (a float, a float, a float) or a tuple of
         the form: (a float, a float, a float, a float))
        FWHM along each axis
out_acf: (an existing file name)
        output acf file
out_detrend: (a file name)
        output file, detrended
out_file: (an existing file name)
        output file
out_subbricks: (an existing file name)
        output file (subbricks)

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
        flag: -ideal_file %s, position: 2
in_file: (an existing file name)
        input file to 3dfim+
        flag:  -input %s, position: 1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
fim_thr: (a float)
        fim internal mask threshold value
        flag: -fim_thr %f, position: 3
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
        flag: -out %s, position: 4
out_file: (a file name)
        output image file name
        flag: -bucket %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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
        flag: -highpass %f, position: 1
in_file: (an existing file name)
        input file to 3dFourier
        flag: %s, position: -1
lowpass: (a float)
        lowpass
        flag: -lowpass %f, position: 0

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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 image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

Hist

Link to code

Wraps command 3dHist

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

For complete details, see the 3dHist Documentation.

Examples

>>> from nipype.interfaces import afni as afni
>>> hist = afni.Hist()
>>> hist.inputs.in_file = 'functional.nii'
>>> hist.cmdline
'3dHist -input functional.nii -prefix functional_hist'
>>> res = hist.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dHist
        flag: -input %s, position: 1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
bin_width: (a float)
        bin width
        flag: -binwidth %f
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag: -mask %s
max_value: (a float)
        maximum intensity value
        flag: -max %f
min_value: (a float)
        minimum intensity value
        flag: -min %f
nbin: (an integer (int or long))
        number of bins
        flag: -nbin %d
out_file: (a file name)
        Write histogram to niml file with this prefix
        flag: -prefix %s
out_show: (a file name)
        output image file name
        flag: > %s, position: -1
showhist: (a boolean, nipype default value: False)
        write a text visual histogram
        flag: -showhist
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file
out_show: (a file name)
        output visual histogram

LFCD

Link to code

Wraps command 3dLFCD

Performs degree centrality on a dataset using a given maskfile via the 3dLFCD command

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

Examples

>>> from nipype.interfaces import afni as afni
>>> lfcd = afni.LFCD()
>>> lfcd.inputs.in_file = 'functional.nii'
>>> lfcd.inputs.mask = 'mask.nii'
>>> lfcd.inputs.thresh = 0.8 # keep all connections with corr >= 0.8
>>> lfcd.inputs.out_file = 'out.nii'
>>> lfcd.cmdline
'3dLFCD -mask mask.nii -prefix out.nii -thresh 0.800000 functional.nii'
>>> res = lfcd.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dLFCD
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
autoclip: (a boolean)
        Clip off low-intensity regions in the dataset
        flag: -autoclip
automask: (a boolean)
        Mask the dataset to target brain-only voxels
        flag: -automask
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 to mask input data
        flag: -mask %s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
polort: (an integer (int or long))
        flag: -polort %d
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
thresh: (a float)
        threshold to exclude connections where corr <= thresh
        flag: -thresh %f

Outputs:

out_file: (an existing file name)
        output file

MaskTool

Link to code

Wraps command 3dmask_tool

3dmask_tool - for combining/dilating/eroding/filling masks

For complete details, see the 3dmask_tool 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 or files to 3dmask_tool
        flag: -input %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
count: (a boolean)
        Instead of created a binary 0/1 mask dataset, create one with.
        counts of voxel overlap, i.e each voxel will contain the number of
        masks that it is set in.
        flag: -count, position: 2
datum: ('byte' or 'short' or 'float')
        specify data type for output. Valid types are 'byte', 'short' and
        'float'.
        flag: -datum %s
dilate_inputs: (a string)
        Use this option to dilate and/or erode datasets as they are read.
        ex. '5 -5' to dilate and erode 5 times
        flag: -dilate_inputs %s
dilate_results: (a string)
        dilate and/or erode combined mask at the given levels.
        flag: -dilate_results %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
fill_dirs: (a string)
        fill holes only in the given directions. This option is for use with
        -fill holes. should be a single string that specifies 1-3 of the
        axes using {x,y,z} labels (i.e. dataset axis order), or using the
        labels in {R,L,A,P,I,S}.
        flag: -fill_dirs %s
        requires: fill_holes
fill_holes: (a boolean)
        This option can be used to fill holes in the resulting mask, i.e.
        after all other processing has been done.
        flag: -fill_holes
frac: (a float)
        When combining masks (across datasets and sub-bricks), use this
        option to restrict the result to a certain fraction of the set of
        volumes
        flag: -frac %s
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
inter: (a boolean)
        intersection, this means -frac 1.0
        flag: -inter
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
union: (a boolean)
        union, this means -frac 0
        flag: -union

