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# workflows.smri.niftyreg.groupwise

## `create_groupwise_average()`

Link to code

Create the overall workflow that embeds all the rigid, affine and non-linear components.

Inputs:

    inputspec.in_files - The input files to be registered
    inputspec.ref_file - The initial reference image that the input files
                          are registered to
    inputspec.rmask_file - Mask of the reference image
    inputspec.in_trans_files - Initial transformation files (affine or
                                cpps)

Outputs:

    outputspec.average_image - The average image
    outputspec.cpp_files - The bspline transformation files

### Example

    >>> from nipype.workflows.smri.niftyreg import create_groupwise_average
    >>> node = create_groupwise_average('groupwise_av')
    >>> node.inputs.inputspec.in_files = [
    ...     'file1.nii.gz', 'file2.nii.gz']
    >>> node.inputs.inputspec.ref_file = ['ref.nii.gz']
    >>> node.inputs.inputspec.rmask_file = ['mask.nii.gz']
    >>> node.run()

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## `create_linear_gw_step()`

Link to code

Creates a workflow that performs linear co-registration of a set of images using RegAladin, producing an average image and a set of affine transformation matrices linking each of the floating images to the average.

Inputs:

    inputspec.in_files - The input files to be registered
    inputspec.ref_file - The initial reference image that the input files
                          are registered to
    inputspec.rmask_file - Mask of the reference image
    inputspec.in_aff_files - Initial affine transformation files

Outputs:

    outputspec.average_image - The average image
    outputspec.aff_files - The affine transformation files

Optional arguments:

    linear_options_hash - An options dictionary containing a list of
                          parameters for RegAladin that take
    the same form as given in the interface (default None)
    demean - Selects whether to demean the transformation matrices when
             performing the averaging (default True)
    initial_affines - Selects whether to iterate over initial affine
                      images, which we generally won't have (default False)

### Example

    >>> from nipype.workflows.smri.niftyreg import create_linear_gw_step
    >>> lgw = create_linear_gw_step('my_linear_coreg')
    >>> lgw.inputs.inputspec.in_files = [
    ...     'file1.nii.gz', 'file2.nii.gz']
    >>> lgw.inputs.inputspec.ref_file = ['ref.nii.gz']
    >>> lgw.run()

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## `create_nonlinear_gw_step()`

Link to code

Creates a workflow that perform non-linear co-registrations of a set of images using RegF3d, producing an non-linear average image and a set of cpp transformation linking each of the floating images to the average.

Inputs:

    inputspec.in_files - The input files to be registered
    inputspec.ref_file - The initial reference image that the input files
                          are registered to
    inputspec.rmask_file - Mask of the reference image
    inputspec.in_trans_files - Initial transformation files (affine or
                                cpps)

Outputs:

    outputspec.average_image - The average image
    outputspec.cpp_files - The bspline transformation files

Optional arguments:

    nonlinear_options_hash - An options dictionary containing a list of
                             parameters for RegAladin that take the
    same form as given in the interface (default None)
    initial_affines - Selects whether to iterate over initial affine
                      images, which we generally won't have (default False)

### Example

    >>> from nipype.workflows.smri.niftyreg import create_nonlinear_gw_step
    >>> nlc = create_nonlinear_gw_step('nonlinear_coreg')
    >>> nlc.inputs.inputspec.in_files = [
    ...     'file1.nii.gz', 'file2.nii.gz']
    >>> nlc.inputs.inputspec.ref_file = ['ref.nii.gz']
    >>> nlc.run()

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