Wraps command 3dAllineate
Program to align one dataset (the ‘source’) to a base dataset
For complete details, see the 3dAllineate Documentation.
>>> from nipype.interfaces import afni as afni
>>> allineate = afni.Allineate()
>>> allineate.inputs.in_file = 'functional.nii'
>>> allineate.inputs.out_file= 'functional_allineate.nii'
>>> allineate.inputs.matrix= 'cmatrix.mat'
>>> res = allineate.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dAllineate
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
autobox: (a boolean)
Expand the -automask function to enclose a rectangular
box that holds the irregular mask.
automask: (an integer)
Compute a mask function, set a value for dilation or 0.
autoweight: (a string)
Compute a weight function using the 3dAutomask
algorithm plus some blurring of the base image.
center_of_mass: (a string)
Use the center-of-mass calculation to bracket the shifts.
check: (a list of items which are 'leastsq' or 'ls' or 'mutualinfo' or 'mi' or
'corratio_mul' or 'crM' or 'norm_mutualinfo' or 'nmi' or 'hellinger' or 'hel' or
'corratio_add' or 'crA' or 'corratio_uns' or 'crU')
After cost functional optimization is done, start at the
final parameters and RE-optimize using this new cost functions.
If the results are too different, a warning message will be
printed. However, the final parameters from the original
optimization will be used to create the output dataset.
convergence: (a float)
Convergence test in millimeters (default 0.05mm).
cost: ('leastsq' or 'ls' or 'mutualinfo' or 'mi' or 'corratio_mul' or 'crM' or
'norm_mutualinfo' or 'nmi' or 'hellinger' or 'hel' or 'corratio_add' or 'crA' or
'corratio_uns' or 'crU')
Defines the 'cost' function that defines the matching
between the source and the base
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
epi: (a boolean)
Treat the source dataset as being composed of warped
EPI slices, and the base as comprising anatomically
'true' images. Only phase-encoding direction image
shearing and scaling will be allowed with this option.
final_interpolation: ('nearestneighbour' or 'linear' or 'cubic' or 'quintic' or 'wsinc5')
Defines interpolation method used to create the output dataset
fine_blur: (a float)
Set the blurring radius to use in the fine resolution
pass to 'x' mm. A small amount (1-2 mm?) of blurring at
the fine step may help with convergence, if there is
some problem, especially if the base volume is very noisy.
[Default == 0 mm = no blurring at the final alignment pass]
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
in_matrix: (a file name)
matrix to align input file
in_param_file: (an existing file name)
Read warp parameters from file and apply them to
the source dataset, and produce a new dataset
interpolation: ('nearestneighbour' or 'linear' or 'cubic' or 'quintic')
Defines interpolation method to use during matching
master: (an existing file name)
Write the output dataset on the same grid as this file
newgrid: (a float)
Write the output dataset using isotropic grid spacing in mm
nmatch: (an integer)
Use at most n scattered points to match the datasets.
no_pad: (a boolean)
Do not use zero-padding on the base image.
nomask: (a boolean)
Don't compute the autoweight/mask; if -weight is not
also used, then every voxel will be counted equally.
nwarp: ('bilinear' or 'cubic' or 'quintic' or 'heptic' or 'nonic' or 'poly3' or 'poly5'
or 'poly7' or 'poly9')
Experimental nonlinear warping: bilinear or legendre poly.
nwarp_fixdep: (a list of items which are 'X' or 'Y' or 'Z' or 'I' or 'J' or 'K')
To fix non-linear warp dependency along directions.
nwarp_fixmot: (a list of items which are 'X' or 'Y' or 'Z' or 'I' or 'J' or 'K')
To fix motion along directions.
one_pass: (a boolean)
Use only the refining pass -- do not try a coarse
resolution pass first. Useful if you know that only
small amounts of image alignment are needed.
out_file: (a file name)
output file from 3dAllineate
out_matrix: (a file name)
Save the transformation matrix for each volume.
out_param_file: (a file name)
Save the warp parameters in ASCII (.1D) format.
out_weight_file: (a file name)
Write the weight volume to disk as a dataset
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
reference: (an existing file name)
file to be used as reference, the first volume will be used
if not given the reference will be the first volume of in_file.
replacebase: (a boolean)
If the source has more than one volume, then after the first
volume is aligned to the base
replacemeth: ('leastsq' or 'ls' or 'mutualinfo' or 'mi' or 'corratio_mul' or 'crM' or
'norm_mutualinfo' or 'nmi' or 'hellinger' or 'hel' or 'corratio_add' or 'crA' or
'corratio_uns' or 'crU')
After first volume is aligned, switch method for later volumes.
