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

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


.. _nipype.interfaces.fsl.dti.BEDPOSTX5:


.. index:: BEDPOSTX5

BEDPOSTX5
---------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L415>`__

Wraps the executable command ``bedpostx``.

BEDPOSTX stands for Bayesian Estimation of Diffusion Parameters Obtained
using Sampling Techniques. The X stands for modelling Crossing Fibres.
bedpostx runs Markov Chain Monte Carlo sampling to build up distributions
on diffusion parameters at each voxel. It creates all the files necessary
for running probabilistic tractography. For an overview of the modelling
carried out within bedpostx see this `technical report
<http://www.fmrib.ox.ac.uk/analysis/techrep/tr03tb1/tr03tb1/index.html>`_.


.. note:: Consider using
  :func:`nipype.workflows.fsl.dmri.create_bedpostx_pipeline` instead.


Example
~~~~~~~

>>> from nipype.interfaces import fsl
>>> bedp = fsl.BEDPOSTX5(bvecs='bvecs', bvals='bvals', dwi='diffusion.nii',
...                     mask='mask.nii', n_fibres=1)
>>> bedp.cmdline
'bedpostx bedpostx -b 0 --burnin_noard=0 --forcedir -n 1 -j 5000 -s 1 --updateproposalevery=40'

Inputs::

        [Mandatory]
        dwi: (an existing file name)
                diffusion weighted image data file
        mask: (an existing file name)
                bet binary mask file
        bvecs: (an existing file name)
                b vectors file
        bvals: (an existing file name)
                b values file
        n_fibres: (a long integer >= 1, nipype default value: 2)
                Maximum number of fibres to fit in each voxel
                argument: ``-n %d``
        out_dir: (a directory name, nipype default value: bedpostx)
                output directory
                argument: ``%s``, position: 1

        [Optional]
        logdir: (a directory name)
                argument: ``--logdir=%s``
        model: (1 or 2 or 3)
                use monoexponential (1, default, required for single-shell) or
                multiexponential (2, multi-shell) model
                argument: ``-model %d``
        fudge: (an integer (int or long))
                ARD fudge factor
                argument: ``-w %d``
        n_jumps: (an integer (int or long), nipype default value: 5000)
                Num of jumps to be made by MCMC
                argument: ``-j %d``
        burn_in: (a long integer >= 0, nipype default value: 0)
                Total num of jumps at start of MCMC to be discarded
                argument: ``-b %d``
        sample_every: (a long integer >= 0, nipype default value: 1)
                Num of jumps for each sample (MCMC)
                argument: ``-s %d``
        gradnonlin: (a boolean)
                consider gradient nonlinearities, default off
                argument: ``-g``
        grad_dev: (an existing file name)
                grad_dev file, if gradnonlin, -g is True
        use_gpu: (a boolean)
                Use the GPU version of bedpostx
        burn_in_no_ard: (a long integer >= 0, nipype default value: 0)
                num of burnin jumps before the ard is imposed
                argument: ``--burnin_noard=%d``
        update_proposal_every: (a long integer >= 1, nipype default value:
                  40)
                Num of jumps for each update to the proposal density std (MCMC)
                argument: ``--updateproposalevery=%d``
        seed: (an integer (int or long))
                seed for pseudo random number generator
                argument: ``--seed=%d``
        no_ard: (a boolean)
                Turn ARD off on all fibres
                argument: ``--noard``
                mutually_exclusive: no_ard, all_ard
        all_ard: (a boolean)
                Turn ARD on on all fibres
                argument: ``--allard``
                mutually_exclusive: no_ard, all_ard
        no_spat: (a boolean)
                Initialise with tensor, not spatially
                argument: ``--nospat``
                mutually_exclusive: no_spat, non_linear, cnlinear
        non_linear: (a boolean)
                Initialise with nonlinear fitting
                argument: ``--nonlinear``
                mutually_exclusive: no_spat, non_linear, cnlinear
        cnlinear: (a boolean)
                Initialise with constrained nonlinear fitting
                argument: ``--cnonlinear``
                mutually_exclusive: no_spat, non_linear, cnlinear
        rician: (a boolean)
                use Rician noise modeling
                argument: ``--rician``
        f0_noard: (a boolean)
                Noise floor model: add to the model an unattenuated signal
                compartment f0
                argument: ``--f0``