Outputs:

out_file: (an existing file name)
        mask 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
        flag: %s, position: -2

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag: -mask %s, position: 1
out_file: (a file name)
        output image file name
        flag: > %s, position: -1
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
quiet: (a boolean)
        matrix to align input file
        flag: -quiet, position: 2
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

Means

Link to code

Wraps command 3dMean

Takes the voxel-by-voxel mean of all input datasets using 3dMean

see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dMean.html>

Examples

>>> from nipype.interfaces import afni as afni
>>> means = afni.Means()
>>> means.inputs.in_file_a = 'im1.nii'
>>> means.inputs.in_file_b = 'im2.nii'
>>> means.inputs.out_file =  'output.nii'
>>> means.cmdline
'3dMean im1.nii im2.nii -prefix output.nii'

Inputs:

[Mandatory]
in_file_a: (an existing file name)
        input file to 3dMean
        flag: %s, position: 0

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
count: (a boolean)
        compute count of non-zero voxels
        flag: -count
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)
        another input file to 3dMean
        flag: %s, position: 1
mask_inter: (a boolean)
        create intersection mask
        flag: -mask_inter
mask_union: (a boolean)
        create union mask
        flag: -mask_union
non_zero: (a boolean)
        use only non-zero values
        flag: -non_zero
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
scale: (a string)
        scaling of output
        flag: -%sscale
sqr: (a boolean)
        mean square instead of value
        flag: -sqr
std_dev: (a boolean)
        calculate std dev
        flag: -stdev
summ: (a boolean)
        take sum, (not average)
        flag: -sum
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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: (a list of items which are an existing file name)
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
blurfwhm: (an integer (int or long))
        FWHM blur value (mm)
        flag: -1blur_fwhm %d
doall: (a boolean)
        apply options to all sub-bricks in dataset
        flag: -doall
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 image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

OutlierCount

Link to code

Wraps command 3dToutcount

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
>>> toutcount = afni.OutlierCount()
>>> toutcount.inputs.in_file = 'functional.nii'
>>> toutcount.cmdline 
'3dToutcount functional.nii > functional_outliers'
>>> res = toutcount.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input dataset
        flag: %s, position: -2

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
autoclip: (a boolean, nipype default value: False)
        clip off small voxels
        flag: -autoclip
        mutually_exclusive: in_file
automask: (a boolean, nipype default value: False)
        clip off small voxels
        flag: -automask
        mutually_exclusive: in_file
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
fraction: (a boolean, nipype default value: False)
        write out the fraction of masked voxels which are outliers at each
        timepoint
        flag: -fraction
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
interval: (a boolean, nipype default value: False)
        write out the median + 3.5 MAD of outlier count with each timepoint
        flag: -range
legendre: (a boolean, nipype default value: False)
        use Legendre polynomials
        flag: -legendre
mask: (an existing file name)
        only count voxels within the given mask
        flag: -mask %s
        mutually_exclusive: autoclip, automask
out_file: (a file name)
        capture standard output
        flag: > %s, position: -1
outliers_file: (a file name)
        output image file name
        flag: -save %s
polort: (an integer (int or long))
        detrend each voxel timeseries with polynomials
        flag: -polort %d
qthr: (0.0 <= a floating point number <= 1.0)
        indicate a value for q to compute alpha
        flag: -qthr %.5f
save_outliers: (a boolean, nipype default value: False)
        enables out_file option
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (a file name)
        capture standard output
        flag: > %s, position: -1
out_outliers: (an existing file name)
        output image file name

