interfaces.afni.preprocess¶
Allineate¶
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
>>> allineate = afni.Allineate()
>>> allineate.inputs.in_file = 'functional.nii'
>>> allineate.inputs.out_file = 'functional_allineate.nii'
>>> allineate.inputs.in_matrix = 'cmatrix.mat'
>>> allineate.cmdline
'3dAllineate -1Dmatrix_apply cmatrix.mat -prefix functional_allineate.nii -source functional.nii'
>>> res = allineate.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dAllineate
flag: -source %s, position: -1
[Optional]
args: (a unicode 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 unicode string)
Compute a weight function using the 3dAutomask algorithm plus some
blurring of the base image.
flag: -autoweight%s
center_of_mass: (a unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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 initialpoints to search
for the starting point for the finepass. If bb==0, then no search is
made for the beststarting point, and the identity transformation
isused 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
References:: None None
AutoTcorrelate¶
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].
For complete details, see the 3dAutoTcorrelate Documentation.
Examples¶
>>> from nipype.interfaces import 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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Automask¶
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
>>> 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Bandpass¶
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
>>> from nipype.testing import example_data
>>> bandpass = afni.Bandpass()
>>> bandpass.inputs.in_file = 'functional.nii'
>>> bandpass.inputs.highpass = 0.005
>>> bandpass.inputs.lowpass = 0.1
>>> bandpass.cmdline
'3dBandpass -prefix functional_bp 0.005000 0.100000 functional.nii'
>>> 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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' millimeters.
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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
BlurInMask¶
Wraps command 3dBlurInMask
Blurs a dataset spatially inside a mask. That’s all. Experimental.
For complete details, see the 3dBlurInMask Documentation.
Examples¶
>>> from nipype.interfaces import 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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 unicode string)
options
flag: %s, position: 2
out_file: (a file name)
output to the file
flag: -prefix %s, position: -1
outputtype: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
BlurToFWHM¶
Wraps command 3dBlurToFWHM
Blurs a ‘master’ dataset until it reaches a specified FWHM smoothness (approximately).
For complete details, see the 3dBlurToFWHM 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'
>>> res = blur.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
The dataset that will be smoothed
flag: -input %s
[Optional]
args: (a unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
ClipLevel¶
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'
>>> cliplevel.cmdline
'3dClipLevel anatomical.nii'
>>> res = cliplevel.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dClipLevel
flag: %s, position: -1
[Optional]
args: (a unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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
DegreeCentrality¶
Wraps command 3dDegreeCentrality
Performs degree centrality on a dataset using a given maskfile via 3dDegreeCentrality
For complete details, see the 3dDegreeCentrality Documentation.
Examples¶
>>> from nipype.interfaces import 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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 unicode 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: ('AFNI' or 'NIFTI_GZ' 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 computed after thresholding
with one-dimensional and ijk voxel indices, correlations, image
extents, and affine matrix.
out_file: (an existing file name)
output file
References:: None None
Despike¶
Wraps command 3dDespike
Removes ‘spikes’ from the 3D+time input dataset
For complete details, see the 3dDespike Documentation.
Examples¶
>>> from nipype.interfaces import 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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Detrend¶
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
>>> 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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
ECM¶
Wraps command 3dECM
Performs degree centrality on a dataset using a given maskfile via the 3dECM command
For complete details, see the 3dECM Documentation.
Examples¶
>>> from nipype.interfaces import 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Fim¶
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
>>> 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
>>> fim.cmdline
'3dfim+ -input functional.nii -ideal_file seed.1D -fim_thr 0.000900 -out Correlation -bucket functional_corr.nii'
>>> 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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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 unicode 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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Fourier¶
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
>>> fourier = afni.Fourier()
>>> fourier.inputs.in_file = 'functional.nii'
>>> fourier.inputs.retrend = True
>>> fourier.inputs.highpass = 0.005
>>> fourier.inputs.lowpass = 0.1
>>> fourier.cmdline
'3dFourier -highpass 0.005000 -lowpass 0.100000 -prefix functional_fourier -retrend functional.nii'
>>> res = fourier.run()
Inputs:
[Mandatory]
highpass: (a float)
highpass
flag: -highpass %f
in_file: (an existing file name)
input file to 3dFourier
flag: %s, position: -1
lowpass: (a float)
lowpass
flag: -lowpass %f
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' or 'NIFTI')
AFNI output filetype
retrend: (a boolean)
Any mean and linear trend are removed before filtering. This will
restore the trend after filtering.
flag: -retrend
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
References:: None None
Hist¶
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
>>> 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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¶
Wraps command 3dLFCD
Performs degree centrality on a dataset using a given maskfile via the 3dLFCD command
For complete details, see the 3dLFCD Documentation.