                mutually_exclusive: f0_noard, f0_ard
        f0_ard: (a boolean)
                Noise floor model: add to the model an unattenuated signal
                compartment f0
                argument: ``--f0 --ardf0``
                mutually_exclusive: f0_noard, f0_ard, all_ard
        force_dir: (a boolean, nipype default value: True)
                use the actual directory name given (do not add + to make a new
                directory)
                argument: ``--forcedir``
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

        mean_dsamples: (an existing file name)
                Mean of distribution on diffusivity d
        mean_fsamples: (a list of items which are an existing file name)
                Mean of distribution on f anisotropy
        mean_S0samples: (an existing file name)
                Mean of distribution on T2wbaseline signal intensity S0
        mean_phsamples: (a list of items which are an existing file name)
                Mean of distribution on phi
        mean_thsamples: (a list of items which are an existing file name)
                Mean of distribution on theta
        merged_thsamples: (a list of items which are an existing file name)
                Samples from the distribution on theta
        merged_phsamples: (a list of items which are an existing file name)
                Samples from the distribution on phi
        merged_fsamples: (a list of items which are an existing file name)
                Samples from the distribution on anisotropic volume fraction
        dyads: (a list of items which are an existing file name)
                Mean of PDD distribution in vector form.
        dyads_dispersion: (a list of items which are an existing file name)
                Dispersion

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.DTIFit:


.. index:: DTIFit

DTIFit
------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L92>`__

Wraps the executable 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]
        dwi: (an existing file name)
                diffusion weighted image data file
                argument: ``-k %s``, position: 0
        mask: (an existing file name)
                bet binary mask file
                argument: ``-m %s``, position: 2
        bvecs: (an existing file name)
                b vectors file
                argument: ``-r %s``, position: 3
        bvals: (an existing file name)
                b values file
                argument: ``-b %s``, position: 4

        [Optional]
        base_name: (a unicode string, nipype default value: dtifit_)
                base_name that all output files will start with
                argument: ``-o %s``, position: 1
        min_z: (an integer (int or long))
                min z
                argument: ``-z %d``
        max_z: (an integer (int or long))
                max z
                argument: ``-Z %d``
        min_y: (an integer (int or long))
                min y
                argument: ``-y %d``
        max_y: (an integer (int or long))
                max y
                argument: ``-Y %d``
        min_x: (an integer (int or long))
                min x
                argument: ``-x %d``
        max_x: (an integer (int or long))
                max x
                argument: ``-X %d``
        save_tensor: (a boolean)
                save the elements of the tensor
                argument: ``--save_tensor``
        sse: (a boolean)
                output sum of squared errors
                argument: ``--sse``
        cni: (an existing file name)
                input counfound regressors
                argument: ``--cni=%s``
        little_bit: (a boolean)
                only process small area of brain
                argument: ``--littlebit``
        gradnonlin: (an existing file name)
                gradient non linearities
                argument: ``--gradnonlin=%s``
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

        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
        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
        FA: (an existing file name)
                path/name of file with the fractional anisotropy
        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
        tensor: (an existing file name)
                path/name of file with the 4D tensor volume
        sse: (an existing file name)
                path/name of file with the summed squared error

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.DistanceMap:


.. index:: DistanceMap

DistanceMap
-----------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L1389>`__

Wraps the executable command ``distancemap``.

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

Example
~~~~~~~

