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

workflows.dmri.fsl.dti
======================


.. module:: nipype.workflows.dmri.fsl.dti


.. _nipype.workflows.dmri.fsl.dti.bedpostx_parallel:

:func:`bedpostx_parallel`
-------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/workflows/dmri/fsl/dti.py#L127>`__



Does the same as :func:`.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)
>>> 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') # doctest: +SKIP

Inputs::

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

Outputs::

    outputnode wraps all XFibres outputs


Graph
~~~~~

.. graphviz::

	digraph bedpostx_parallel{

	  label="bedpostx_parallel";

	  bedpostx_parallel_inputnode[label="inputnode (utility)"];

	  bedpostx_parallel_slice_dwi[label="slice_dwi (misc)"];

	  bedpostx_parallel_xfibres[label="xfibres (fsl)"];

	  bedpostx_parallel_Make_dyads[label="Make_dyads (fsl)"];

	  bedpostx_parallel_Merge_dyads[label="Merge_dyads (misc)"];

	  bedpostx_parallel_Merge_mean_fsamples[label="Merge_mean_fsamples (misc)"];

	  bedpostx_parallel_outputnode[label="outputnode (utility)"];

	  bedpostx_parallel_inputnode -> bedpostx_parallel_slice_dwi;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_slice_dwi;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_Make_dyads;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_xfibres;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_xfibres;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_Merge_dyads;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_Merge_mean_fsamples;

	  bedpostx_parallel_slice_dwi -> bedpostx_parallel_Merge_dyads;

	  bedpostx_parallel_slice_dwi -> bedpostx_parallel_xfibres;

	  bedpostx_parallel_slice_dwi -> bedpostx_parallel_xfibres;

	  bedpostx_parallel_slice_dwi -> bedpostx_parallel_Merge_mean_fsamples;

	  bedpostx_parallel_xfibres -> bedpostx_parallel_Merge_dyads;

	  bedpostx_parallel_xfibres -> bedpostx_parallel_Merge_mean_fsamples;

	  subgraph cluster_bedpostx_parallel_phsamples {

	      label="phsamples";

	    bedpostx_parallel_phsamples_inputnode[label="inputnode (utility)"];

	    bedpostx_parallel_phsamples_Merge[label="Merge (misc)"];

	    bedpostx_parallel_phsamples_Mean[label="Mean (fsl)"];

	    bedpostx_parallel_phsamples_outputnode[label="outputnode (utility)"];

	    bedpostx_parallel_phsamples_inputnode -> bedpostx_parallel_phsamples_Merge;

	    bedpostx_parallel_phsamples_inputnode -> bedpostx_parallel_phsamples_Merge;

	    bedpostx_parallel_phsamples_inputnode -> bedpostx_parallel_phsamples_Merge;

	    bedpostx_parallel_phsamples_Merge -> bedpostx_parallel_phsamples_Mean;

	    bedpostx_parallel_phsamples_Merge -> bedpostx_parallel_phsamples_outputnode;

	    bedpostx_parallel_phsamples_Mean -> bedpostx_parallel_phsamples_outputnode;

	  }

	  subgraph cluster_bedpostx_parallel_thsamples {

	      label="thsamples";

	    bedpostx_parallel_thsamples_inputnode[label="inputnode (utility)"];

	    bedpostx_parallel_thsamples_Merge[label="Merge (misc)"];

	    bedpostx_parallel_thsamples_Mean[label="Mean (fsl)"];

	    bedpostx_parallel_thsamples_outputnode[label="outputnode (utility)"];

	    bedpostx_parallel_thsamples_inputnode -> bedpostx_parallel_thsamples_Merge;

	    bedpostx_parallel_thsamples_inputnode -> bedpostx_parallel_thsamples_Merge;

	    bedpostx_parallel_thsamples_inputnode -> bedpostx_parallel_thsamples_Merge;

	    bedpostx_parallel_thsamples_Merge -> bedpostx_parallel_thsamples_Mean;

	    bedpostx_parallel_thsamples_Merge -> bedpostx_parallel_thsamples_outputnode;

	    bedpostx_parallel_thsamples_Mean -> bedpostx_parallel_thsamples_outputnode;

	  }

	  bedpostx_parallel_Make_dyads -> bedpostx_parallel_outputnode;

	  subgraph cluster_bedpostx_parallel_fsamples {

	      label="fsamples";

	    bedpostx_parallel_fsamples_inputnode[label="inputnode (utility)"];

	    bedpostx_parallel_fsamples_Merge[label="Merge (misc)"];

	    bedpostx_parallel_fsamples_Mean[label="Mean (fsl)"];

	    bedpostx_parallel_fsamples_outputnode[label="outputnode (utility)"];

	    bedpostx_parallel_fsamples_inputnode -> bedpostx_parallel_fsamples_Merge;

	    bedpostx_parallel_fsamples_inputnode -> bedpostx_parallel_fsamples_Merge;

	    bedpostx_parallel_fsamples_inputnode -> bedpostx_parallel_fsamples_Merge;

	    bedpostx_parallel_fsamples_Merge -> bedpostx_parallel_fsamples_Mean;

	    bedpostx_parallel_fsamples_Merge -> bedpostx_parallel_fsamples_outputnode;

	    bedpostx_parallel_fsamples_Mean -> bedpostx_parallel_fsamples_outputnode;

	  }

	  bedpostx_parallel_Merge_dyads -> bedpostx_parallel_outputnode;

	  bedpostx_parallel_Merge_mean_fsamples -> bedpostx_parallel_outputnode;

	  bedpostx_parallel_slice_dwi -> bedpostx_parallel_fsamples_inputnode;

	  bedpostx_parallel_slice_dwi -> bedpostx_parallel_phsamples_inputnode;

	  bedpostx_parallel_slice_dwi -> bedpostx_parallel_thsamples_inputnode;

	  bedpostx_parallel_thsamples_outputnode -> bedpostx_parallel_Make_dyads;

	  bedpostx_parallel_thsamples_outputnode -> bedpostx_parallel_outputnode;

	  bedpostx_parallel_thsamples_outputnode -> bedpostx_parallel_outputnode;

	  bedpostx_parallel_fsamples_outputnode -> bedpostx_parallel_outputnode;

	  bedpostx_parallel_fsamples_outputnode -> bedpostx_parallel_outputnode;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_fsamples_inputnode;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_thsamples_inputnode;

	  bedpostx_parallel_inputnode -> bedpostx_parallel_phsamples_inputnode;

	  bedpostx_parallel_phsamples_outputnode -> bedpostx_parallel_Make_dyads;

	  bedpostx_parallel_phsamples_outputnode -> bedpostx_parallel_outputnode;

	  bedpostx_parallel_phsamples_outputnode -> bedpostx_parallel_outputnode;

	  bedpostx_parallel_xfibres -> bedpostx_parallel_phsamples_inputnode;

	  bedpostx_parallel_xfibres -> bedpostx_parallel_thsamples_inputnode;

	  bedpostx_parallel_xfibres -> bedpostx_parallel_fsamples_inputnode;

	}


.. _nipype.workflows.dmri.fsl.dti.create_bedpostx_pipeline:

:func:`create_bedpostx_pipeline`
--------------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/workflows/dmri/fsl/dti.py#L19>`__



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() # doctest: +SKIP

Inputs::

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

Outputs::

    outputnode wraps all XFibres outputs


Graph
~~~~~

