.. AUTO-GENERATED FILE -- DO NOT EDIT!

nipype.interfaces.fsl.dti
=========================


:class:`BEDPOSTX`
-----------------


Wraps command **bedpostx**

Deprecated! Please use create_bedpostx_pipeline instead

Example
~~~~~~~

>>> from nipype.interfaces import fsl
>>> bedp = fsl.BEDPOSTX(bpx_directory='subjdir', bvecs='bvecs', bvals='bvals', dwi='diffusion.nii',     mask='mask.nii', fibres=1)
>>> bedp.cmdline
'bedpostx subjdir -n 1'

Inputs:: 

	[Mandatory]
	bvals : (an existing file name)
		b values file
	bvecs : (an existing file name)
		b vectors file
	dwi : (an existing file name)
		diffusion weighted image data file
	mask : (an existing file name)
		bet binary mask file

	[Optional]
	args : (a string)
		Additional parameters to the command
	bpx_directory : (a directory name)
		the name for this subjects bedpostx folder
	burn_period : (an integer)
		burnin period
	environ : (a dictionary with keys which are a value of type 'str' and with values which are a value of type 'str')
		Environment variables
	fibres : (an integer)
		number of fibres per voxel
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	jumps : (an integer)
		number of jumps
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type
	sampling : (an integer)
		sample every
	weight : (a float)
		ARD weight, more weight means less secondary fibres per voxel


Outputs:: 

	bpx_out_directory : (an existing directory name)
		path/name of directory with all bedpostx output files for this subject
	dyads : (a list of items which are a file name)
		a list of path/name of mean of PDD distribution in vector form
	mean_fsamples : (a list of items which are a file name)
		a list of path/name of 3D volume with mean of distribution on f anisotropy
	mean_phsamples : (a list of items which are a file name)
		a list of path/name of 3D volume with mean of distribution on phi
	mean_thsamples : (a list of items which are a file name)
		a list of path/name of 3D volume with mean of distribution on theta
	merged_fsamples : (a list of items which are a file name)
		a list of path/name of 4D volume with samples from the distribution on anisotropic volume fraction
	merged_phsamples : (a list of items which are a file name)
		a list of path/name of file with samples from the distribution on phi
	merged_thsamples : (a list of items which are a file name)
		a list of path/name of 4D volume with samples from the distribution on theta
	xfms_directory : (an existing directory name)
		path/name of directory with the tranformation matrices

:class:`DTIFit`
---------------


Wraps command **dtifit**

Use FSL  dtifit command for fitting a diffusion tensor model at each
voxel

Example
~~~~~~~

>>> from nipype.interfaces import fsl
>>> dti = fsl.DTIFit()
>>> dti.inputs.dwi = 'diffusion.nii'
>>> dti.inputs.bvecs = 'bvecs'
>>> dti.inputs.bvals = 'bvals'
>>> dti.inputs.base_name = 'TP'
>>> dti.inputs.mask = 'mask.nii'
>>> dti.cmdline
'dtifit -k diffusion.nii -o TP -m mask.nii -r bvecs -b bvals'

Inputs:: 

	[Mandatory]
	bvals : (an existing file name)
		b values file
	bvecs : (an existing file name)
		b vectors file
	dwi : (an existing file name)
		diffusion weighted image data file
	mask : (an existing file name)
		bet binary mask file

	[Optional]
	args : (a string)
		Additional parameters to the command
	base_name : (a string)
		base_name that all output files will start with
	cni : (an existing file name)
		input counfound regressors
	environ : (a dictionary with keys which are a value of type 'str' and with values which are a value of type 'str')
		Environment variables
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	little_bit : (a boolean)
		only process small area of brain
	max_x : (an integer)
		max x
	max_y : (an integer)
		max y
	max_z : (an integer)
		max z
	min_x : (an integer)
		min x
	min_y : (an integer)
		min y
	min_z : (an integer)
		min z
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type
	save_tensor : (a boolean)
		save the elements of the tensor
	sse : (a boolean)
		output sum of squared errors


Outputs:: 