>>> 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
                argument: ``--in=%s``

        [Optional]
        mask_file: (an existing file name)
                binary mask to contrain calculations
                argument: ``--mask=%s``
        invert_input: (a boolean)
                invert input image
                argument: ``--invert``
        local_max_file: (a boolean or a file name)
                write an image of the local maxima
                argument: ``--localmax=%s``
        distance_map: (a file name)
                distance map to write
                argument: ``--out=%s``
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

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

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.FindTheBiggest:


.. index:: FindTheBiggest

FindTheBiggest
--------------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L1195>`__

Wraps the executable 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 an existing file name)
                a list of input volumes or a singleMatrixFile
                argument: ``%s``, position: 0

        [Optional]
        out_file: (a file name)
                file with the resulting segmentation
                argument: ``%s``, position: 2
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

        out_file: (an existing file name)
                output file indexed in order of input files
                argument: ``%s``

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.MakeDyadicVectors:


.. index:: MakeDyadicVectors

MakeDyadicVectors
-----------------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L1460>`__

Wraps the executable command ``make_dyadic_vectors``.

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

Inputs::

        [Mandatory]
        theta_vol: (an existing file name)
                argument: ``%s``, position: 0
        phi_vol: (an existing file name)
                argument: ``%s``, position: 1

        [Optional]
        mask: (an existing file name)
                argument: ``%s``, position: 2
        output: (a file name, nipype default value: dyads)
                argument: ``%s``, position: 3
        perc: (a float)
                the {perc}% angle of the output cone of uncertainty (output will be
                in degrees)
                argument: ``%f``, position: 4
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

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

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.ProbTrackX:


.. index:: ProbTrackX

ProbTrackX
----------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L731>`__

Wraps the executable 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]
        thsamples: (a list of items which are an existing file name)
        phsamples: (a list of items which are an existing file name)
        fsamples: (a list of items which are an existing file name)
        mask: (an existing file name)
                bet binary mask file in diffusion space
                argument: ``-m %s``
        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 (int or long))
                seed volume(s), or voxel(s) or freesurfer label file
                argument: ``--seed=%s``

        [Optional]
        mode: ('simple' or 'two_mask_symm' or 'seedmask')
                options: simple (single seed voxel), seedmask (mask of seed voxels),
                twomask_symm (two bet binary masks)
                argument: ``--mode=%s``
        mask2: (an existing file name)
                second bet binary mask (in diffusion space) in twomask_symm mode
                argument: ``--mask2=%s``
        mesh: (an existing file name)
                Freesurfer-type surface descriptor (in ascii format)
                argument: ``--mesh=%s``
        samples_base_name: (a unicode string, nipype default value: merged)
                the rootname/base_name for samples files
                argument: ``--samples=%s``
        target_masks: (a list of items which are a file name)
                list of target masks - required for seeds_to_targets classification
                argument: ``--targetmasks=%s``
        waypoints: (an existing file name)
                waypoint mask or ascii list of waypoint masks - only keep paths
                going through ALL the masks
                argument: ``--waypoints=%s``
        network: (a boolean)
                activate network mode - only keep paths going through at least one
                seed mask (required if multiple seed masks)
                argument: ``--network``
        seed_ref: (an existing file name)
                reference vol to define seed space in simple mode - diffusion space
                assumed if absent
                argument: ``--seedref=%s``
        out_dir: (an existing directory name)
                directory to put the final volumes in
                argument: ``--dir=%s``
        force_dir: (a boolean, nipype default value: True)
                use the actual directory name given - i.e. do not add + to make a
                new directory
                argument: ``--forcedir``
        opd: (a boolean, nipype default value: True)
                outputs path distributions
                argument: ``--opd``
        correct_path_distribution: (a boolean)
                correct path distribution for the length of the pathways
                argument: ``--pd``
        os2t: (a boolean)
                Outputs seeds to targets
                argument: ``--os2t``
        avoid_mp: (an existing file name)
                reject pathways passing through locations given by this mask
                argument: ``--avoid=%s``
        stop_mask: (an existing file name)
                stop tracking at locations given by this mask file
                argument: ``--stop=%s``
        xfm: (an existing file name)
                transformation matrix taking seed space to DTI space (either FLIRT
                matrix or FNIRT warp_field) - default is identity
                argument: ``--xfm=%s``
        inv_xfm: (a file name)
                transformation matrix taking DTI space to seed space (compulsory
                when using a warp_field for seeds_to_dti)
                argument: ``--invxfm=%s``
        n_samples: (an integer (int or long), nipype default value: 5000)
                number of samples - default=5000
                argument: ``--nsamples=%d``
        n_steps: (an integer (int or long))
                number of steps per sample - default=2000
                argument: ``--nsteps=%d``
        dist_thresh: (a float)
                discards samples shorter than this threshold (in mm - default=0)
                argument: ``--distthresh=%.3f``
        c_thresh: (a float)
                curvature threshold - default=0.2
                argument: ``--cthr=%.3f``
        sample_random_points: (a boolean)
                sample random points within seed voxels
                argument: ``--sampvox``
        step_length: (a float)
                step_length in mm - default=0.5
                argument: ``--steplength=%.3f``
        loop_check: (a boolean)
                perform loop_checks on paths - slower, but allows lower curvature
                threshold
                argument: ``--loopcheck``
        use_anisotropy: (a boolean)
                use anisotropy to constrain tracking
                argument: ``--usef``
        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)
                argument: ``--randfib=%d``
        fibst: (an integer (int or long))
                force a starting fibre for tracking - default=1, i.e. first fibre
                orientation. Only works if randfib==0
                argument: ``--fibst=%d``
        mod_euler: (a boolean)
                use modified euler streamlining
                argument: ``--modeuler``
        random_seed: (a boolean)
                random seed
                argument: ``--rseed``
        s2tastext: (a boolean)
                output seed-to-target counts as a text file (useful when seeding
                from a mesh)
                argument: ``--s2tastext``
        verbose: (0 or 1 or 2)
                Verbose level, [0-2]. Level 2 is required to output particle files.
                argument: ``--verbose=%d``
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

