Wraps command 3dAllineate
Program to align one dataset (the ‘source’) to a base dataset
For complete details, see the 3dAllineate Documentation.
>>> from nipype.interfaces import afni as afni
>>> allineate = afni.Allineate()
>>> allineate.inputs.in_file = 'functional.nii'
>>> allineate.inputs.out_file= 'functional_allineate.nii'
>>> allineate.inputs.matrix= 'cmatrix.mat'
>>> res = allineate.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dAllineate
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
matrix: (an existing file name)
matrix to align input file
out_file: (a file name, nipype default value: %s_allineate)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dAutoTcorrelate
Computes the correlation coefficient between the time series of each pair of voxels in the input dataset, and stores the output into a new anatomical bucket dataset [scaled to shorts to save memory space].
>>> from nipype.interfaces import afni as afni
>>> corr = afni.AutoTcorrelate()
>>> corr.inputs.in_file = 'functional.nii'
>>> corr.inputs.out_file = 'my_similarity_matrix.1D'
>>> corr.inputs.polort = -1
>>> corr.inputs.eta2 = True
>>> corr.inputs.mask = 'mask.nii'
>>> corr.inputs.mask_only_targets = True
>>> corr.cmdline
'3dAutoTcorrelate -eta2 -mask mask.nii -mask_only_targets -prefix ...my_similarity_matrix.1D -polort -1 functional.nii'
>>> res = corr.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
timeseries x space (volume or surface) file
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
eta2: (a boolean)
eta^2 similarity
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
mask of voxels
mask_only_targets: (a boolean)
use mask only on targets voxels
out_file: (a file name, nipype default value: %s_similarity_matrix.1D)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
polort: (an integer)
Remove polynomical trend of order m or -1 for no detrending
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 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
[Optional]
apply_mask: (a file name)
output file from 3dAutomask
apply_suffix: (a string)
out_file suffix
args: (a string)
Additional parameters to the command
brain_file: (a file name, nipype default value: %s_masked)
output file from 3dAutomask
clfrac: (a float)
sets the clip level fraction (must be 0.1-0.9). A small value will tend to make the mask
larger [default = 0.5].
dilate: (an integer)
dilate the mask outwards
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
erode: (an integer)
erode the mask inwards
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask_suffix: (a string)
out_file suffix
out_file: (a file name, nipype default value: %s_mask)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
brain_file: (an existing file name)
brain file (skull stripped)
out_file: (an existing file name)
mask 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
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
-mask dset = use dset as mask to include/exclude voxels
min: (a boolean)
print the minimum value in dataset
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
Outputs:
min_val: (a float)
output
Wraps command 3dcalc
This program does voxel-by-voxel arithmetic on 3D datasets
For complete details, see the 3dcalc Documentation.
>>> from nipype.interfaces import afni as afni
>>> calc = afni.Calc()
>>> calc.inputs.in_file_a = 'functional.nii'
>>> calc.inputs.in_file_b = 'functional2.nii'
>>> calc.inputs.expr='a*b'
>>> calc.inputs.out_file = 'functional_calc.nii.gz'
>>> calc.inputs.outputtype = "NIFTI"
>>> calc.cmdline
'3dcalc -a functional.nii -b functional2.nii -expr "a*b" -prefix functional_calc.nii.gz'
Inputs:
[Mandatory]
expr: (a string)
expr
in_file_a: (an existing file name)
input file to 3dcalc
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
in_file_b: (an existing file name)
operand file to 3dcalc
out_file: (a file name, nipype default value: %s_calc)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
single_idx: (an integer)
volume index for in_file_a
start_idx: (an integer)
start index for in_file_a
requires: stop_idx
stop_idx: (an integer)
stop index for in_file_a
requires: start_idx
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dcopy
Copies an image of one type to an image of the same or different type using 3dcopy command
For complete details, see the 3dcopy Documentation.
>>> from nipype.interfaces import afni as afni
>>> copy = afni.Copy()
>>> copy.inputs.in_file = 'functional.nii'
>>> copy.inputs.out_file = 'new_func.nii'
>>> res = copy.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dcopy
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_copy)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dDespike
Removes ‘spikes’ from the 3D+time input dataset
For complete details, see the 3dDespike Documentation.
