# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
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"""
Head-Motion Estimation and Correction (HMC) of BOLD images
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. autofunction:: init_bold_hmc_wf

"""

from nipype.interfaces import fsl
from nipype.interfaces import utility as niu
from nipype.pipeline import engine as pe


def init_bold_hmc_wf(mem_gb: float, omp_nthreads: int, name: str = 'bold_hmc_wf'):
    """
    Build a workflow to estimate head-motion parameters.

    This workflow estimates the motion parameters to perform
    :abbr:`HMC (head motion correction)` over the input
    :abbr:`BOLD (blood-oxygen-level dependent)` image.

    Workflow Graph
        .. workflow::
            :graph2use: orig
            :simple_form: yes

            from fmriprep.workflows.bold import init_bold_hmc_wf
            wf = init_bold_hmc_wf(
                mem_gb=3,
                omp_nthreads=1)

    Parameters
    ----------
    mem_gb : :obj:`float`
        Size of BOLD file in GB
    omp_nthreads : :obj:`int`
        Maximum number of threads an individual process may use
    name : :obj:`str`
        Name of workflow (default: ``bold_hmc_wf``)

    Inputs
    ------
    bold_file
        BOLD series NIfTI file
    raw_ref_image
        Reference image to which BOLD series is motion corrected

    Outputs
    -------
    xforms
        ITKTransform file aligning each volume to ``ref_image``

    """
    from niworkflows.engine.workflows import LiterateWorkflow as Workflow
    from niworkflows.interfaces.itk import MCFLIRT2ITK

    workflow = Workflow(name=name)
    workflow.__desc__ = """\
Head-motion parameters with respect to the BOLD reference
(transformation matrices, and six corresponding rotation and translation
parameters) are estimated before any spatiotemporal filtering using
`mcflirt` [FSL {fsl_ver}, @mcflirt].
""".format(fsl_ver=fsl.Info().version() or '<ver>')

    inputnode = pe.Node(
        niu.IdentityInterface(fields=['bold_file', 'raw_ref_image']), name='inputnode'
    )
    outputnode = pe.Node(niu.IdentityInterface(fields=['xforms']), name='outputnode')

    # Head motion correction (hmc)
    mcflirt = pe.Node(fsl.MCFLIRT(save_mats=True), name='mcflirt', mem_gb=mem_gb * 3)

    fsl2itk = pe.Node(MCFLIRT2ITK(), name='fsl2itk', mem_gb=0.05, n_procs=omp_nthreads)

    workflow.connect([
        (inputnode, mcflirt, [('raw_ref_image', 'ref_file'),
                              ('bold_file', 'in_file')]),
        (inputnode, fsl2itk, [('raw_ref_image', 'in_source'),
                              ('raw_ref_image', 'in_reference')]),
        (mcflirt, fsl2itk, [('mat_file', 'in_files')]),
        (fsl2itk, outputnode, [('out_file', 'xforms')]),
    ])  # fmt:skip

    return workflow