For use with '-replacebase'.
source_automask: (an integer)
Automatically mask the source dataset with dilation or 0.
source_mask: (an existing file name)
mask the input dataset
suffix: (a string, nipype default value: _allineate)
out_file suffix
two_best: (an integer)
In the coarse pass, use the best 'bb' set of initial
points to search for the starting point for the fine
pass. If bb==0, then no search is made for the best
starting point, and the identity transformation is
used as the starting point. [Default=5; min=0 max=11]
two_blur: (a float)
Set the blurring radius for the first pass in mm.
two_first: (a boolean)
Use -twopass on the first image to be registered, and
then on all subsequent images from the source dataset,
use results from the first image's coarse pass to start
the fine pass.
two_pass: (a boolean)
Use a two pass alignment strategy for all volumes, searching
for a large rotation+shift and then refining the alignment.
usetemp: (a boolean)
temporary file use
warp_type: ('shift_only' or 'shift_rotate' or 'shift_rotate_scale' or 'affine_general')
Set the warp type.
warpfreeze: (a boolean)
Freeze the non-rigid body parameters after first volume.
weight_file: (an existing file name)
Set the weighting for each voxel in the base dataset;
larger weights mean that voxel count more in the cost function.
Must be defined on the same grid as the base dataset
zclip: (a boolean)
Replace negative values in the input datasets (source & base) with zero.
Outputs:
matrix: (a file name)
matrix to align input file
out_file: (a file name)
output image file name
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].
>>> from nipype.interfaces import afni as afni
>>> corr = afni.AutoTcorrelate()
>>> corr.inputs.in_file = 'functional.nii'
>>> corr.inputs.out_file = 'my_similarity_matrix.1D'
>>> corr.inputs.polort = -1
>>> corr.inputs.eta2 = True
>>> corr.inputs.mask = 'mask.nii'
>>> corr.inputs.mask_only_targets = True
>>> corr.cmdline
'3dAutoTcorrelate -eta2 -mask mask.nii -mask_only_targets -prefix ...my_similarity_matrix.1D -polort -1 functional.nii'
>>> res = corr.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
timeseries x space (volume or surface) file
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
eta2: (a boolean)
eta^2 similarity
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
mask of voxels
mask_only_targets: (a boolean)
use mask only on targets voxels
mutually_exclusive: mask_source
mask_source: (an existing file name)
mask for source voxels
mutually_exclusive: mask_only_targets
out_file: (a file name, nipype default value: %s_similarity_matrix.1D)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
polort: (an integer)
Remove polynomical trend of order m or -1 for no detrending
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
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>
>>> from nipype.interfaces import afni as afni
>>> abox = afni.Autobox()
>>> abox.inputs.in_file = 'structural.nii'
>>> abox.inputs.padding = 5
>>> res = abox.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
no_clustering: (a boolean)
Don't do any clustering to find box. Any non-zero
voxel will be preserved in the cropped volume.
The default method uses some clustering to find the
cropping box, and will clip off small isolated blobs.
out_file: (a file name)
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
padding: (an integer)
Number of extra voxels to pad on each side of box
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (a file name)
output file
x_max: (an integer)
x_min: (an integer)
y_max: (an integer)
y_min: (an integer)
z_max: (an integer)
z_min: (an integer)
Wraps command 3dAutomask
Create a brain-only mask of the image using AFNI 3dAutomask command
For complete details, see the 3dAutomask Documentation.
