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# workflows.dmri.fsl.dti

## `bedpostx_parallel()`

Link to code

Does the same as `create_bedpostx_pipeline()` by splitting the input dMRI in small ROIs that are better suited for parallel processing).

### Example

    >>> from nipype.workflows.dmri.fsl.dti import bedpostx_parallel
    >>> params = dict(n_fibres = 2, fudge = 1, burn_in = 1000,
    ...               n_jumps = 1250, sample_every = 25)
    >>> bpwf = bedpostx_parallel('nipype_bedpostx_parallel', params=params)
    >>> bpwf.inputs.inputnode.dwi = 'diffusion.nii'
    >>> bpwf.inputs.inputnode.mask = 'mask.nii'
    >>> bpwf.inputs.inputnode.bvecs = 'bvecs'
    >>> bpwf.inputs.inputnode.bvals = 'bvals'
    >>> bpwf.run(plugin='CondorDAGMan')

Inputs:

    inputnode.dwi
    inputnode.mask
    inputnode.bvecs
    inputnode.bvals

Outputs:

    outputnode wraps all XFibres outputs

### Graph

## `create_bedpostx_pipeline()`

Link to code

Creates a pipeline that does the same as bedpostx script from FSL - calculates diffusion model parameters (distributions not MLE) voxelwise for the whole volume (by splitting it slicewise).

### Example

    >>> from nipype.workflows.dmri.fsl.dti import create_bedpostx_pipeline
    >>> params = dict(n_fibres = 2, fudge = 1, burn_in = 1000,
    ...               n_jumps = 1250, sample_every = 25)
    >>> bpwf = create_bedpostx_pipeline('nipype_bedpostx', params)
    >>> bpwf.inputs.inputnode.dwi = 'diffusion.nii'
    >>> bpwf.inputs.inputnode.mask = 'mask.nii'
    >>> bpwf.inputs.inputnode.bvecs = 'bvecs'
    >>> bpwf.inputs.inputnode.bvals = 'bvals'
    >>> bpwf.run()

Inputs:

    inputnode.dwi
    inputnode.mask
    inputnode.bvecs
    inputnode.bvals

Outputs:

    outputnode wraps all XFibres outputs

### Graph

## `merge_and_mean()`

Link to code

### Graph

## `merge_and_mean_parallel()`

Link to code

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## `transpose()`

Link to code