        log: (an existing file name)
                path/name of a text record of the command that was run
        fdt_paths: (a list of items which are an existing file name)
                path/name of a 3D image file containing the output connectivity
                distribution to the seed mask
        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
        targets: (a list of items which are an existing file name)
                a list with all generated seeds_to_target files
        particle_files: (a list of items which are an existing file name)
                Files describing all of the tract samples. Generated only if verbose
                is set to 2

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.ProbTrackX2:


.. index:: ProbTrackX2

ProbTrackX2
-----------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L956>`__

Wraps the executable command ``probtrackx2``.

Use FSL  probtrackx2 for tractography on bedpostx results

Examples
~~~~~~~~

>>> from nipype.interfaces import fsl
>>> pbx2 = fsl.ProbTrackX2()
>>> pbx2.inputs.seed = 'seed_source.nii.gz'
>>> pbx2.inputs.thsamples = 'merged_th1samples.nii.gz'
>>> pbx2.inputs.fsamples = 'merged_f1samples.nii.gz'
>>> pbx2.inputs.phsamples = 'merged_ph1samples.nii.gz'
>>> pbx2.inputs.mask = 'nodif_brain_mask.nii.gz'
>>> pbx2.inputs.out_dir = '.'
>>> pbx2.inputs.n_samples = 3
>>> pbx2.inputs.n_steps = 10
>>> pbx2.cmdline
'probtrackx2 --forcedir -m nodif_brain_mask.nii.gz --nsamples=3 --nsteps=10 --opd --dir=. --samples=merged --seed=seed_source.nii.gz'

Inputs::

        [Mandatory]
        thsamples: (a list of items which are an existing file name)
        phsamples: (a list of items which are an existing file name)
        fsamples: (a list of items which are an existing file name)
        mask: (an existing file name)
                bet binary mask file in diffusion space
                argument: ``-m %s``
        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 (int or long))
                seed volume(s), or voxel(s) or freesurfer label file
                argument: ``--seed=%s``

        [Optional]
        simple: (a boolean)
                rack from a list of voxels (seed must be a ASCII list of
                coordinates)
                argument: ``--simple``
        fopd: (an existing file name)
                Other mask for binning tract distribution
                argument: ``--fopd=%s``
        waycond: ('OR' or 'AND')
                Waypoint condition. Either "AND" (default) or "OR"
                argument: ``--waycond=%s``
        wayorder: (a boolean)
                Reject streamlines that do not hit waypoints in given order. Only
                valid if waycond=AND
                argument: ``--wayorder``
        onewaycondition: (a boolean)
                Apply waypoint conditions to each half tract separately
                argument: ``--onewaycondition``
        omatrix1: (a boolean)
                Output matrix1 - SeedToSeed Connectivity
                argument: ``--omatrix1``
        distthresh1: (a float)
                Discards samples (in matrix1) shorter than this threshold (in mm -
                default=0)
                argument: ``--distthresh1=%.3f``
        omatrix2: (a boolean)
                Output matrix2 - SeedToLowResMask
                argument: ``--omatrix2``
                requires: target2
        target2: (an existing file name)
                Low resolution binary brain mask for storing connectivity
                distribution in matrix2 mode
                argument: ``--target2=%s``
        omatrix3: (a boolean)
                Output matrix3 (NxN connectivity matrix)
                argument: ``--omatrix3``
                requires: target3, lrtarget3
        target3: (an existing file name)
                Mask used for NxN connectivity matrix (or Nxn if lrtarget3 is set)
                argument: ``--target3=%s``
        lrtarget3: (an existing file name)
                Column-space mask used for Nxn connectivity matrix
                argument: ``--lrtarget3=%s``
        distthresh3: (a float)
                Discards samples (in matrix3) shorter than this threshold (in mm -
                default=0)
                argument: ``--distthresh3=%.3f``
        omatrix4: (a boolean)
                Output matrix4 - DtiMaskToSeed (special Oxford Sparse Format)
                argument: ``--omatrix4``
        colmask4: (an existing file name)
                Mask for columns of matrix4 (default=seed mask)
                argument: ``--colmask4=%s``
        target4: (an existing file name)
                Brain mask in DTI space
                argument: ``--target4=%s``
        meshspace: ('caret' or 'freesurfer' or 'first' or 'vox')
                Mesh reference space - either "caret" (default) or "freesurfer" or
                "first" or "vox"
                argument: ``--meshspace=%s``
        samples_base_name: (a unicode string, nipype default value: merged)
                the rootname/base_name for samples files
                argument: ``--samples=%s``
        target_masks: (a list of items which are a file name)
                list of target masks - required for seeds_to_targets classification
                argument: ``--targetmasks=%s``
        waypoints: (an existing file name)
                waypoint mask or ascii list of waypoint masks - only keep paths
                going through ALL the masks
                argument: ``--waypoints=%s``
        network: (a boolean)
                activate network mode - only keep paths going through at least one
                seed mask (required if multiple seed masks)
                argument: ``--network``
        seed_ref: (an existing file name)
                reference vol to define seed space in simple mode - diffusion space