QualityIndex

Link to code

Wraps command 3dTqual

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
>>> tqual = afni.QualityIndex()
>>> tqual.inputs.in_file = 'functional.nii'
>>> tqual.cmdline 
'3dTqual functional.nii > functional_tqual'
>>> res = tqual.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input dataset
        flag: %s, position: -2

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
autoclip: (a boolean, nipype default value: False)
        clip off small voxels
        flag: -autoclip
        mutually_exclusive: mask
automask: (a boolean, nipype default value: False)
        clip off small voxels
        flag: -automask
        mutually_exclusive: mask
clip: (a float)
        clip off values below
        flag: -clip %f
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
interval: (a boolean, nipype default value: False)
        write out the median + 3.5 MAD of outlier count with each timepoint
        flag: -range
mask: (an existing file name)
        compute correlation only across masked voxels
        flag: -mask %s
        mutually_exclusive: autoclip, automask
out_file: (a file name)
        capture standard output
        flag: > %s, position: -1
quadrant: (a boolean, nipype default value: False)
        Similar to -spearman, but using 1 minus the quadrant correlation
        coefficient as the quality index.
        flag: -quadrant
spearman: (a boolean, nipype default value: False)
        Quality index is 1 minus the Spearman (rank) correlation coefficient
        of each sub-brick with the median sub-brick. (default)
        flag: -spearman
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (a file name)
        file containing the caputured standard output

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
        flag: %s, position: -1
terminal_output: ('allatonce', nipype default value: allatonce)
        Control terminal output:`allatonce` - waits till command is finished
        to display output

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag: -mask %s, position: 3
mask_f2short: (a boolean)
        Tells the program to convert a float mask to short integers, by
        simple rounding.
        flag: -mask_f2short, position: 2
quiet: (a boolean)
        execute quietly
        flag: -quiet, position: 1

Outputs:

stats: (an existing file name)
        output tab separated values file

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
>>> refit.cmdline
'3drefit -deoblique structural.nii'
>>> res = refit.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3drefit
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
deoblique: (a boolean)
        replace current transformation matrix with cardinal matrix
        flag: -deoblique
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
space: ('TLRC' or 'MNI' or 'ORIG')
        Associates the dataset with a specific template type, e.g. TLRC,
        MNI, ORIG
        flag: -space %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
xdel: (a float)
        new x voxel dimension in mm
        flag: -xdel %f
xorigin: (a string)
        x distance for edge voxel offset
        flag: -xorigin %s
ydel: (a float)
        new y voxel dimension in mm
        flag: -ydel %f
yorigin: (a string)
        y distance for edge voxel offset
        flag: -yorigin %s
zdel: (a float)
        new z voxel dimension in mm
        flag: -zdel %f
zorigin: (a string)
        z distance for edge voxel offset
        flag: -zorigin %s

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'
>>> resample.inputs.outputtype = "NIFTI"
>>> resample.cmdline
'3dresample -orient RPI -prefix functional_resample.nii -inset functional.nii'
>>> res = resample.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dresample
        flag: -inset %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag: -master %s
orientation: (a string)
        new orientation code
        flag: -orient %s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
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.
        flag: -rmode %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
voxel_size: (a tuple of the form: (a float, a float, a float))
        resample to new dx, dy and dz
        flag: -dxyz %f %f %f

Outputs:

out_file: (an existing file name)
        output file

Retroicor

Link to code

Wraps command 3dretroicor

Performs Retrospective Image Correction for physiological motion effects, using a slightly modified version of the RETROICOR algorithm

The durations of the physiological inputs are assumed to equal the duration of the dataset. Any constant sampling rate may be used, but 40 Hz seems to be acceptable. This program’s cardiac peak detection algorithm is rather simplistic, so you might try using the scanner’s cardiac gating output (transform it to a spike wave if necessary).

This program uses slice timing information embedded in the dataset to estimate the proper cardiac/respiratory phase for each slice. It makes sense to run this program before any program that may destroy the slice timings (e.g. 3dvolreg for motion correction).

For complete details, see the 3dretroicor Documentation.