>>> from nipype.interfaces import afni as afni
>>> despike = afni.Despike()
>>> despike.inputs.in_file = 'functional.nii'
>>> res = despike.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dDespike
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_despike)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dDetrend
This program removes components from voxel time series using linear least squares
For complete details, see the 3dDetrend Documentation.
>>> from nipype.interfaces import afni as afni
>>> detrend = afni.Detrend()
>>> detrend.inputs.in_file = 'functional.nii'
>>> detrend.inputs.args = '-polort 2'
>>> res = detrend.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dDetrend
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_detrend)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dfim+
Program to calculate the cross-correlation of an ideal reference waveform with the measured FMRI time series for each voxel
For complete details, see the 3dfim+ Documentation.
>>> from nipype.interfaces import afni as afni
>>> fim = afni.Fim()
>>> fim.inputs.in_file = 'functional.nii'
>>> fim.inputs.ideal_file= 'seed.1D'
>>> fim.inputs.out_file = 'functional_corr.nii'
>>> fim.inputs.out = 'Correlation'
>>> fim.inputs.fim_thr = 0.0009
>>> res = fim.run()
Inputs:
[Mandatory]
ideal_file: (an existing file name)
ideal time series file name
in_file: (an existing file name)
input file to 3dfim+
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
fim_thr: (a float)
fim internal mask threshold value
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out: (a string)
Flag to output the specified parameter
out_file: (a file name, nipype default value: %s_fim)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dFourier
Program to lowpass and/or highpass each voxel time series in a dataset, via the FFT
For complete details, see the 3dFourier Documentation.
>>> from nipype.interfaces import afni as afni
>>> fourier = afni.Fourier()
>>> fourier.inputs.in_file = 'functional.nii'
>>> fourier.inputs.args = '-retrend'
>>> fourier.inputs.highpass = 0.005
>>> fourier.inputs.lowpass = 0.1
>>> res = fourier.run()
Inputs:
[Mandatory]
highpass: (a float)
highpass
in_file: (an existing file name)
input file to 3dFourier
lowpass: (a float)
lowpass
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_fourier)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dmaskave
Computes average of all voxels in the input dataset which satisfy the criterion in the options list
For complete details, see the 3dmaskave Documentation.
>>> from nipype.interfaces import afni as afni
>>> maskave = afni.Maskave()
>>> maskave.inputs.in_file = 'functional.nii'
>>> maskave.inputs.mask= 'seed_mask.nii'
>>> maskave.inputs.quiet= True
>>> maskave.cmdline
'3dmaskave -mask seed_mask.nii -quiet functional.nii > functional_maskave.1D'
>>> res = maskave.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dmaskave
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
matrix to align input file
out_file: (a file name, nipype default value: %s_maskave.1D)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
quiet: (a boolean)
matrix to align input file
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dmerge
Merge or edit volumes using AFNI 3dmerge command
For complete details, see the 3dmerge Documentation.
>>> from nipype.interfaces import afni as afni
>>> merge = afni.Merge()
>>> merge.inputs.in_files = ['functional.nii', 'functional2.nii']
>>> merge.inputs.blurfwhm = 4
>>> merge.inputs.doall = True
>>> merge.inputs.out_file = 'e7.nii'
>>> res = merge.run()
Inputs:
[Mandatory]
in_files: (an existing file name)
[Optional]
args: (a string)
Additional parameters to the command
blurfwhm: (an integer)
FWHM blur value (mm)
doall: (a boolean)
apply options to all sub-bricks in dataset
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_merge)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dROIstats
Display statistics over masked regions
For complete details, see the 3dROIstats Documentation.
>>> from nipype.interfaces import afni as afni
>>> roistats = afni.ROIStats()
>>> roistats.inputs.in_file = 'functional.nii'
>>> roistats.inputs.mask = 'skeleton_mask.nii.gz'
>>> roistats.inputs.quiet=True
>>> res = roistats.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dROIstats
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
mask: (an existing file name)
input mask
mask_f2short: (a boolean)
Tells the program to convert a float mask to short integers, by simple rounding.
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
quiet: (a boolean)
execute quietly
Outputs:
stats: (an existing file name)
output
Wraps command 3drefit
Changes some of the information inside a 3D dataset’s header
For complete details, see the `3drefit Documentation. <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3drefit.html>
>>> from nipype.interfaces import afni as afni
>>> refit = afni.Refit()
>>> refit.inputs.in_file = 'structural.nii'
>>> refit.inputs.deoblique=True
>>> res = refit.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3drefit
[Optional]
args: (a string)
Additional parameters to the command
deoblique: (a boolean)
replace current transformation matrix with cardinal matrix
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_refit)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
xorigin: (a string)
x distance for edge voxel offset
yorigin: (a string)
y distance for edge voxel offset
zorigin: (a string)
z distance for edge voxel offset
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dresample
Resample or reorient an image using AFNI 3dresample command
For complete details, see the 3dresample Documentation.