>>> from nipype.interfaces import afni as afni
>>> automask = afni.Automask()
>>> automask.inputs.in_file = 'functional.nii'
>>> automask.inputs.dilate = 1
>>> automask.inputs.outputtype = "NIFTI"
>>> automask.cmdline
'3dAutomask -apply_prefix functional_masked.nii -dilate 1 -prefix functional_mask.nii functional.nii'
>>> res = automask.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dAutomask
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
apply_mask: (a file name)
output file from 3dAutomask
apply_suffix: (a string)
out_file suffix
args: (a string)
Additional parameters to the command
brain_file: (a file name, nipype default value: %s_masked)
output file from 3dAutomask
clfrac: (a float)
sets the clip level fraction (must be 0.1-0.9). A small value will tend to make the mask
larger [default = 0.5].
dilate: (an integer)
dilate the mask outwards
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
erode: (an integer)
erode the mask inwards
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask_suffix: (a string)
out_file suffix
out_file: (a file name, nipype default value: %s_mask)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
brain_file: (a file name)
brain file (skull stripped)
out_file: (an existing file name)
mask file
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.
>>> from nipype.interfaces import afni as afni
>>> from nipype.testing import example_data
>>> bandpass = afni.Bandpass()
>>> bandpass.inputs.in_file = example_data('functional.nii')
>>> bandpass.inputs.highpass = 0.005
>>> bandpass.inputs.lowpass = 0.1
>>> res = bandpass.run()
Inputs:
[Mandatory]
highpass: (a float)
highpass
in_file: (an existing file name)
input file to 3dBandpass
lowpass: (a float)
lowpass
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
automask: (a boolean)
Create a mask from the input dataset
blur: (a float)
Blur (inside the mask only) with a filter
width (FWHM) of 'fff' millimeters.
despike: (a boolean)
Despike each time series before other processing.
++ Hopefully, you don't actually need to do this,
which is why it is optional.
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
localPV: (a float)
Replace each vector by the local Principal Vector
(AKA first singular vector) from a neighborhood
of radius 'rrr' millimiters.
++ Note that the PV time series is L2 normalized.
++ This option is mostly for Bob Cox to have fun with.
mask: (an existing file name)
mask file
nfft: (an integer)
set the FFT length [must be a legal value]
no_detrend: (a boolean)
Skip the quadratic detrending of the input that
occurs before the FFT-based bandpassing.
++ You would only want to do this if the dataset
had been detrended already in some other program.
normalize: (a boolean)
Make all output time series have L2 norm = 1
++ i.e., sum of squares = 1
notrans: (a boolean)
Don't check for initial positive transients in the data:
++ The test is a little slow, so skipping it is OK,
if you KNOW the data time series are transient-free.
orthogonalize_dset: (an existing file name)
Orthogonalize each voxel to the corresponding
voxel time series in dataset 'fset', which must
have the same spatial and temporal grid structure
as the main input dataset.
++ At present, only one '-dsort' option is allowed.
orthogonalize_file: (an existing file name)
Also orthogonalize input to columns in f.1D
++ Multiple '-ort' options are allowed.
out_file: (a file name, nipype default value: %s_bp)
output file from 3dBandpass
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
tr: (a float)
set time step (TR) in sec [default=from dataset header]
Outputs:
out_file: (an existing file name)
output file
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>
>>> from nipype.interfaces import afni as afni
>>> bim = afni.BlurInMask()
>>> bim.inputs.in_file = 'functional.nii'
>>> bim.inputs.mask = 'mask.nii'
>>> bim.inputs.fwhm = 5.0
>>> res = bim.run()
Inputs:
[Mandatory]
fwhm: (a float)
fwhm kernel size
in_file: (an existing file name)
input file to 3dSkullStrip
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
automask: (a boolean)
Create an automask from the input dataset.
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
float_out: (a boolean)
Save dataset as floats, no matter what the input data type is.
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (a file name)
Mask dataset, if desired. Blurring will occur only within the mask. Voxels NOT in the
mask will be set to zero in the output.
multimask: (a file name)
Multi-mask dataset -- each distinct nonzero value in dataset will be treated as a
separate mask for blurring purposes.
options: (a string)
options
out_file: (a file name)
output to the file
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
preserve: (a boolean)
Normally, voxels not in the mask will be set to zero in the output. If you want the
original values in the dataset to be preserved in the output, use this option.
suffix: (a string, nipype default value: _blurmask)
out_file suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dBrickStat
Compute maximum and/or minimum voxel values of an input dataset
For complete details, see the 3dBrickStat Documentation.