	FA : (an existing file name)
		path/name of file with the fractional anisotropy
	L1 : (an existing file name)
		path/name of file with the 1st eigenvalue
	L2 : (an existing file name)
		path/name of file with the 2nd eigenvalue
	L3 : (an existing file name)
		path/name of file with the 3rd eigenvalue
	MD : (an existing file name)
		path/name of file with the mean diffusivity
	MO : (an existing file name)
		path/name of file with the mode of anisotropy
	S0 : (an existing file name)
		path/name of file with the raw T2 signal with no diffusion weighting
	V1 : (an existing file name)
		path/name of file with the 1st eigenvector
	V2 : (an existing file name)
		path/name of file with the 2nd eigenvector
	V3 : (an existing file name)
		path/name of file with the 3rd eigenvector
	tensor : (an existing file name)
		path/name of file with the 4D tensor volume

:class:`DistanceMap`
--------------------


Wraps command **distancemap**

Use FSL's distancemap to generate a map of the distance to the nearest nonzero voxel.

Examples
~~~~~~~~

import nipype.interfaces.fsl as fsl
mapper = fsl.DistanceMap()
mapper.inputs.in_file = "skeleton_mask.nii.gz"
mapper.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	in_file : (an existing file name)
		image to calculate distance values for

	[Optional]
	args : (a string)
		Additional parameters to the command
	distance_map : (a file name)
		distance map to write
	environ : (a dictionary with keys which are a value of type 'str' and with values which are a value of type 'str')
		Environment variables
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	invert_input : (a boolean)
		invert input image
	local_max_file : (a boolean or a file name)
		write an image of the local maxima
	mask_file : (an existing file name)
		binary mask to contrain calculations
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type


Outputs:: 

	distance_map : (an existing file name)
		value is distance to nearest nonzero voxels
	local_max_file : (a file name)
		image of local maxima

:class:`EddyCorrect`
--------------------


Wraps command **eddy_correct**

Deprecated! Please use create_eddy_correct_pipeline instead

Example
~~~~~~~

>>> from nipype.interfaces import fsl
>>> eddyc = fsl.EddyCorrect(in_file='diffusion.nii',out_file="diffusion_edc.nii", ref_num=0)
>>> eddyc.cmdline
'eddy_correct diffusion.nii diffusion_edc.nii 0'

Inputs:: 

	[Mandatory]
	in_file : (an existing file name)
		4D input file
	ref_num : (an integer)
		reference number

	[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')
		Environment variables
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	out_file : (a file name)
		4D output file
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type


Outputs:: 

	eddy_corrected : (an existing file name)
		path/name of 4D eddy corrected output file

:class:`FindTheBiggest`
-----------------------


Wraps command **find_the_biggest**

Use FSL find_the_biggest for performing hard segmentation on
the outputs of connectivity-based thresholding in probtrack.
For complete details, see the `FDT
Documentation. <http://www.fmrib.ox.ac.uk/fsl/fdt/fdt_biggest.html>`_

Example
~~~~~~~

>>> from nipype.interfaces import fsl
>>> ldir = ['seeds_to_M1.nii', 'seeds_to_M2.nii']
>>> fBig = fsl.FindTheBiggest(in_files=ldir, out_file='biggestSegmentation')
>>> fBig.cmdline
'find_the_biggest seeds_to_M1.nii seeds_to_M2.nii biggestSegmentation'

Inputs:: 

	[Mandatory]
	in_files : (a list of items which are a file name)
		a list of input volumes or a singleMatrixFile

	[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')
		Environment variables
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	out_file : (a file name)
		file with the resulting segmentation
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type


Outputs:: 

	out_file : (an existing file name)
		output file indexed in order of input files

:class:`MakeDyadicVectors`
--------------------------


Wraps command **make_dyadic_vectors**

Create vector volume representing mean principal diffusion direction
and its uncertainty (dispersion)

Inputs:: 

	[Mandatory]
	phi_vol : (an existing file name)
		Unknown
	theta_vol : (an existing file name)
		Unknown

	[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')
		Environment variables
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	mask : (an existing file name)
		Unknown
	output : (a file name)
		Unknown
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type
	perc : (a float)
		the {perc}% angle of the output cone of uncertainty (output will be in degrees)


Outputs:: 

	dispersion : (an existing file name)
		Unknown
	dyads : (an existing file name)
		Unknown

:class:`ProbTrackX`
-------------------


Wraps command **probtrackx**

Use FSL  probtrackx for tractography on bedpostx results

Examples
~~~~~~~~