Examples¶
>>> from nipype.interfaces import 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Maskave¶
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
>>> 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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Means¶
Wraps command 3dMean
Takes the voxel-by-voxel mean of all input datasets using 3dMean
For complete details, see the 3dMean Documentation.
Examples¶
>>> from nipype.interfaces import 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'
>>> res = means.run()
Inputs:
[Mandatory]
in_file_a: (an existing file name)
input file to 3dMean
flag: %s, position: 0
[Optional]
args: (a unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' or 'NIFTI')
AFNI output filetype
scale: (a unicode 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
References:: None None
OutlierCount¶
Wraps command 3dToutcount
Calculates number of ‘outliers’ a 3D+time dataset, at each time point, and writes the results to stdout.
For complete details, see the 3dToutcount 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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¶
Wraps command 3dTqual
Computes a `quality index’ for each sub-brick in a 3D+time dataset. The output is a 1D time series with the index for each sub-brick. The results are written to stdout.
For complete details, see the 3dTqual 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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 captured standard output
QwarpPlusMinus¶
Wraps command 3dQwarp -prefix Qwarp.nii.gz -plusminus
A version of 3dQwarp for performing field susceptibility correction using two images with opposing phase encoding directions.
For complete details, see the 3dQwarp Documentation.
Examples¶
>>> from nipype.interfaces import afni
>>> qwarp = afni.QwarpPlusMinus()
>>> qwarp.inputs.source_file = 'sub-01_dir-LR_epi.nii.gz'
>>> qwarp.inputs.nopadWARP = True
>>> qwarp.inputs.base_file = 'sub-01_dir-RL_epi.nii.gz'
>>> qwarp.cmdline
'3dQwarp -prefix Qwarp.nii.gz -plusminus -base sub-01_dir-RL_epi.nii.gz -nopadWARP -source sub-01_dir-LR_epi.nii.gz'
>>> res = warp.run()
Inputs:
[Mandatory]
base_file: (an existing file name)
Base image (opposite phase encoding direction than source image).
flag: -base %s
source_file: (an existing file name)
Source image (opposite phase encoding direction than base image).
flag: -source %s
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
blur: (a list of from 1 to 2 items which are a float)
Gaussian blur the input images by (FWHM) voxels before doing the
alignment (the output dataset will not be blurred). The default is
2.345 (for no good reason). Optionally, you can provide 2 values,
and then the first one is applied to the base volume, the second to
the source volume. A negative blur radius means to use 3D median
filtering, rather than Gaussian blurring. This type of filtering
will better preserve edges, which can be important in alignment.
flag: -blur %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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
minpatch: (an integer (int or long))
Set the minimum patch size for warp searching to 'mm' voxels.
flag: -minpatch %d
nopadWARP: (a boolean)
If for some reason you require the warp volume tomatch the base
volume, then use this option to have the outputWARP dataset(s)
truncated.
flag: -nopadWARP
noweight: (a boolean)
If you want a binary weight (the old default), use this option.That
is, each voxel in the base volume automask will beweighted the same
in the computation of the cost functional.
flag: -noweight
pblur: (a list of from 1 to 2 items which are a float)
The fraction of the patch size thatis used for the progressive blur
by providing a value between 0 and 0.25. If you provide TWO values,
the first fraction is used for progressively blurring the base image
and the second for the source image.
flag: -pblur %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:
base_warp: (an existing file name)
Field suceptibility correction warp (in 'mm') for base image.
source_warp: (an existing file name)
Field suceptibility correction warp (in 'mm') for source image.
warped_base: (an existing file name)
Undistorted base file.
warped_source: (an existing file name)
Undistorted source file.
ROIStats¶
Wraps command 3dROIstats
Display statistics over masked regions
For complete details, see the 3dROIstats Documentation.