>>> from nipype.interfaces import afni as afni
>>> brickstat = afni.BrickStat()
>>> brickstat.inputs.in_file = 'functional.nii'
>>> brickstat.inputs.mask = 'skeleton_mask.nii.gz'
>>> brickstat.inputs.min = True
>>> res = brickstat.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dmaskave
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
-mask dset = use dset as mask to include/exclude voxels
min: (a boolean)
print the minimum value in dataset
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
Outputs:
min_val: (a float)
output
Wraps command 3dcalc
This program does voxel-by-voxel arithmetic on 3D datasets
For complete details, see the 3dcalc Documentation.
>>> from nipype.interfaces import afni as afni
>>> calc = afni.Calc()
>>> calc.inputs.in_file_a = 'functional.nii'
>>> calc.inputs.in_file_b = 'functional2.nii'
>>> calc.inputs.expr='a*b'
>>> calc.inputs.out_file = 'functional_calc.nii.gz'
>>> calc.inputs.outputtype = "NIFTI"
>>> calc.cmdline
'3dcalc -a functional.nii -b functional2.nii -expr "a*b" -prefix functional_calc.nii.gz'
Inputs:
[Mandatory]
expr: (a string)
expr
in_file_a: (an existing file name)
input file to 3dcalc
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
in_file_b: (an existing file name)
operand file to 3dcalc
other: (a file name)
other options
out_file: (a file name, nipype default value: %s_calc)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
single_idx: (an integer)
volume index for in_file_a
start_idx: (an integer)
start index for in_file_a
requires: stop_idx
stop_idx: (an integer)
stop index for in_file_a
requires: start_idx
suffix: (a string, nipype default value: _calc)
out_file suffix
Outputs:
out_file: (an existing file name)
output file
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.
>>> from nipype.interfaces import afni as afni
>>> copy = afni.Copy()
>>> copy.inputs.in_file = 'functional.nii'
>>> copy.inputs.out_file = 'new_func.nii'
>>> res = copy.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dcopy
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_copy)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dDespike
Removes ‘spikes’ from the 3D+time input dataset
For complete details, see the 3dDespike Documentation.
>>> from nipype.interfaces import afni as afni
>>> despike = afni.Despike()
>>> despike.inputs.in_file = 'functional.nii'
>>> res = despike.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dDespike
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_despike)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dDetrend
This program removes components from voxel time series using linear least squares
For complete details, see the 3dDetrend Documentation.
>>> from nipype.interfaces import afni as afni
>>> detrend = afni.Detrend()
>>> detrend.inputs.in_file = 'functional.nii'
>>> detrend.inputs.args = '-polort 2'
>>> res = detrend.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dDetrend
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_detrend)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
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.
>>> from nipype.interfaces import afni as afni
>>> fim = afni.Fim()
>>> fim.inputs.in_file = 'functional.nii'
>>> fim.inputs.ideal_file= 'seed.1D'
>>> fim.inputs.out_file = 'functional_corr.nii'
>>> fim.inputs.out = 'Correlation'
>>> fim.inputs.fim_thr = 0.0009
>>> res = fim.run()
Inputs:
[Mandatory]
ideal_file: (an existing file name)
ideal time series file name
in_file: (an existing file name)
input file to 3dfim+
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
fim_thr: (a float)
fim internal mask threshold value
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out: (a string)
Flag to output the specified parameter
out_file: (a file name, nipype default value: %s_fim)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
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.
>>> from nipype.interfaces import afni as afni
>>> fourier = afni.Fourier()
>>> fourier.inputs.in_file = 'functional.nii'
>>> fourier.inputs.args = '-retrend'
>>> fourier.inputs.highpass = 0.005
>>> fourier.inputs.lowpass = 0.1
>>> res = fourier.run()
Inputs:
[Mandatory]
highpass: (a float)
highpass
in_file: (an existing file name)
input file to 3dFourier
lowpass: (a float)
lowpass
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_fourier)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
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.
>>> from nipype.interfaces import afni as afni
>>> maskave = afni.Maskave()
>>> maskave.inputs.in_file = 'functional.nii'
>>> maskave.inputs.mask= 'seed_mask.nii'
>>> maskave.inputs.quiet= True
>>> maskave.cmdline
'3dmaskave -mask seed_mask.nii -quiet functional.nii > functional_maskave.1D'
>>> res = maskave.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dmaskave
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
matrix to align input file
out_file: (a file name, nipype default value: %s_maskave.1D)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
quiet: (a boolean)
matrix to align input file
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dmerge
Merge or edit volumes using AFNI 3dmerge command
For complete details, see the 3dmerge Documentation.