>>> from nipype.interfaces import fsl
>>> pbx = fsl.ProbTrackX(samples_base_name='merged', mask='mask.nii',     seed='MASK_average_thal_right.nii', mode='seedmask',     xfm='trans.mat', n_samples=3, n_steps=10, force_dir=True, opd=True, os2t=True,     target_masks = ['targets_MASK1.nii','targets_MASK2.nii'],     thsamples='merged_thsamples.nii', fsamples='merged_fsamples.nii', phsamples='merged_phsamples.nii',     out_dir='.')
>>> pbx.cmdline
'probtrackx --forcedir -m mask.nii --mode=seedmask --nsamples=3 --nsteps=10 --opd --os2t --dir=. --samples=merged --seed=MASK_average_thal_right.nii --targetmasks=targets.txt --xfm=trans.mat'

Inputs:: 

	[Mandatory]
	fsamples : (an existing file name)
		Unknown
	mask : (an existing file name)
		bet binary mask file in diffusion space
	phsamples : (an existing file name)
		Unknown
	seed : (an existing file name or a list of items which are an existing file name or a list of items which are a list of from 3 to 3 items which are an integer)
		seed volume(s), or voxel(s)or freesurfer label file
	thsamples : (an existing file name)
		Unknown

	[Optional]
	args : (a string)
		Additional parameters to the command
	avoid_mp : (an existing file name)
		reject pathways passing through locations given by this mask
	c_thresh : (a float)
		curvature threshold - default=0.2
	correct_path_distribution : (a boolean)
		correct path distribution for the length of the pathways
	dist_thresh : (a float)
		discards samples shorter than this threshold (in mm - default=0)
	environ : (a dictionary with keys which are a value of type 'str' and with values which are a value of type 'str')
		Environment variables
	fibst : (an integer)
		force a starting fibre for tracking - default=1, i.e. first fibre orientation. Only works if randfib==0
	force_dir : (a boolean)
		use the actual directory name given - i.e. do not add + to make a new directory
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	inv_xfm : (a file name)
		transformation matrix taking DTI space to seed space (compulsory when using a warp_field for seeds_to_dti)
	loop_check : (a boolean)
		perform loop_checks on paths - slower, but allows lower curvature threshold
	mask2 : (an existing file name)
		second bet binary mask (in diffusion space) in twomask_symm mode
	mesh : (an existing file name)
		Freesurfer-type surface descriptor (in ascii format)
	mod_euler : (a boolean)
		use modified euler streamlining
	mode : ('simple' or 'two_mask_symm' or 'seedmask')
		options: simple (single seed voxel), seedmask (mask of seed voxels),twomask_symm (two bet binary masks) 
	n_samples : (an integer)
		number of samples - default=5000
	n_steps : (an integer)
		number of steps per sample - default=2000
	network : (a boolean)
		activate network mode - only keep paths going through at least one seed mask (required if multiple seed masks)
	opd : (a boolean)
		outputs path distributions
	os2t : (a boolean)
		Outputs seeds to targets
	out_dir : (an existing directory name)
		directory to put the final volumes in
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type
	rand_fib : (0 or 1 or 2 or 3)
		options: 0 - default, 1 - to randomly sample initial fibres (with f > fibthresh), 2 - to sample in proportion fibres (with f>fibthresh) to f, 3 - to sample ALL populations at random (even if f<fibthresh)
	random_seed : (a boolean)
		random seed
	s2tastext : (a boolean)
		output seed-to-target counts as a text file (useful when seeding from a mesh)
	sample_random_points : (a boolean)
		sample random points within seed voxels
	samples_base_name : (a string)
		the rootname/base_name for samples files
	seed_ref : (an existing file name)
		reference vol to define seed space in simple mode - diffusion space assumed if absent
	step_length : (a float)
		step_length in mm - default=0.5
	stop_mask : (an existing file name)
		stop tracking at locations given by this mask file
	target_masks : (a file name)
		list of target masks - required for seeds_to_targets classification
	use_anisotropy : (a boolean)
		use anisotropy to constrain tracking
	verbose : (0 or 1 or 2)
		Verbose level, [0-2]
	waypoints : (an existing file name)
		waypoint mask or ascii list of waypoint masks - only keep paths going through ALL the masks
	xfm : (an existing file name)
		transformation matrix taking seed space to DTI space (either FLIRT matrix or FNIRT warp_field) - default is identity