.. graphviz::

	digraph bedpostx{

	  label="bedpostx";

	  bedpostx_inputnode[label="inputnode (utility)"];

	  bedpostx_slice_msk[label="slice_msk (fsl)"];

	  bedpostx_slice_dwi[label="slice_dwi (fsl)"];

	  bedpostx_mask_dwi[label="mask_dwi (fsl)"];

	  bedpostx_xfibres[label="xfibres (fsl)"];

	  bedpostx_make_dyads[label="make_dyads (fsl)"];

	  bedpostx_outputnode[label="outputnode (utility)"];

	  bedpostx_inputnode -> bedpostx_slice_msk;

	  bedpostx_inputnode -> bedpostx_make_dyads;

	  bedpostx_inputnode -> bedpostx_slice_dwi;

	  bedpostx_inputnode -> bedpostx_xfibres;

	  bedpostx_inputnode -> bedpostx_xfibres;

	  bedpostx_slice_msk -> bedpostx_mask_dwi;

	  bedpostx_slice_dwi -> bedpostx_xfibres;

	  bedpostx_slice_dwi -> bedpostx_mask_dwi;

	  bedpostx_mask_dwi -> bedpostx_xfibres;

	  subgraph cluster_bedpostx_fsamples {

	      label="fsamples";

	    bedpostx_fsamples_inputnode[label="inputnode (utility)"];

	    bedpostx_fsamples_Merge[label="Merge (fsl)"];

	    bedpostx_fsamples_Mean[label="Mean (fsl)"];

	    bedpostx_fsamples_outputnode[label="outputnode (utility)"];

	    bedpostx_fsamples_inputnode -> bedpostx_fsamples_Merge;

	    bedpostx_fsamples_Merge -> bedpostx_fsamples_Mean;

	    bedpostx_fsamples_Merge -> bedpostx_fsamples_outputnode;

	    bedpostx_fsamples_Mean -> bedpostx_fsamples_outputnode;

