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.in_matrix= 'cmatrix.mat'
>>> res = allineate.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dAllineate
flag: -source %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
autobox: (a boolean)
Expand the -automask function to enclose a rectangular
box that holds the irregular mask.
flag: -autobox
automask: (an integer)
Compute a mask function, set a value for dilation or 0.
flag: -automask+%d
autoweight: (a string)
Compute a weight function using the 3dAutomask
algorithm plus some blurring of the base image.
flag: -autoweight%s
center_of_mass: (a string)
Use the center-of-mass calculation to bracket the shifts.
flag: -cmass%s
check: (a list of items which are 'leastsq' or 'ls' or 'mutualinfo'
or 'mi' or 'corratio_mul' or 'crM' or 'norm_mutualinfo' or 'nmi' or
'hellinger' or 'hel' or 'corratio_add' or 'crA' or 'corratio_uns'
or 'crU')
After cost functional optimization is done, start at the
final parameters and RE-optimize using this new cost functions.
If the results are too different, a warning message will be
printed. However, the final parameters from the original
optimization will be used to create the output dataset.
flag: -check %s
convergence: (a float)
Convergence test in millimeters (default 0.05mm).
flag: -conv %f
cost: ('leastsq' or 'ls' or 'mutualinfo' or 'mi' or 'corratio_mul' or
'crM' or 'norm_mutualinfo' or 'nmi' or 'hellinger' or 'hel' or
'corratio_add' or 'crA' or 'corratio_uns' or 'crU')
Defines the 'cost' function that defines the matching
between the source and the base
flag: -cost %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
epi: (a boolean)
Treat the source dataset as being composed of warped
EPI slices, and the base as comprising anatomically
'true' images. Only phase-encoding direction image
shearing and scaling will be allowed with this option.
flag: -EPI
final_interpolation: ('nearestneighbour' or 'linear' or 'cubic' or
'quintic' or 'wsinc5')
Defines interpolation method used to create the output dataset
flag: -final %s
fine_blur: (a float)
Set the blurring radius to use in the fine resolution
pass to 'x' mm. A small amount (1-2 mm?) of blurring at
the fine step may help with convergence, if there is
some problem, especially if the base volume is very noisy.
[Default == 0 mm = no blurring at the final alignment pass]
flag: -fineblur %f
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
in_matrix: (a file name)
matrix to align input file
flag: -1Dmatrix_apply %s, position: -3
in_param_file: (an existing file name)
Read warp parameters from file and apply them to
the source dataset, and produce a new dataset
flag: -1Dparam_apply %s
interpolation: ('nearestneighbour' or 'linear' or 'cubic' or
'quintic')
Defines interpolation method to use during matching
flag: -interp %s
master: (an existing file name)
Write the output dataset on the same grid as this file
flag: -master %s
newgrid: (a float)
Write the output dataset using isotropic grid spacing in mm
flag: -newgrid %f
nmatch: (an integer)
Use at most n scattered points to match the datasets.
flag: -nmatch %d
no_pad: (a boolean)
Do not use zero-padding on the base image.
flag: -nopad
nomask: (a boolean)
Don't compute the autoweight/mask; if -weight is not
also used, then every voxel will be counted equally.
flag: -nomask
nwarp: ('bilinear' or 'cubic' or 'quintic' or 'heptic' or 'nonic' or
'poly3' or 'poly5' or 'poly7' or 'poly9')
Experimental nonlinear warping: bilinear or legendre poly.
flag: -nwarp %s
nwarp_fixdep: (a list of items which are 'X' or 'Y' or 'Z' or 'I' or
'J' or 'K')
To fix non-linear warp dependency along directions.
flag: -nwarp_fixdep%s
nwarp_fixmot: (a list of items which are 'X' or 'Y' or 'Z' or 'I' or
'J' or 'K')
To fix motion along directions.
flag: -nwarp_fixmot%s
one_pass: (a boolean)
Use only the refining pass -- do not try a coarse
resolution pass first. Useful if you know that only
small amounts of image alignment are needed.
flag: -onepass
out_file: (a file name)
output file from 3dAllineate
flag: -prefix %s, position: -2
out_matrix: (a file name)
Save the transformation matrix for each volume.
flag: -1Dmatrix_save %s
out_param_file: (a file name)
Save the warp parameters in ASCII (.1D) format.
flag: -1Dparam_save %s
out_weight_file: (a file name)
Write the weight volume to disk as a dataset
flag: -wtprefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
reference: (an existing file name)
file to be used as reference, the first volume will be used
if not given the reference will be the first volume of in_file.
flag: -base %s
replacebase: (a boolean)
If the source has more than one volume, then after the first
volume is aligned to the base
flag: -replacebase
replacemeth: ('leastsq' or 'ls' or 'mutualinfo' or 'mi' or
'corratio_mul' or 'crM' or 'norm_mutualinfo' or 'nmi' or
'hellinger' or 'hel' or 'corratio_add' or 'crA' or 'corratio_uns'
or 'crU')
After first volume is aligned, switch method for later volumes.