Outputs:: 

	fdt_paths : (an existing file name)
		path/name of a 3D image file containing the output connectivity distribution to the seed mask
	log : (an existing file name)
		path/name of a text record of the command that was run
	particle_files : (a list of items which are a file name)
		Unknown
	targets : (a list of items which are a file name)
		a list with all generated seeds_to_target files
	way_total : (an existing file name)
		path/name of a text file containing a single number corresponding to the total number of generated tracts that have not been rejected by inclusion/exclusion mask criteria

:class:`ProjThresh`
-------------------


Wraps command **proj_thresh**

Use FSL proj_thresh for thresholding some outputs of probtrack
For complete details, see the FDT Documentation
<http://www.fmrib.ox.ac.uk/fsl/fdt/fdt_thresh.html>

Example
~~~~~~~

>>> from nipype.interfaces import fsl
>>> ldir = ['seeds_to_M1.nii', 'seeds_to_M2.nii']
>>> pThresh = fsl.ProjThresh(in_files=ldir,threshold=3)
>>> pThresh.cmdline
'proj_thresh seeds_to_M1.nii seeds_to_M2.nii 3'

Inputs:: 

	[Mandatory]
	in_files : (a list of items which are a file name)
		a list of input volumes
	threshold : (an integer)
		threshold indicating minimum number of seed voxels entering this mask region

	[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')
		Environment variables
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type


Outputs:: 

	out_files : (a list of items which are a file name)
		path/name of output volume after thresholding

:class:`TractSkeleton`
----------------------


Wraps command **tbss_skeleton**

Use FSL's tbss_skeleton to skeletonise an FA image or project arbitrary values onto a skeleton.

There are two ways to use this interface.  To create a skeleton from an FA image, just
supply the ``in_file`` and set ``skeleton_file`` to True (or specify a skeleton filename.
To project values onto a skeleton, you must set ``project_data`` to True, and then also
supply values for ``threshold``, ``distance_map``, and ``data_file``. The ``search_mask_file``
and ``use_cingulum_mask`` inputs are also used in data projection, but ``use_cingulum_mask``
is set to True by default.  This mask controls where the projection algorithm searches
within a circular space around a tract, rather than in a single perpindicular direction.

Examples
~~~~~~~~

import nipype.interfaces.fsl as fsl
skeletor = fsl.TractSkeleton()
skeletor.inputs.in_file = "all_FA.nii.gz"
skeletor.inputs.skeleton_file = True
skeletor.run() # doctest: +SKIP

Inputs:: 

	[Mandatory]
	in_file : (an existing file name)
		input image (typcially mean FA volume)

	[Optional]
	alt_data_file : (an existing file name)
		4D non-FA data to project onto skeleton
	alt_skeleton : (an existing file name)
		alternate skeleton to use
	args : (a string)
		Additional parameters to the command
	data_file : (an existing file name)
		4D data to project onto skeleton (usually FA)
	distance_map : (an existing file name)
		distance map image
	environ : (a dictionary with keys which are a value of type 'str' and with values which are a value of type 'str')
		Environment variables
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type
	project_data : (a boolean)
		project data onto skeleton
		requires: threshold,distance_map,data_file
	projected_data : (a file name)
		input data projected onto skeleton
	search_mask_file : (an existing file name)
		mask in which to use alternate search rule
		exclusive: use_cingulum_mask
	skeleton_file : (a boolean or a file name)
		write out skeleton image
	threshold : (a float)
		skeleton threshold value
	use_cingulum_mask : (a boolean)
		perform alternate search using built-in cingulum mask
		exclusive: search_mask_file


Outputs:: 

	projected_data : (a file name)
		input data projected onto skeleton
	skeleton_file : (a file name)
		tract skeleton image

:class:`VecReg`
---------------


Wraps command **vecreg**

Use FSL vecreg for registering vector data
For complete details, see the FDT Documentation
<http://www.fmrib.ox.ac.uk/fsl/fdt/fdt_vecreg.html>