>>> from nipype.interfaces import afni as afni
>>> merge = afni.Merge()
>>> merge.inputs.in_files = ['functional.nii', 'functional2.nii']
>>> merge.inputs.blurfwhm = 4
>>> merge.inputs.doall = True
>>> merge.inputs.out_file = 'e7.nii'
>>> res = merge.run()
Inputs:
[Mandatory]
in_files: (an existing file name)
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
blurfwhm: (an integer)
FWHM blur value (mm)
doall: (a boolean)
apply options to all sub-bricks in dataset
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_merge)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dROIstats
Display statistics over masked regions
For complete details, see the 3dROIstats Documentation.
>>> from nipype.interfaces import afni as afni
>>> roistats = afni.ROIStats()
>>> roistats.inputs.in_file = 'functional.nii'
>>> roistats.inputs.mask = 'skeleton_mask.nii.gz'
>>> roistats.inputs.quiet=True
>>> res = roistats.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dROIstats
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
input mask
mask_f2short: (a boolean)
Tells the program to convert a float mask to short integers, by simple rounding.
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
quiet: (a boolean)
execute quietly
Outputs:
stats: (an existing file name)
output
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>
>>> from nipype.interfaces import afni as afni
>>> refit = afni.Refit()
>>> refit.inputs.in_file = 'structural.nii'
>>> refit.inputs.deoblique=True
>>> res = refit.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3drefit
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
deoblique: (a boolean)
replace current transformation matrix with cardinal matrix
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_refit)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string, nipype default value: _refit)
out_file suffix
xorigin: (a string)
x distance for edge voxel offset
yorigin: (a string)
y distance for edge voxel offset
zorigin: (a string)
z distance for edge voxel offset
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dresample
Resample or reorient an image using AFNI 3dresample command
For complete details, see the 3dresample Documentation.
>>> from nipype.interfaces import afni as afni
>>> resample = afni.Resample()
>>> resample.inputs.in_file = 'functional.nii'
>>> resample.inputs.orientation= 'RPI'
>>> res = resample.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dresample
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
master: (a file name)
align dataset grid to a reference file
orientation: (a string)
new orientation code
out_file: (a file name, nipype default value: %s_resample)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
resample_mode: ('NN' or 'Li' or 'Cu' or 'Bk')
resampling method from set {'NN', 'Li', 'Cu', 'Bk'}. These are for 'Nearest Neighbor',
'Linear', 'Cubic' and 'Blocky' interpolation, respectively. Default is NN.
suffix: (a string)
output image suffix
voxel_size: (a tuple of the form: (a float, a float, a float))
resample to new dx, dy and dz
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dSkullStrip
A program to extract the brain from surrounding tissue from MRI T1-weighted images
For complete details, see the 3dSkullStrip Documentation.
>>> from nipype.interfaces import afni as afni
>>> skullstrip = afni.SkullStrip()
>>> skullstrip.inputs.in_file = 'functional.nii'
>>> skullstrip.inputs.args = '-o_ply'
>>> res = skullstrip.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dSkullStrip
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_skullstrip)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dTcat
Concatenate sub-bricks from input datasets into one big 3D+time dataset
For complete details, see the 3dTcat Documentation.