Examples

>>> from nipype.interfaces import afni as afni
>>> ret = afni.Retroicor()
>>> ret.inputs.in_file = 'functional.nii'
>>> ret.inputs.card = 'mask.1D'
>>> ret.inputs.resp = 'resp.1D'
>>> res = ret.run()   

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dretroicor
        flag: %s, position: -1
out_file: (a file name)
        output image file name
        flag: -prefix %s, position: 1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
card: (an existing file name)
        1D cardiac data file for cardiac correction
        flag: -card %s, position: -2
cardphase: (a file name)
        Filename for 1D cardiac phase output
        flag: -cardphase %s, position: -6
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
order: (an integer (int or long))
        The order of the correction (2 is typical)
        flag: -order %s, position: -5
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
resp: (an existing file name)
        1D respiratory waveform data for correction
        flag: -resp %s, position: -3
respphase: (a file name)
        Filename for 1D resp phase output
        flag: -respphase %s, position: -7
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
threshold: (an integer (int or long))
        Threshold for detection of R-wave peaks in input (Make sure it is
        above the background noise level, Try 3/4 or 4/5 times range plus
        minimum)
        flag: -threshold %d, position: -4

Outputs:

out_file: (an existing file name)
        output file

Seg

Link to code

Wraps command 3dSeg

3dSeg segments brain volumes into tissue classes. The program allows

for adding a variety of global and voxelwise priors. However for the moment, only mixing fractions and MRF are documented.

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

Examples

>>> from nipype.interfaces.afni import preprocess
>>> seg = preprocess.Seg()
>>> seg.inputs.in_file = 'structural.nii'
>>> seg.inputs.mask = 'AUTO'
>>> res = seg.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        ANAT is the volume to segment
        flag: -anat %s, position: -1
mask: ('AUTO' or an existing file name)
        only non-zero voxels in mask are analyzed. mask can either be a
        dataset or the string "AUTO" which would use AFNI's automask
        function to create the mask.
        flag: -mask %s, position: -2

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
bias_classes: (a string)
        A semcolon demlimited string of classes that contribute to the
        estimation of the bias field
        flag: -bias_classes %s
bias_fwhm: (a float)
        The amount of blurring used when estimating the field bias with the
        Wells method
        flag: -bias_fwhm %f
blur_meth: ('BFT' or 'BIM')
        set the blurring method for bias field estimation
        flag: -blur_meth %s
bmrf: (a float)
        Weighting factor controlling spatial homogeneity of the
        classifications
        flag: -bmrf %f
classes: (a string)
        CLASS_STRING is a semicolon delimited string of class labels
        flag: -classes %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
main_N: (an integer (int or long))
        Number of iterations to perform.
        flag: -main_N %d
mixfloor: (a float)
        Set the minimum value for any class's mixing fraction
        flag: -mixfloor %f
mixfrac: (a string)
        MIXFRAC sets up the volume-wide (within mask) tissue fractions while
        initializing the segmentation (see IGNORE for exception)
        flag: -mixfrac %s
prefix: (a string)
        the prefix for the output folder containing all output volumes
        flag: -prefix %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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
        flag: -input %s, position: 1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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 image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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: (a list of items which are an existing file name)
        input file to 3dTcat
        flag:  %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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 image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
rlt: (a string)
        options
        flag: -rlt%s, position: 1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file

TCorr1D

Link to code

Wraps command 3dTcorr1D

Computes the correlation coefficient between each voxel time series in the input 3D+time dataset. For complete details, see the 3dTcorr1D Documentation.

>>> from nipype.interfaces import afni as afni
>>> tcorr1D = afni.TCorr1D()
>>> tcorr1D.inputs.xset= 'u_rc1s1_Template.nii'
>>> tcorr1D.inputs.y_1d = 'seed.1D'
>>> tcorr1D.cmdline
'3dTcorr1D -prefix u_rc1s1_Template_correlation.nii.gz  u_rc1s1_Template.nii  seed.1D'
>>> res = tcorr1D.run() 

Inputs:

[Mandatory]
xset: (an existing file name)
        3d+time dataset input
        flag:  %s, position: -2
y_1d: (an existing file name)
        1D time series file input
        flag:  %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
ktaub: (a boolean)
        Correlation is the Kendall's tau_b correlation coefficient
        flag:  -ktaub, position: 1
        mutually_exclusive: pearson, spearman, quadrant
out_file: (a file name)
        output filename prefix
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
pearson: (a boolean)
        Correlation is the normal Pearson correlation coefficient
        flag:  -pearson, position: 1
        mutually_exclusive: spearman, quadrant, ktaub
quadrant: (a boolean)
        Correlation is the quadrant correlation coefficient
        flag:  -quadrant, position: 1
        mutually_exclusive: pearson, spearman, ktaub
spearman: (a boolean)
        Correlation is the Spearman (rank) correlation coefficient
        flag:  -spearman, position: 1
        mutually_exclusive: pearson, quadrant, ktaub
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file containing correlations

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)
        flag: -input %s

[Optional]
absolute_threshold: (a file name)
        flag: -Thresh %f %s
        mutually_exclusive: absolute_threshold, var_absolute_threshold,
         var_absolute_threshold_normalize
args: (a string)
        Additional parameters to the command
        flag: %s
automask: (a boolean)
        flag: -automask
average_expr: (a file name)
        flag: -Aexpr %s %s
        mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
average_expr_nonzero: (a file name)
        flag: -Cexpr %s %s
        mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
bandpass: (a tuple of the form: (a float, a float))
        flag: -bpass %f %f
blur_fwhm: (a float)
        flag: -Gblur %f
correlation_maps: (a file name)
        flag: -CorrMap %s
correlation_maps_masked: (a file name)
        flag: -CorrMask %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
expr: (a string)
histogram: (a file name)
        flag: -Hist %d %s
histogram_bin_numbers: (an integer (int or long))
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)
        flag: -mask %s
mean_file: (a file name)
        flag: -Mean %s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
pmean: (a file name)
        flag: -Pmean %s
polort: (an integer (int or long))
        flag: -polort %d
qmean: (a file name)
        flag: -Qmean %s
regress_out_timeseries: (a file name)
        flag: -ort %s
seeds: (an existing file name)
        flag: -seed %s
        mutually_exclusive: s, e, e, d, s, _, w, i, d, t, h
seeds_width: (a float)
        flag: -Mseed %f
        mutually_exclusive: s, e, e, d, s
sum_expr: (a file name)
        flag: -Sexpr %s %s
        mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
thresholds: (a list of items which are an integer (int or long))
var_absolute_threshold: (a file name)
        flag: -VarThresh %f %f %f %s
        mutually_exclusive: absolute_threshold, var_absolute_threshold,
         var_absolute_threshold_normalize
var_absolute_threshold_normalize: (a file name)
        flag: -VarThreshN %f %f %f %s
        mutually_exclusive: absolute_threshold, var_absolute_threshold,
         var_absolute_threshold_normalize
zmean: (a file name)
        flag: -Zmean %s

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]
xset: (an existing file name)
        input xset
        flag:  %s, position: -2
yset: (an existing file name)
        input yset
        flag:  %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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 image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
pearson: (a boolean)
        Correlation is the normal Pearson correlation coefficient
        flag: -pearson, position: 1
polort: (an integer (int or long))
        Remove polynomical trend of order m
        flag: -polort %d, position: 2
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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 #doctest:
'3dTshift -prefix functional_tshift -tpattern alt+z -tzero 0.0 functional.nii'
>>> res = tshift.run()   

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dTShift
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
ignore: (an integer (int or long))
        ignore the first set of points specified
        flag: -ignore %s
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
interp: ('Fourier' or 'linear' or 'cubic' or 'quintic' or 'heptic')
        different interpolation methods (see 3dTShift for details) default =
        Fourier
        flag: -%s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
rlt: (a boolean)
        Before shifting, remove the mean and linear trend
        flag: -rlt
rltplus: (a boolean)
        Before shifting, remove the mean and linear trend and later put back
        the mean
        flag: -rlt+
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
tpattern: (a string)
        use specified slice time pattern rather than one in header
        flag: -tpattern %s
tr: (a string)
        manually set the TRYou can attach suffix "s" for seconds or "ms" for
        milliseconds.
        flag: -TR %s
tslice: (an integer (int or long))
        align each slice to time offset of given slice
        flag: -slice %s
        mutually_exclusive: tzero
tzero: (a float)
        align each slice to given time offset
        flag: -tzero %s
        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'
>>> tstat.inputs.out_file = "stats"
>>> tstat.cmdline
'3dTstat -mean -prefix stats functional.nii'
>>> res = tstat.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dTstat
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag: -mask %s
options: (a string)
        selected statistical output
        flag: %s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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.in_folder = '.'
>>> To3D.inputs.out_file = 'dicomdir.nii'
>>> To3D.inputs.filetype = "anat"
>>> To3D.cmdline 
'to3d -datum float -anat -prefix dicomdir.nii ./*.dcm'
>>> res = To3D.run() 