>>> from nipype.interfaces import afni as afni
>>> resample = afni.Resample()
>>> resample.inputs.in_file = 'functional.nii'
>>> resample.inputs.orientation= 'RPI'
>>> res = resample.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dresample
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
orientation: (a string)
new orientation code
out_file: (a file name, nipype default value: %s_resample)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
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
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_skullstrip)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dTcat
Concatenate sub-bricks from input datasets into one big 3D+time dataset
For complete details, see the 3dTcat Documentation.
>>> from nipype.interfaces import afni as afni
>>> tcat = afni.TCat()
>>> tcat.inputs.in_files = ['functional.nii', 'functional2.nii']
>>> tcat.inputs.out_file= 'functional_tcat.nii'
>>> tcat.inputs.rlt = '+'
>>> res = tcat.run()
Inputs:
[Mandatory]
in_files: (an existing file name)
input file to 3dTcat
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_tcat)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
rlt: (a string)
options
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 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]
xset: (an existing file name)
input xset
yset: (an existing file name)
input yset
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_tcorr)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
pearson: (a boolean)
Correlation is the normal Pearson correlation coefficient
polort: (an integer)
Remove polynomical trend of order m
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dTshift
Shifts voxel time series from input so that seperate slices are aligned to the same temporal origin
For complete details, see the `3dTshift Documentation. <http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTshift.html>
>>> from nipype.interfaces import afni as afni
>>> tshift = afni.TShift()
>>> tshift.inputs.in_file = 'functional.nii'
>>> tshift.inputs.tpattern = 'alt+z'
>>> tshift.inputs.tzero = 0.0
>>> tshift.cmdline
'3dTshift -prefix functional_tshift+orig.BRIK -tpattern alt+z -tzero 0.0 functional.nii'
>>> res = tshift.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dTShift
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore: (an integer)
ignore the first set of points specified
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
interp: ('Fourier' or 'linear' or 'cubic' or 'quintic' or 'heptic')
different interpolation methods (see 3dTShift for details) default = Fourier
out_file: (a file name, nipype default value: %s_tshift)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
rlt: (a boolean)
Before shifting, remove the mean and linear trend
rltplus: (a boolean)
Before shifting, remove the mean and linear trend and later put back the mean
suffix: (a string)
output image suffix
tpattern: ('alt+z' or 'alt+z2' or 'alt-z' or 'alt-z2' or 'seq+z' or 'seq-z')
use specified slice time pattern rather than one in header
tr: (a string)
manually set the TRYou can attach suffix "s" for seconds or "ms" for milliseconds.
tslice: (an integer)
align each slice to time offset of given slice
mutually_exclusive: tzero
tzero: (a float)
align each slice to given time offset
mutually_exclusive: tslice
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dTstat
Compute voxel-wise statistics using AFNI 3dTstat command
For complete details, see the 3dTstat Documentation.
>>> from nipype.interfaces import afni as afni
>>> tstat = afni.TStat()
>>> tstat.inputs.in_file = 'functional.nii'
>>> tstat.inputs.args= '-mean'
>>> res = tstat.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dTstat
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s_tstat)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command to3d
Create a 3D dataset from 2D image files using AFNI to3d command
For complete details, see the to3d Documentation
>>> from nipype.interfaces import afni
>>> To3D = afni.To3D()
>>> To3D.inputs.datatype = 'float'
>>> To3D.inputs.infolder = 'dicomdir'
>>> To3D.inputs.filetype = "anat"
>>> To3D.inputs.outputtype = "NIFTI"
>>> To3D.cmdline
'to3d -datum float -anat -prefix dicomdir.nii dicomdir/*.dcm'
>>> res = To3D.run()
Inputs:
[Mandatory]
in_folder: (an existing directory name)
folder with DICOM images to convert
mutually_exclusive: infolder, in_folder
infolder: (an existing directory name)
folder with DICOM images to convert
mutually_exclusive: infolder, in_folder
[Optional]
args: (a string)
Additional parameters to the command
assumemosaic: (a boolean)
assume that Siemens image is mosaic
datatype: ('short' or 'float' or 'byte' or 'complex')
set output file datatype
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
filetype: ('spgr' or 'fse' or 'epan' or 'anat' or 'ct' or 'spct' or 'pet' or 'mra' or
'bmap' or 'diff' or 'omri' or 'abuc' or 'fim' or 'fith' or 'fico' or 'fitt' or 'fift'
or 'fizt' or 'fict' or 'fibt' or 'fibn' or 'figt' or 'fipt' or 'fbuc')
type of datafile being converted
funcparams: (a string)
parameters for functional data
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
out_file: (a file name, nipype default value: %s)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
skipoutliers: (a boolean)
skip the outliers check
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dvolreg
Register input volumes to a base volume using AFNI 3dvolreg command
For complete details, see the 3dvolreg Documentation.