For use with '-replacebase'.
flag: -replacemeth %s
source_automask: (an integer)
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
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]
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
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.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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
eta2: (a boolean)
eta^2 similarity
flag: -eta2
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask: (an existing file name)
mask of voxels
flag: -mask %s
mask_only_targets: (a boolean)
use mask only on targets voxels
flag: -mask_only_targets
mutually_exclusive: mask_source
mask_source: (an existing file name)
mask for source voxels
flag: -mask_source %s
mutually_exclusive: mask_only_targets
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
polort: (an integer)
Remove polynomical trend of order m or -1 for no detrending
flag: -polort %d
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
flag: -input %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
no_clustering: (a boolean)
Don't do any clustering to find box. Any non-zero
voxel will be preserved in the cropped volume.
The default method uses some clustering to find the
cropping box, and will clip off small isolated blobs.
flag: -noclust
out_file: (a file name)
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
padding: (an integer)
Number of extra voxels to pad on each side of box
flag: -npad %d
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
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
brain_file: (a file name)
output file from 3dAutomask
flag: -apply_prefix %s
clfrac: (a float)
sets the clip level fraction (must be 0.1-0.9). A small value will
tend to make the mask larger [default = 0.5].
flag: -clfrac %s
dilate: (an integer)
dilate the mask outwards
flag: -dilate %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
erode: (an integer)
erode the mask inwards
flag: -erode %s
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
Outputs:
brain_file: (an existing 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
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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
automask: (a boolean)
Create a mask from the input dataset
flag: -automask
blur: (a float)
Blur (inside the mask only) with a filter
width (FWHM) of 'fff' millimeters.
flag: -blur %f
despike: (a boolean)
Despike each time series before other processing.
++ Hopefully, you don't actually need to do this,
which is why it is optional.
flag: -despike
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
localPV: (a float)
Replace each vector by the local Principal Vector
(AKA first singular vector) from a neighborhood
of radius 'rrr' millimiters.
++ Note that the PV time series is L2 normalized.
++ This option is mostly for Bob Cox to have fun with.
flag: -localPV %f
mask: (an existing file name)
mask file
flag: -mask %s, position: 2
nfft: (an integer)
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: (an existing file name)
Also orthogonalize input to columns in f.1D
++ Multiple '-ort' options are allowed.
flag: -ort %s
out_file: (a file name)
output file from 3dBandpass
flag: -prefix %s, position: 1
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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
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
>>> 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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
automask: (a boolean)
Create an automask from the input dataset.
flag: -automask
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
float_out: (a boolean)
Save dataset as floats, no matter what the input data type is.
flag: -float
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask: (a file name)
Mask dataset, if desired. Blurring will occur only within the mask.
Voxels NOT in the mask will be set to zero in the output.
flag: -mask %s
multimask: (a file name)
Multi-mask dataset -- each distinct nonzero value in dataset will be
treated as a separate mask for blurring purposes.
flag: -Mmask %s
options: (a string)
options
flag: %s, position: 2
out_file: (a file name)
output to the file
flag: -prefix %s, position: -1
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
preserve: (a boolean)
Normally, voxels not in the mask will be set to zero in the output.
If you want the original values in the dataset to be preserved in
the output, use this option.