                assumed if absent
                argument: ``--seedref=%s``
        out_dir: (an existing directory name)
                directory to put the final volumes in
                argument: ``--dir=%s``
        force_dir: (a boolean, nipype default value: True)
                use the actual directory name given - i.e. do not add + to make a
                new directory
                argument: ``--forcedir``
        opd: (a boolean, nipype default value: True)
                outputs path distributions
                argument: ``--opd``
        correct_path_distribution: (a boolean)
                correct path distribution for the length of the pathways
                argument: ``--pd``
        os2t: (a boolean)
                Outputs seeds to targets
                argument: ``--os2t``
        avoid_mp: (an existing file name)
                reject pathways passing through locations given by this mask
                argument: ``--avoid=%s``
        stop_mask: (an existing file name)
                stop tracking at locations given by this mask file
                argument: ``--stop=%s``
        xfm: (an existing file name)
                transformation matrix taking seed space to DTI space (either FLIRT
                matrix or FNIRT warp_field) - default is identity
                argument: ``--xfm=%s``
        inv_xfm: (a file name)
                transformation matrix taking DTI space to seed space (compulsory
                when using a warp_field for seeds_to_dti)
                argument: ``--invxfm=%s``
        n_samples: (an integer (int or long), nipype default value: 5000)
                number of samples - default=5000
                argument: ``--nsamples=%d``
        n_steps: (an integer (int or long))
                number of steps per sample - default=2000
                argument: ``--nsteps=%d``
        dist_thresh: (a float)
                discards samples shorter than this threshold (in mm - default=0)
                argument: ``--distthresh=%.3f``
        c_thresh: (a float)
                curvature threshold - default=0.2
                argument: ``--cthr=%.3f``
        sample_random_points: (a boolean)
                sample random points within seed voxels
                argument: ``--sampvox``
        step_length: (a float)
                step_length in mm - default=0.5
                argument: ``--steplength=%.3f``
        loop_check: (a boolean)
                perform loop_checks on paths - slower, but allows lower curvature
                threshold
                argument: ``--loopcheck``
        use_anisotropy: (a boolean)
                use anisotropy to constrain tracking
                argument: ``--usef``
        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)
                argument: ``--randfib=%d``
        fibst: (an integer (int or long))
                force a starting fibre for tracking - default=1, i.e. first fibre
                orientation. Only works if randfib==0
                argument: ``--fibst=%d``
        mod_euler: (a boolean)
                use modified euler streamlining
                argument: ``--modeuler``
        random_seed: (a boolean)
                random seed
                argument: ``--rseed``
        s2tastext: (a boolean)
                output seed-to-target counts as a text file (useful when seeding
                from a mesh)
                argument: ``--s2tastext``
        verbose: (0 or 1 or 2)
                Verbose level, [0-2]. Level 2 is required to output particle files.
                argument: ``--verbose=%d``
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

        network_matrix: (an existing file name)
                the network matrix generated by --omatrix1 option
        matrix1_dot: (an existing file name)
                Output matrix1.dot - SeedToSeed Connectivity
        lookup_tractspace: (an existing file name)
                lookup_tractspace generated by --omatrix2 option
        matrix2_dot: (an existing file name)
                Output matrix2.dot - SeedToLowResMask
        matrix3_dot: (an existing file name)
                Output matrix3 - NxN connectivity matrix
        log: (an existing file name)
                path/name of a text record of the command that was run
        fdt_paths: (a list of items which are an existing file name)
                path/name of a 3D image file containing the output connectivity
                distribution to the seed mask
        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
        targets: (a list of items which are an existing file name)
                a list with all generated seeds_to_target files
        particle_files: (a list of items which are an existing file name)
                Files describing all of the tract samples. Generated only if verbose
                is set to 2

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.ProjThresh:


.. index:: ProjThresh

ProjThresh
----------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L1139>`__

Wraps the executable 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 an existing file name)
                a list of input volumes
                argument: ``%s``, position: 0
        threshold: (an integer (int or long))
                threshold indicating minimum number of seed voxels entering this
                mask region
                argument: ``%d``, position: 1

        [Optional]
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

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

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.TractSkeleton:


.. index:: TractSkeleton

TractSkeleton
-------------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L1281>`__

Wraps the executable 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.

Example
~~~~~~~