>>> from nipype.interfaces import afni as afni
>>> tcat = afni.TCat()
>>> tcat.inputs.in_files = ['functional.nii', 'functional2.nii']
>>> tcat.inputs.out_file= 'functional_tcat.nii'
>>> tcat.inputs.rlt = '+'
>>> res = tcat.run()
Inputs:
[Mandatory]
in_files: (an existing file name)
input file to 3dTcat
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_tcat)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
rlt: (a string)
options
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
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>
>>> from nipype.interfaces import afni as afni
>>> tcm = afni.TcorrMap()
>>> tcm.inputs.in_file = 'functional.nii'
>>> tcm.inputs.mask = 'mask.nii'
>>> tcm.mean_file = '%s_meancorr.nii'
>>> res = tcm.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
absolute_threshold: (a file name)
mutually_exclusive: absolute_threshold, var_absolute_threshold,
var_absolute_threshold_normalize
args: (a string)
Additional parameters to the command
automask: (a boolean)
average_expr: (a file name)
mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
average_expr_nonzero: (a file name)
mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
bandpass: (a tuple of the form: (a float, a float))
blur_fwhm: (a float)
correlation_maps: (a file name)
correlation_maps_masked: (a file name)
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
expr: (a string)
histogram: (a file name)
histogram_bin_numbers: (an integer)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
mean_file: (a file name)
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
pmean: (a file name)
polort: (an integer)
qmean: (a file name)
regress_out_timeseries: (a file name)
seeds: (an existing file name)
mutually_exclusive: s, e, e, d, s, _, w, i, d, t, h
seeds_width: (a float)
mutually_exclusive: s, e, e, d, s
sum_expr: (a file name)
mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
thresholds: (a list of items which are an integer)
var_absolute_threshold: (a file name)
mutually_exclusive: absolute_threshold, var_absolute_threshold,
var_absolute_threshold_normalize
var_absolute_threshold_normalize: (a file name)
mutually_exclusive: absolute_threshold, var_absolute_threshold,
var_absolute_threshold_normalize
zmean: (a file name)
Outputs:
absolute_threshold: (a file name)
average_expr: (a file name)
average_expr_nonzero: (a file name)
correlation_maps: (a file name)
correlation_maps_masked: (a file name)
histogram: (a file name)
mean_file: (a file name)
pmean: (a file name)
qmean: (a file name)
sum_expr: (a file name)
var_absolute_threshold: (a file name)
var_absolute_threshold_normalize: (a file name)
zmean: (a file name)
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.
>>> from nipype.interfaces import afni as afni
>>> tcorrelate = afni.TCorrelate()
>>> tcorrelate.inputs.xset= 'u_rc1s1_Template.nii'
>>> tcorrelate.inputs.yset = 'u_rc1s2_Template.nii'
>>> tcorrelate.inputs.out_file = 'functional_tcorrelate.nii.gz'
>>> tcorrelate.inputs.polort = -1
>>> tcorrelate.inputs.pearson = True
>>> res = tcarrelate.run()
Inputs:
[Mandatory]
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
xset: (an existing file name)
input xset
yset: (an existing file name)
input yset
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_tcorr)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
pearson: (a boolean)
Correlation is the normal Pearson correlation coefficient
polort: (an integer)
Remove polynomical trend of order m
Outputs:
out_file: (an existing file name)
output file
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>
>>> from nipype.interfaces import afni as afni
>>> tshift = afni.TShift()
>>> tshift.inputs.in_file = 'functional.nii'
>>> tshift.inputs.tpattern = 'alt+z'
>>> tshift.inputs.tzero = 0.0
>>> tshift.cmdline
'3dTshift -prefix functional_tshift+orig.BRIK -tpattern alt+z -tzero 0.0 functional.nii'
>>> res = tshift.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dTShift
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore: (an integer)
ignore the first set of points specified
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
interp: ('Fourier' or 'linear' or 'cubic' or 'quintic' or 'heptic')
different interpolation methods (see 3dTShift for details) default = Fourier
out_file: (a file name, nipype default value: %s_tshift)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
rlt: (a boolean)
Before shifting, remove the mean and linear trend
rltplus: (a boolean)
Before shifting, remove the mean and linear trend and later put back the mean
suffix: (a string)
output image suffix
tpattern: ('alt+z' or 'alt+z2' or 'alt-z' or 'alt-z2' or 'seq+z' or 'seq-z')
use specified slice time pattern rather than one in header
tr: (a string)
manually set the TRYou can attach suffix "s" for seconds or "ms" for milliseconds.
tslice: (an integer)
align each slice to time offset of given slice
mutually_exclusive: tzero
tzero: (a float)
align each slice to given time offset
mutually_exclusive: tslice
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dTstat
Compute voxel-wise statistics using AFNI 3dTstat command
For complete details, see the 3dTstat Documentation.