>>> from nipype.interfaces import afni as afni
>>> volreg = afni.Volreg()
>>> volreg.inputs.in_file = 'functional.nii'
>>> volreg.inputs.args = '-Fourier -twopass'
>>> volreg.inputs.zpad = 4
>>> volreg.inputs.outputtype = "NIFTI"
>>> volreg.cmdline
'3dvolreg -Fourier -twopass -1Dfile functional.1D -prefix functional_volreg.nii -zpad 4 functional.nii'
>>> res = volreg.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dvolreg
[Optional]
args: (a string)
Additional parameters to the command
basefile: (an existing file name)
base file for registration
copyorigin: (a boolean)
copy base file origin coords to output
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
md1dfile: (a file name)
max displacement output file
oned_file: (a file name, nipype default value: %s.1D)
1D movement parameters output file
out_file: (a file name, nipype default value: %s_volreg)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
timeshift: (a boolean)
time shift to mean slice time offset
verbose: (a boolean)
more detailed description of the process
zpad: (an integer)
Zeropad around the edges by 'n' voxels during rotations
Outputs:
md1d_file: (an existing file name)
max displacement info file
oned_file: (an existing file name)
movement parameters info file
out_file: (an existing file name)
registered file
Wraps command 3dWarp
Use 3dWarp for spatially transforming a dataset
For complete details, see the 3dWarp Documentation.
>>> from nipype.interfaces import afni as afni
>>> warp = afni.Warp()
>>> warp.inputs.in_file = 'structural.nii'
>>> warp.inputs.deoblique = True
>>> res = warp.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dWarp
[Optional]
args: (a string)
Additional parameters to the command
deoblique: (a boolean)
transform dataset from oblique to cardinal
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
gridset: (an existing file name)
copy grid of specified dataset
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
interp: ('linear' or 'cubic' or 'NN' or 'quintic')
spatial interpolation methods [default = linear]
matparent: (an existing file name)
apply transformation from 3dWarpDrive
mni2tta: (a boolean)
transform dataset from MNI152 to Talaraich
out_file: (a file name, nipype default value: %s_warp)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string, nipype default value: _warp)
out_file suffix
tta2mni: (a boolean)
transform dataset from Talairach to MNI152
zpad: (an integer)
pad input dataset with N planes of zero on all sides.
Outputs:
out_file: (an existing file name)
output file
Wraps command 3dZcutup
Cut z-slices from a volume using AFNI 3dZcutup command
For complete details, see the 3dZcutup Documentation.
>>> from nipype.interfaces import afni as afni
>>> zcutup = afni.ZCutUp()
>>> zcutup.inputs.in_file = 'functional.nii'
>>> zcutup.inputs.out_file = 'functional_zcutup.nii'
>>> zcutup.inputs.keep= '0 10'
>>> res = zcutup.run()
Inputs:
[Mandatory]
in_file: (an existing file name)
input file to 3dZcutup
[Optional]
args: (a string)
Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
are a value of type 'str', nipype default value: {})
Environment variables
ignore_exception: (a boolean, nipype default value: False)
Print an error message instead of throwing an exception in case the interface fails to
run
keep: (a string)
slice range to keep in output
out_file: (a file name, nipype default value: %s_zcupup)
output image file name
outputtype: ('NIFTI_GZ' or 'AFNI' or 'NIFTI')
AFNI output filetype
prefix: (a string)
output image prefix
suffix: (a string)
output image suffix
Outputs:
out_file: (an existing file name)
output file