>>> 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)
                argument: ``-i %s``

        [Optional]
        project_data: (a boolean)
                project data onto skeleton
                argument: ``-p %.3f %s %s %s %s``
                requires: threshold, distance_map, data_file
        threshold: (a float)
                skeleton threshold value
        distance_map: (an existing file name)
                distance map image
        search_mask_file: (an existing file name)
                mask in which to use alternate search rule
                mutually_exclusive: use_cingulum_mask
        use_cingulum_mask: (a boolean, nipype default value: True)
                perform alternate search using built-in cingulum mask
                mutually_exclusive: search_mask_file
        data_file: (an existing file name)
                4D data to project onto skeleton (usually FA)
        alt_data_file: (an existing file name)
                4D non-FA data to project onto skeleton
                argument: ``-a %s``
        alt_skeleton: (an existing file name)
                alternate skeleton to use
                argument: ``-s %s``
        projected_data: (a file name)
                input data projected onto skeleton
        skeleton_file: (a boolean or a file name)
                write out skeleton image
                argument: ``-o %s``
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

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

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.VecReg:


.. index:: VecReg

VecReg
------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L1070>`__

Wraps the executable 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
                argument: ``-i %s``
        ref_vol: (an existing file name)
                filename for reference (target) volume
                argument: ``-r %s``

        [Optional]
        out_file: (a file name)
                filename for output registered vector or tensor field
                argument: ``-o %s``
        affine_mat: (an existing file name)
                filename for affine transformation matrix
                argument: ``-t %s``
        warp_field: (an existing file name)
                filename for 4D warp field for nonlinear registration
                argument: ``-w %s``
        rotation_mat: (an existing file name)
                filename for secondary affine matrix if set, this will be used for
                the rotation of the vector/tensor field
                argument: ``--rotmat=%s``
        rotation_warp: (an existing file name)
                filename for secondary warp field if set, this will be used for the
                rotation of the vector/tensor field
                argument: ``--rotwarp=%s``
        interpolation: ('nearestneighbour' or 'trilinear' or 'sinc' or
                  'spline')
                interpolation method : nearestneighbour, trilinear (default), sinc
                or spline
                argument: ``--interp=%s``
        mask: (an existing file name)
                brain mask in input space
                argument: ``-m %s``
        ref_mask: (an existing file name)
                brain mask in output space (useful for speed up of nonlinear reg)
                argument: ``--refmask=%s``
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