>>> from nipype.interfaces import afni as afni
>>> tstat = afni.TStat()
>>> tstat.inputs.in_file = 'functional.nii'
>>> tstat.inputs.args= '-mean'
>>> res = tstat.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dTstat
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
mask file
options: (a string)
selected statistical output
out_file: (a file name, nipype default value: %s_tstat)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command to3d
Create a 3D dataset from 2D image files using AFNI to3d command
For complete details, see the to3d Documentation
>>> from nipype.interfaces import afni
>>> To3D = afni.To3D()
>>> To3D.inputs.datatype = 'float'
>>> To3D.inputs.infolder = 'dicomdir'
>>> To3D.inputs.filetype = "anat"
>>> To3D.inputs.outputtype = "NIFTI"
>>> To3D.cmdline
'to3d -datum float -anat -prefix dicomdir.nii dicomdir/*.dcm'
>>> res = To3D.run()
Inputs:
[Mandatory]
in_folder: (an existing directory name)
folder with DICOM images to convert
mutually_exclusive: infolder, in_folder
infolder: (an existing directory name)
folder with DICOM images to convert
mutually_exclusive: infolder, in_folder
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
assumemosaic: (a boolean)
assume that Siemens image is mosaic
datatype: ('short' or 'float' or 'byte' or 'complex')
set output file datatype
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
filetype: ('spgr' or 'fse' or 'epan' or 'anat' or 'ct' or 'spct' or 'pet' or 'mra' or
'bmap' or 'diff' or 'omri' or 'abuc' or 'fim' or 'fith' or 'fico' or 'fitt' or 'fift'
or 'fizt' or 'fict' or 'fibt' or 'fibn' or 'figt' or 'fipt' or 'fbuc')
type of datafile being converted
funcparams: (a string)
parameters for functional data
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
skipoutliers: (a boolean)
skip the outliers check
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dvolreg
Register input volumes to a base volume using AFNI 3dvolreg command
For complete details, see the 3dvolreg Documentation.
>>> from nipype.interfaces import afni as afni
>>> volreg = afni.Volreg()
>>> volreg.inputs.in_file = 'functional.nii'
>>> volreg.inputs.args = '-Fourier -twopass'
>>> volreg.inputs.zpad = 4
>>> volreg.inputs.outputtype = "NIFTI"
>>> volreg.cmdline
'3dvolreg -Fourier -twopass -1Dfile functional.1D -prefix functional_volreg.nii -zpad 4 functional.nii'
>>> res = volreg.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dvolreg
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
basefile: (an existing file name)
base file for registration
copyorigin: (a boolean)
copy base file origin coords to output
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
md1dfile: (a file name)
max displacement output file
oned_file: (a file name, nipype default value: %s.1D)
1D movement parameters output file
out_file: (a file name, nipype default value: %s_volreg)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
timeshift: (a boolean)
time shift to mean slice time offset
verbose: (a boolean)
more detailed description of the process
zpad: (an integer)
Zeropad around the edges by 'n' voxels during rotations
Outputs:
md1d_file: (an existing file name)
max displacement info file
oned_file: (an existing file name)
movement parameters info file
out_file: (an existing file name)
registered file
Wraps command 3dWarp
Use 3dWarp for spatially transforming a dataset
For complete details, see the 3dWarp Documentation.
>>> from nipype.interfaces import afni as afni
>>> warp = afni.Warp()
>>> warp.inputs.in_file = 'structural.nii'
>>> warp.inputs.deoblique = True
>>> res = warp.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dWarp
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
deoblique: (a boolean)
transform dataset from oblique to cardinal
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
gridset: (an existing file name)
copy grid of specified dataset
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
interp: ('linear' or 'cubic' or 'NN' or 'quintic')
spatial interpolation methods [default = linear]
matparent: (an existing file name)
apply transformation from 3dWarpDrive
mni2tta: (a boolean)
transform dataset from MNI152 to Talaraich
out_file: (a file name, nipype default value: %s_warp)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string, nipype default value: _warp)
out_file suffix
tta2mni: (a boolean)
transform dataset from Talairach to MNI152
zpad: (an integer)
pad input dataset with N planes of zero on all sides.
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dZcutup
Cut z-slices from a volume using AFNI 3dZcutup command
For complete details, see the 3dZcutup Documentation.
>>> from nipype.interfaces import afni as afni
>>> zcutup = afni.ZCutUp()
>>> zcutup.inputs.in_file = 'functional.nii'
>>> zcutup.inputs.out_file = 'functional_zcutup.nii'
>>> zcutup.inputs.keep= '0 10'
>>> res = zcutup.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dZcutup
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
keep: (a string)
slice range to keep in output
out_file: (a file name, nipype default value: %s_zcupup)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file