Inputs:

[Mandatory]
in_folder: (an existing directory name)
        folder with DICOM images to convert
        flag: %s/*.dcm, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
assumemosaic: (a boolean)
        assume that Siemens image is mosaic
        flag: -assume_dicom_mosaic
datatype: ('short' or 'float' or 'byte' or 'complex')
        set output file datatype
        flag: -datum %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag: -%s
funcparams: (a string)
        parameters for functional data
        flag: -time:zt %s alt+z2
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 image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
skipoutliers: (a boolean)
        skip the outliers check
        flag: -skip_outliers
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

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 -1Dmatrix_save functional.aff12.1D -prefix functional_volreg.nii -zpad 4 -maxdisp1D functional_md.1D functional.nii'
>>> res = volreg.run() 

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dvolreg
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
basefile: (an existing file name)
        base file for registration
        flag: -base %s, position: -6
copyorigin: (a boolean)
        copy base file origin coords to output
        flag: -twodup
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
md1d_file: (a file name)
        max displacement output file
        flag: -maxdisp1D %s, position: -4
oned_file: (a file name)
        1D movement parameters output file
        flag: -1Dfile %s
oned_matrix_save: (a file name)
        Save the matrix transformation
        flag: -1Dmatrix_save %s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
timeshift: (a boolean)
        time shift to mean slice time offset
        flag: -tshift 0
verbose: (a boolean)
        more detailed description of the process
        flag: -verbose
zpad: (an integer (int or long))
        Zeropad around the edges by 'n' voxels during rotations
        flag: -zpad %d, position: -5

Outputs:

md1d_file: (an existing file name)
        max displacement info file
oned_file: (an existing file name)
        movement parameters info file
oned_matrix_save: (an existing file name)
        matrix transformation from base to input
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
>>> warp.inputs.out_file = "trans.nii.gz"
>>> warp.cmdline
'3dWarp -deoblique -prefix trans.nii.gz structural.nii'

>>> warp_2 = afni.Warp()
>>> warp_2.inputs.in_file = 'structural.nii'
>>> warp_2.inputs.newgrid = 1.0
>>> warp_2.inputs.out_file = "trans.nii.gz"
>>> warp_2.cmdline
'3dWarp -newgrid 1.000000 -prefix trans.nii.gz structural.nii'

Inputs:

[Mandatory]
in_file: (an existing file name)
        input file to 3dWarp
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
deoblique: (a boolean)
        transform dataset from oblique to cardinal
        flag: -deoblique
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
        flag: -gridset %s
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the
        interface fails to run
interp: ('linear' or 'cubic' or 'NN' or 'quintic')
        spatial interpolation methods [default = linear]
        flag: -%s
matparent: (an existing file name)
        apply transformation from 3dWarpDrive
        flag: -matparent %s
mni2tta: (a boolean)
        transform dataset from MNI152 to Talaraich
        flag: -mni2tta
newgrid: (a float)
        specify grid of this size (mm)
        flag: -newgrid %f
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored
tta2mni: (a boolean)
        transform dataset from Talairach to MNI152
        flag: -tta2mni
zpad: (an integer (int or long))
        pad input dataset with N planes of zero on all sides.
        flag: -zpad %d

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
        flag: %s, position: -1

[Optional]
args: (a string)
        Additional parameters to the command
        flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
         with values which are a value of type 'str', nipype default value:
         {})
        Environment variables
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
        flag: -keep %s
out_file: (a file name)
        output image file name
        flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
        AFNI output filetype
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output: `stream` - displays to terminal immediately
        (default), `allatonce` - waits till command is finished to display
        output, `file` - writes output to file, `none` - output is ignored

Outputs:

out_file: (an existing file name)
        output file