flag: -preserve
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
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask: (an existing file name)
-mask dset = use dset as mask to include/exclude voxels
flag: -mask %s, position: 2
min: (a boolean)
print the minimum value in dataset
flag: -min, position: 1
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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
flag: -expr "%s", position: 3
in_file_a: (an existing file name)
input file to 3dcalc
flag: -a %s, position: 0
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
in_file_b: (an existing file name)
operand file to 3dcalc
flag: -b %s, position: 1
in_file_c: (an existing file name)
operand file to 3dcalc
flag: -c %s, position: 2
other: (a file name)
other options
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
single_idx: (an integer)
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
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
flag: %s, position: -2
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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'
>>> 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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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'
>>> 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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
Outputs:
out_file: (an existing file name)
output file
Wraps command 1deval
Evaluates an expression that may include columns of data from one or more text files
see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/1deval.html>
>>> from nipype.interfaces import afni as afni
>>> eval = afni.Eval()
>>> eval.inputs.in_file_a = 'seed.1D'
>>> eval.inputs.in_file_b = 'resp.1D'
>>> eval.inputs.expr='a*b'
>>> eval.inputs.out1D = True
>>> eval.inputs.out_file = 'data_calc.1D'
>>> calc.cmdline
'3deval -a timeseries1.1D -b timeseries2.1D -expr "a*b" -1D -prefix data_calc.1D'
Inputs:
[Mandatory]
expr: (a string)
expr
flag: -expr "%s", position: 3
in_file_a: (an existing file name)
input file to 1deval
flag: -a %s, position: 0
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
in_file_b: (an existing file name)
operand file to 1deval
flag: -b %s, position: 1
in_file_c: (an existing file name)
operand file to 1deval
flag: -c %s, position: 2
other: (a file name)
other options
out1D: (a boolean)
output in 1D
flag: -1D
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
single_idx: (an integer)
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
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
flag: -ideal_file %s, position: 2
in_file: (an existing file name)
input file to 3dfim+
flag: -input %s, position: 1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
fim_thr: (a float)
fim internal mask threshold value
flag: -fim_thr %f, position: 3
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out: (a string)
Flag to output the specified parameter
flag: -out %s, position: 4
out_file: (a file name)
output image file name
flag: -bucket %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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
flag: -highpass %f, position: 1
in_file: (an existing file name)
input file to 3dFourier
flag: %s, position: -1
lowpass: (a float)
lowpass
flag: -lowpass %f, position: 0
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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
flag: %s, position: -2
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask: (an existing file name)
matrix to align input file
flag: -mask %s, position: 1
out_file: (a file name)
output image file name
flag: > %s, position: -1
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
quiet: (a boolean)
matrix to align input file
flag: -quiet, position: 2
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dMean
Takes the voxel-by-voxel mean of all input datasets using 3dMean
see AFNI Documentation: <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dMean.html>
>>> from nipype.interfaces import afni as afni
>>> means = afni.Means()
>>> means.inputs.in_file_a = 'im1.nii'
>>> means.inputs.in_file_b = 'im2.nii'
>>> means.inputs.out_file = 'output.nii'
>>> means.cmdline
'3dMean im1.nii im2.nii -prefix output.nii'
Inputs:
[Mandatory]
in_file_a: (an existing file name)
input file to 3dMean
flag: %s, position: 0
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
count: (a boolean)
compute count of non-zero voxels
flag: -count
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
in_file_b: (an existing file name)
another input file to 3dMean
flag: %s, position: 1
mask_inter: (a boolean)
create intersection mask
flag: -mask_inter
mask_union: (a boolean)
create union mask
flag: -mask_union
non_zero: (a boolean)
use only non-zero values
flag: -non_zero
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
scale: (a string)
scaling of output
flag: -%sscale
sqr: (a boolean)
mean square instead of value
flag: -sqr
std_dev: (a boolean)
calculate std dev
flag: -stdev
summ: (a boolean)
take sum, (not average)
flag: -sum
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)
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
blurfwhm: (an integer)
FWHM blur value (mm)
flag: -1blur_fwhm %d
doall: (a boolean)
apply options to all sub-bricks in dataset
flag: -doall
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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
flag: %s, position: -1
terminal_output: ('allatonce', nipype default value: allatonce)
Control terminal output:`allatonce` - waits till command is finished
to display output
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask: (an existing file name)
input mask
flag: -mask %s, position: 3
mask_f2short: (a boolean)
Tells the program to convert a float mask to short integers, by
simple rounding.
flag: -mask_f2short, position: 2
quiet: (a boolean)
execute quietly
flag: -quiet, position: 1
Outputs:
stats: (an existing file name)
output tab separated values file
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
>>> refit.cmdline
'3drefit -deoblique structural.nii'
>>> res = refit.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3drefit
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
deoblique: (a boolean)
replace current transformation matrix with cardinal matrix
flag: -deoblique
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
xorigin: (a string)
x distance for edge voxel offset
flag: -xorigin %s
yorigin: (a string)
y distance for edge voxel offset
flag: -yorigin %s
zorigin: (a string)
z distance for edge voxel offset
flag: -zorigin %s
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'
>>> resample.inputs.outputtype = "NIFTI"
>>> resample.cmdline
'3dresample -orient RPI -prefix functional_resample.nii -inset functional.nii'
>>> res = resample.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dresample
flag: -inset %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
master: (a file name)
align dataset grid to a reference file
flag: -master %s
orientation: (a string)
new orientation code
flag: -orient %s
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
resample_mode: ('NN' or 'Li' or 'Cu' or 'Bk')
resampling method from set {'NN', 'Li', 'Cu', 'Bk'}. These are for
'Nearest Neighbor', 'Linear', 'Cubic' and 'Blocky' interpolation,
respectively. Default is NN.
flag: -rmode %s
voxel_size: (a tuple of the form: (a float, a float, a float))
resample to new dx, dy and dz
flag: -dxyz %f %f %f
Outputs:
out_file: (an existing file name)
output file
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.