Examples¶
>>> from nipype.interfaces import afni
>>> roistats = afni.ROIStats()
>>> roistats.inputs.in_file = 'functional.nii'
>>> roistats.inputs.mask = 'skeleton_mask.nii.gz'
>>> roistats.inputs.quiet = True
>>> roistats.cmdline
'3dROIstats -quiet -mask skeleton_mask.nii.gz functional.nii'
>>> 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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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
Retroicor¶
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
>>> ret = afni.Retroicor()
>>> ret.inputs.in_file = 'functional.nii'
>>> ret.inputs.card = 'mask.1D'
>>> ret.inputs.resp = 'resp.1D'
>>> ret.inputs.outputtype = 'NIFTI'
>>> ret.cmdline
'3dretroicor -prefix functional_retroicor.nii -resp resp.1D -card mask.1D functional.nii'
>>> res = ret.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dretroicor
flag: %s, position: -1
[Optional]
args: (a unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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
out_file: (a file name)
output image file name
flag: -prefix %s, position: 1
outputtype: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Seg¶
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.
Examples¶
>>> from nipype.interfaces.afni import preprocess
>>> seg = preprocess.Seg()
>>> seg.inputs.in_file = 'structural.nii'
>>> seg.inputs.mask = 'AUTO'
>>> seg.cmdline
'3dSeg -mask AUTO -anat structural.nii'
>>> 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 unicode string)
Additional parameters to the command
flag: %s
bias_classes: (a unicode string)
A semicolon delimited 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 unicode string)
CLASS_STRING is a semicolon delimited string of class labels
flag: -classes %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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 unicode string)
MIXFRAC sets up the volume-wide (within mask) tissue fractions while
initializing the segmentation (see IGNORE for exception)
flag: -mixfrac %s
prefix: (a unicode 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¶
Wraps command 3dSkullStrip
A program to extract the brain from surrounding tissue from MRI T1-weighted images. TODO Add optional arguments.
For complete details, see the 3dSkullStrip Documentation.
Examples¶
>>> from nipype.interfaces import afni
>>> skullstrip = afni.SkullStrip()
>>> skullstrip.inputs.in_file = 'functional.nii'
>>> skullstrip.inputs.args = '-o_ply'
>>> skullstrip.cmdline
'3dSkullStrip -input functional.nii -o_ply -prefix functional_skullstrip'
>>> res = skullstrip.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dSkullStrip
flag: -input %s, position: 1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
TCorr1D¶
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
>>> 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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
TCorrMap¶
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.
Examples¶
>>> from nipype.interfaces import afni
>>> tcm = afni.TCorrMap()
>>> tcm.inputs.in_file = 'functional.nii'
>>> tcm.inputs.mask = 'mask.nii'
>>> tcm.mean_file = 'functional_meancorr.nii'
>>> tcm.cmdline
'3dTcorrMap -input functional.nii -mask mask.nii -Mean functional_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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class 'str', nipype default value: {})
Environment variables
expr: (a unicode 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: ('AFNI' or 'NIFTI_GZ' 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)
References:: None None
TCorrelate¶
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
>>> 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
>>> tcorrelate.cmdline
'3dTcorrelate -prefix functional_tcorrelate.nii.gz -pearson -polort -1 u_rc1s1_Template.nii u_rc1s2_Template.nii'
>>> 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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' or 'NIFTI')
AFNI output filetype
pearson: (a boolean)
Correlation is the normal Pearson correlation coefficient
flag: -pearson
polort: (an integer (int or long))
Remove polynomical trend of order m
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
References:: None None
TShift¶
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.
Examples¶
>>> from nipype.interfaces import 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 -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 unicode string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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 unicode string)
use specified slice time pattern rather than one in header
flag: -tpattern %s
tr: (a unicode string)
manually set the TR. You 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
References:: None None
Volreg¶
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
>>> 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 unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None
Warp¶
Wraps command 3dWarp
Use 3dWarp for spatially transforming a dataset
For complete details, see the 3dWarp Documentation.
Examples¶
>>> from nipype.interfaces import 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'
>>> res = warp.run()
>>> 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'
>>> res = warp_2.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dWarp
flag: %s, position: -1
[Optional]
args: (a unicode 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 bytes or None or a value
of class 'str' and with values which are a bytes or None or a value
of class '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: ('AFNI' or 'NIFTI_GZ' 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
References:: None None