Example
~~~~~~~

>>> from nipype.interfaces import fsl
>>> vreg = fsl.VecReg(in_file='diffusion.nii',                  affine_mat='trans.mat',                  ref_vol='mni.nii',                  out_file='diffusion_vreg.nii')
>>> vreg.cmdline
'vecreg -t trans.mat -i diffusion.nii -o diffusion_vreg.nii -r mni.nii'

Inputs:: 

	[Mandatory]
	in_file : (an existing file name)
		filename for input vector or tensor field
	ref_vol : (an existing file name)
		filename for reference (target) volume

	[Optional]
	affine_mat : (an existing file name)
		filename for affine transformation matrix
	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')
		Environment variables
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	interpolation : ('nearestneighbour' or 'trilinear' or 'sinc' or 'spline')
		interpolation method : nearestneighbour, trilinear (default), sinc or spline
	mask : (an existing file name)
		brain mask in input space
	out_file : (a file name)
		filename for output registered vector or tensor field
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type
	ref_mask : (an existing file name)
		brain mask in output space (useful for speed up of nonlinear reg)
	rotation_mat : (an existing file name)
		filename for secondary affine matrixif set, this will be used for the rotation of the vector/tensor field
	rotation_warp : (an existing file name)
		filename for secondary warp fieldif set, this will be used for the rotation of the vector/tensor field
	warp_field : (an existing file name)
		filename for 4D warp field for nonlinear registration


Outputs:: 

	out_file : (an existing file name)
		path/name of filename for the registered vector or tensor field

:class:`XFibres`
----------------


Wraps command **xfibres**

Perform model parameters estimation for local (voxelwise) diffusion parameters

Inputs:: 

	[Mandatory]
	bvals : (an existing file name)
		Unknown
	bvecs : (an existing file name)
		Unknown
	dwi : (an existing file name)
		Unknown
	mask : (an existing file name)
		Unknown

	[Optional]
	all_ard : (a boolean)
		Turn ARD on on all fibres
		exclusive: no_ard,all_ard
	args : (a string)
		Additional parameters to the command
	burn_in : (an integer >= 0)
		Total num of jumps at start of MCMC to be discarded
	burn_in_no_ard : (an integer >= 0)
		num of burnin jumps before the ard is imposed
	environ : (a dictionary with keys which are a value of type 'str' and with values which are a value of type 'str')
		Environment variables
	force_dir : (a boolean)
		use the actual directory name given - i.e. do not add + to make a new directory
	fudge : (an integer)
		ARD fudge factor
	ignore_exception : (a boolean)
		Print an error message instead of throwing an exception in case the interface fails to run
	logdir : (a directory name)
		Unknown
	model : (an integer)
		Which model to use. 1=mono-exponential (default and required for single shell). 2=continous exponential (for multi-shell experiments)
	n_fibres : (an integer >= 1)
		Maximum nukmber of fibres to fit in each voxel
	n_jumps : (an integer >= 1)
		Num of jumps to be made by MCMC
	no_ard : (a boolean)
		Turn ARD off on all fibres
		exclusive: no_ard,all_ard
	no_spat : (a boolean)
		Initialise with tensor, not spatially
		exclusive: no_spat,non_linear
	non_linear : (a boolean)
		Initialise with nonlinear fitting
		exclusive: no_spat,non_linear
	output_type : ('NIFTI_PAIR' or 'NIFTI_PAIR_GZ' or 'NIFTI_GZ' or 'NIFTI')
		FSL output type
	sample_every : (an integer >= 0)
		Num of jumps for each sample (MCMC)
	seed : (an integer)
		seed for pseudo random number generator
	update_proposal_every : (an integer >= 1)
		Num of jumps for each update to the proposal density std (MCMC)


Outputs:: 

	dyads : (an existing file name)
		Mean of PDD distribution in vector form.
	fsamples : (an existing file name)
		Samples from the distribution on anisotropic volume fraction
	mean_S0samples : (an existing file name)
		Samples from S0 distribution
	mean_dsamples : (an existing file name)
		Mean of distribution on diffusivity d
	mean_fsamples : (an existing file name)
		Mean of distribution on f anisotropy
	phsamples : (an existing file name)
		Samples from the distribution on phi
	thsamples : (an existing file name)
		Samples from the distribution on theta