>>> from nipype.interfaces import afni as afni
>>> ret = afni.Retroicor()
>>> ret.inputs.in_file = 'functional.nii'
>>> ret.inputs.card = 'mask.1D'
>>> ret.inputs.resp = 'resp.1D'
>>> res = ret.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dretroicor
flag: %s, position: -1
out_file: (a file name)
output image file name
flag: -prefix %s, position: 1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
card: (an existing file name)
1D cardiac data file for cardiac correction
flag: -card %s, position: -2
cardphase: (a file name)
Filename for 1D cardiac phase output
flag: -cardphase %s, position: -6
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
order: (an integer)
The order of the correction (2 is typical)
flag: -order %s, position: -5
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
resp: (an existing file name)
1D respiratory waveform data for correction
flag: -resp %s, position: -3
respphase: (a file name)
Filename for 1D resp phase output
flag: -respphase %s, position: -7
threshold: (an integer)
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
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
flag: -input %s, position: 1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
rlt: (a string)
options
flag: -rlt%s, position: 1
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dTcorr1D
Computes the correlation coefficient between each voxel time series in the input 3D+time dataset. For complete details, see the 3dTcorr1D Documentation.
>>> from nipype.interfaces import afni as afni
>>> tcorr1D = afni.TCorr1D()
>>> tcorr1D.inputs.xset= 'u_rc1s1_Template.nii'
>>> tcorr1D.inputs.y_1d = 'seed.1D'
>>> tcorr1D.cmdline
'3dTcorr1D -prefix u_rc1s1_Template_correlation.nii.gz u_rc1s1_Template.nii seed.1D'
>>> res = tcorr1D.run()
Inputs:
[Mandatory]
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
xset: (an existing file name)
3d+time dataset input
flag: %s, position: -2
y_1d: (an existing file name)
1D time series file input
flag: %s, position: -1
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
ktaub: (a boolean)
Correlation is the Kendall's tau_b correlation coefficient
flag: -ktaub, position: 1
mutually_exclusive: pearson, spearman, quadrant
out_file: (a file name)
output filename prefix
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
pearson: (a boolean)
Correlation is the normal Pearson correlation coefficient
flag: -pearson, position: 1
mutually_exclusive: spearman, quadrant, ktaub
quadrant: (a boolean)
Correlation is the quadrant correlation coefficient
flag: -quadrant, position: 1
mutually_exclusive: pearson, spearman, ktaub
spearman: (a boolean)
Correlation is the Spearman (rank) correlation coefficient
flag: -spearman, position: 1
mutually_exclusive: pearson, quadrant, ktaub
Outputs:
out_file: (an existing file name)
output file containing correlations
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)
flag: -input %s
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
absolute_threshold: (a file name)
flag: -Thresh %f %s
mutually_exclusive: absolute_threshold, var_absolute_threshold,
var_absolute_threshold_normalize
args: (a string)
Additional parameters to the command
flag: %s
automask: (a boolean)
flag: -automask
average_expr: (a file name)
flag: -Aexpr %s %s
mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
average_expr_nonzero: (a file name)
flag: -Cexpr %s %s
mutually_exclusive: average_expr, average_expr_nonzero, sum_expr
bandpass: (a tuple of the form: (a float, a float))
flag: -bpass %f %f
blur_fwhm: (a float)
flag: -Gblur %f
correlation_maps: (a file name)
flag: -CorrMap %s
correlation_maps_masked: (a file name)
flag: -CorrMask %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
expr: (a string)
histogram: (a file name)
flag: -Hist %d %s
histogram_bin_numbers: (an integer)
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask: (an existing file name)
flag: -mask %s
mean_file: (a file name)
flag: -Mean %s
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
pmean: (a file name)
flag: -Pmean %s
polort: (an integer)
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
thresholds: (a list of items which are an integer)
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)
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: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
xset: (an existing file name)
input xset
flag: %s, position: -2
yset: (an existing file name)
input yset
flag: %s, position: -1
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
pearson: (a boolean)
Correlation is the normal Pearson correlation coefficient
flag: -pearson, position: 1
polort: (an integer)
Remove polynomical trend of order m
flag: -polort %d, position: 2
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 #doctest:
'3dTshift -prefix functional_tshift -tpattern alt+z -tzero 0.0 functional.nii'
>>> res = tshift.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dTShift
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore: (an integer)
ignore the first set of points specified
flag: -ignore %s
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
interp: ('Fourier' or 'linear' or 'cubic' or 'quintic' or 'heptic')
different interpolation methods (see 3dTShift for details) default =
Fourier
flag: -%s
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
rlt: (a boolean)
Before shifting, remove the mean and linear trend
flag: -rlt
rltplus: (a boolean)
Before shifting, remove the mean and linear trend and later put back
the mean
flag: -rlt+
tpattern: (a string)
use specified slice time pattern rather than one in header
flag: -tpattern %s
tr: (a string)
manually set the TRYou can attach suffix "s" for seconds or "ms" for
milliseconds.