	  }

	  subgraph cluster_bedpostx_thsamples {

	      label="thsamples";

	    bedpostx_thsamples_inputnode[label="inputnode (utility)"];

	    bedpostx_thsamples_Merge[label="Merge (fsl)"];

	    bedpostx_thsamples_Mean[label="Mean (fsl)"];

	    bedpostx_thsamples_outputnode[label="outputnode (utility)"];

	    bedpostx_thsamples_inputnode -> bedpostx_thsamples_Merge;

	    bedpostx_thsamples_Merge -> bedpostx_thsamples_outputnode;

	    bedpostx_thsamples_Merge -> bedpostx_thsamples_Mean;

	    bedpostx_thsamples_Mean -> bedpostx_thsamples_outputnode;

	  }

	  subgraph cluster_bedpostx_phsamples {

	      label="phsamples";

	    bedpostx_phsamples_inputnode[label="inputnode (utility)"];

	    bedpostx_phsamples_Merge[label="Merge (fsl)"];

	    bedpostx_phsamples_Mean[label="Mean (fsl)"];

	    bedpostx_phsamples_outputnode[label="outputnode (utility)"];

	    bedpostx_phsamples_inputnode -> bedpostx_phsamples_Merge;

	    bedpostx_phsamples_Merge -> bedpostx_phsamples_Mean;

	    bedpostx_phsamples_Merge -> bedpostx_phsamples_outputnode;

	    bedpostx_phsamples_Mean -> bedpostx_phsamples_outputnode;

	  }

	  bedpostx_make_dyads -> bedpostx_outputnode;

	  bedpostx_make_dyads -> bedpostx_outputnode;

	  bedpostx_xfibres -> bedpostx_fsamples_inputnode;

	  bedpostx_xfibres -> bedpostx_thsamples_inputnode;

	  bedpostx_xfibres -> bedpostx_phsamples_inputnode;

	  bedpostx_fsamples_outputnode -> bedpostx_outputnode;

	  bedpostx_fsamples_outputnode -> bedpostx_outputnode;

	  bedpostx_phsamples_outputnode -> bedpostx_outputnode;

	  bedpostx_phsamples_outputnode -> bedpostx_outputnode;

	  bedpostx_phsamples_outputnode -> bedpostx_make_dyads;

	  bedpostx_thsamples_outputnode -> bedpostx_outputnode;

	  bedpostx_thsamples_outputnode -> bedpostx_outputnode;

	  bedpostx_thsamples_outputnode -> bedpostx_make_dyads;

	}


.. _nipype.workflows.dmri.fsl.dti.merge_and_mean:

:func:`merge_and_mean`
----------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/workflows/dmri/fsl/dti.py#L107>`__






Graph
~~~~~

.. graphviz::

	digraph mm{

	  label="mm";

	  mm_inputnode[label="inputnode (utility)"];

	  mm_Merge[label="Merge (fsl)"];

	  mm_Mean[label="Mean (fsl)"];

	  mm_outputnode[label="outputnode (utility)"];

	  mm_inputnode -> mm_Merge;

	  mm_Merge -> mm_outputnode;

	  mm_Merge -> mm_Mean;

	  mm_Mean -> mm_outputnode;

	}


.. _nipype.workflows.dmri.fsl.dti.merge_and_mean_parallel:

:func:`merge_and_mean_parallel`
-------------------------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/workflows/dmri/fsl/dti.py#L233>`__






Graph
~~~~~

.. graphviz::

	digraph mm{

	  label="mm";

	  mm_inputnode[label="inputnode (utility)"];

	  mm_Merge[label="Merge (misc)"];

	  mm_Mean[label="Mean (fsl)"];

	  mm_outputnode[label="outputnode (utility)"];

	  mm_inputnode -> mm_Merge;

	  mm_inputnode -> mm_Merge;

	  mm_inputnode -> mm_Merge;

	  mm_Merge -> mm_Mean;

	  mm_Merge -> mm_outputnode;

	  mm_Mean -> mm_outputnode;

	}


.. _nipype.workflows.dmri.fsl.dti.transpose:

:func:`transpose`
-----------------

`Link to code <http://github.com/nipy/nipype/tree/e63e055194d62d2bdc4665688261c03a42fd0025/nipype/workflows/dmri/fsl/dti.py#L13>`__