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

References:
~~~~~~~~~~~
None

.. _nipype.interfaces.fsl.dti.XFibres5:


.. index:: XFibres5

XFibres5
--------

`Link to code <file:///build/nipype-1.1.9/nipype/interfaces/fsl/dti.py#L525>`__

Wraps the executable command ``xfibres``.

Perform model parameters estimation for local (voxelwise) diffusion
parameters

Inputs::

        [Mandatory]
        dwi: (an existing file name)
                diffusion weighted image data file
                argument: ``--data=%s``
        mask: (an existing file name)
                brain binary mask file (i.e. from BET)
                argument: ``--mask=%s``
        bvecs: (an existing file name)
                b vectors file
                argument: ``--bvecs=%s``
        bvals: (an existing file name)
                b values file
                argument: ``--bvals=%s``
        n_fibres: (a long integer >= 1, nipype default value: 2)
                Maximum number of fibres to fit in each voxel
                argument: ``--nfibres=%d``

        [Optional]
        gradnonlin: (an existing file name)
                gradient file corresponding to slice
                argument: ``--gradnonlin=%s``
        logdir: (a directory name, nipype default value: .)
                argument: ``--logdir=%s``
        model: (1 or 2 or 3)
                use monoexponential (1, default, required for single-shell) or
                multiexponential (2, multi-shell) model
                argument: ``--model=%d``
        fudge: (an integer (int or long))
                ARD fudge factor
                argument: ``--fudge=%d``
        n_jumps: (an integer (int or long), nipype default value: 5000)
                Num of jumps to be made by MCMC
                argument: ``--njumps=%d``
        burn_in: (a long integer >= 0, nipype default value: 0)
                Total num of jumps at start of MCMC to be discarded
                argument: ``--burnin=%d``
        burn_in_no_ard: (a long integer >= 0, nipype default value: 0)
                num of burnin jumps before the ard is imposed
                argument: ``--burnin_noard=%d``
        sample_every: (a long integer >= 0, nipype default value: 1)
                Num of jumps for each sample (MCMC)
                argument: ``--sampleevery=%d``
        update_proposal_every: (a long integer >= 1, nipype default value:
                  40)
                Num of jumps for each update to the proposal density std (MCMC)
                argument: ``--updateproposalevery=%d``
        seed: (an integer (int or long))
                seed for pseudo random number generator
                argument: ``--seed=%d``
        no_ard: (a boolean)
                Turn ARD off on all fibres
                argument: ``--noard``
                mutually_exclusive: no_ard, all_ard
        all_ard: (a boolean)
                Turn ARD on on all fibres
                argument: ``--allard``
                mutually_exclusive: no_ard, all_ard
        no_spat: (a boolean)
                Initialise with tensor, not spatially
                argument: ``--nospat``
                mutually_exclusive: no_spat, non_linear, cnlinear
        non_linear: (a boolean)
                Initialise with nonlinear fitting
                argument: ``--nonlinear``
                mutually_exclusive: no_spat, non_linear, cnlinear
        cnlinear: (a boolean)
                Initialise with constrained nonlinear fitting
                argument: ``--cnonlinear``
                mutually_exclusive: no_spat, non_linear, cnlinear
        rician: (a boolean)
                use Rician noise modeling
                argument: ``--rician``
        f0_noard: (a boolean)
                Noise floor model: add to the model an unattenuated signal
                compartment f0
                argument: ``--f0``
                mutually_exclusive: f0_noard, f0_ard
        f0_ard: (a boolean)
                Noise floor model: add to the model an unattenuated signal
                compartment f0
                argument: ``--f0 --ardf0``
                mutually_exclusive: f0_noard, f0_ard, all_ard
        force_dir: (a boolean, nipype default value: True)
                use the actual directory name given (do not add + to make a new
                directory)
                argument: ``--forcedir``
        output_type: ('NIFTI' or 'NIFTI_PAIR' or 'NIFTI_GZ' or
                  'NIFTI_PAIR_GZ')
                FSL output type
        args: (a unicode string)
                Additional parameters to the command
                argument: ``%s``
        environ: (a dictionary with keys which are a bytes or None or a value
                  of class 'str' and with values which are a bytes or None or a
                  value of class 'str', nipype default value: {})
                Environment variables

Outputs::

        dyads: (a list of items which are an existing file name)
                Mean of PDD distribution in vector form.
        fsamples: (a list of items which are an existing file name)
                Samples from the distribution on f anisotropy
        mean_dsamples: (an existing file name)
                Mean of distribution on diffusivity d
        mean_fsamples: (a list of items which are an existing file name)
                Mean of distribution on f anisotropy
        mean_S0samples: (an existing file name)
                Mean of distribution on T2wbaseline signal intensity S0
        mean_tausamples: (an existing file name)
                Mean of distribution on tau samples (only with rician noise)
        phsamples: (a list of items which are an existing file name)
                phi samples, per fiber
        thsamples: (a list of items which are an existing file name)
                theta samples, per fiber

References:
~~~~~~~~~~~
None