flag: -TR %s
tslice: (an integer)
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
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'
>>> tstat.inputs.out_file = "stats"
>>> tstat.cmdline
'3dTstat -mean -prefix stats functional.nii'
>>> res = tstat.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dTstat
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
mask: (an existing file name)
mask file
flag: -mask %s
options: (a string)
selected statistical output
flag: %s
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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.in_folder = '.'
>>> To3D.inputs.out_file = 'dicomdir.nii'
>>> To3D.inputs.filetype = "anat"
>>> To3D.cmdline
'to3d -datum float -anat -prefix dicomdir.nii ./*.dcm'
>>> res = To3D.run()
Inputs:
[Mandatory]
in_folder: (an existing directory name)
folder with DICOM images to convert
flag: %s/*.dcm, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
assumemosaic: (a boolean)
assume that Siemens image is mosaic
flag: -assume_dicom_mosaic
datatype: ('short' or 'float' or 'byte' or 'complex')
set output file datatype
flag: -datum %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
filetype: ('spgr' or 'fse' or 'epan' or 'anat' or 'ct' or 'spct' or
'pet' or 'mra' or 'bmap' or 'diff' or 'omri' or 'abuc' or 'fim' or
'fith' or 'fico' or 'fitt' or 'fift' or 'fizt' or 'fict' or 'fibt'
or 'fibn' or 'figt' or 'fipt' or 'fbuc')
type of datafile being converted
flag: -%s
funcparams: (a string)
parameters for functional data
flag: -time:zt %s alt+z2
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
skipoutliers: (a boolean)
skip the outliers check
flag: -skip_outliers
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 -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
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
basefile: (an existing file name)
base file for registration
flag: -base %s, position: -6
copyorigin: (a boolean)
copy base file origin coords to output
flag: -twodup
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
md1d_file: (a file name)
max displacement output file
flag: -maxdisp1D %s, position: -4
oned_file: (a file name)
1D movement parameters output file
flag: -1Dfile %s
oned_matrix_save: (a file name)
Save the matrix transformation
flag: -1Dmatrix_save %s
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
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)
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
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
>>> warp.inputs.out_file = "trans.nii.gz"
>>> warp.cmdline
'3dWarp -deoblique -prefix trans.nii.gz structural.nii'
>>> res = warp.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dWarp
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
deoblique: (a boolean)
transform dataset from oblique to cardinal
flag: -deoblique
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
gridset: (an existing file name)
copy grid of specified dataset
flag: -gridset %s
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
interp: ('linear' or 'cubic' or 'NN' or 'quintic')
spatial interpolation methods [default = linear]
flag: -%s
matparent: (an existing file name)
apply transformation from 3dWarpDrive
flag: -matparent %s
mni2tta: (a boolean)
transform dataset from MNI152 to Talaraich
flag: -mni2tta
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
tta2mni: (a boolean)
transform dataset from Talairach to MNI152
flag: -tta2mni
zpad: (an integer)
pad input dataset with N planes of zero on all sides.
flag: -zpad %d
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
flag: %s, position: -1
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
Control terminal output: `stream` - displays to terminal
immediately, `allatonce` - waits till command is finished to display
output, `file` - writes output to file, `none` - output is ignored
[Optional]
args: (a string)
Additional parameters to the command
flag: %s
environ: (a dictionary with keys which are a value of type 'str' and
with values which are a value of type 'str', nipype default value:
{})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the
interface fails to run
keep: (a string)
slice range to keep in output
flag: -keep %s
out_file: (a file name)
output image file name
flag: -prefix %s
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
Outputs:
out_file: (an existing file name)
output file