outputs.py

# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Writing out derivative files."""
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu

from ...config import DEFAULT_MEMORY_MIN_GB
from ...interfaces import DerivativesDataSink


def init_func_derivatives_wf(
    bids_root,
    cifti_output,
    freesurfer,
    metadata,
    output_dir,
    spaces,
    use_aroma,
    name='func_derivatives_wf',
):
    """
    Set up a battery of datasinks to store derivatives in the right location.

    Parameters
    ----------
    bids_root : :obj:`str`
        Original BIDS dataset path.
    cifti_output : :obj:`bool`
        Whether the ``--cifti-output`` flag was set.
    freesurfer : :obj:`bool`
        Whether FreeSurfer anatomical processing was run.
    metadata : :obj:`dict`
        Metadata dictionary associated to the BOLD run.
    output_dir : :obj:`str`
        Where derivatives should be written out to.
    spaces : :py:class:`~niworkflows.utils.spaces.SpatialReferences`
        A container for storing, organizing, and parsing spatial normalizations. Composed of
        :py:class:`~niworkflows.utils.spaces.Reference` objects representing spatial references.
        Each ``Reference`` contains a space, which is a string of either TemplateFlow template IDs
        (e.g., ``MNI152Lin``, ``MNI152NLin6Asym``, ``MNIPediatricAsym``), nonstandard references
        (e.g., ``T1w`` or ``anat``, ``sbref``, ``run``, etc.), or a custom template located in
        the TemplateFlow root directory. Each ``Reference`` may also contain a spec, which is a
        dictionary with template specifications (e.g., a specification of ``{'resolution': 2}``
        would lead to resampling on a 2mm resolution of the space).
    use_aroma : :obj:`bool`
        Whether ``--use-aroma`` flag was set.
    name : :obj:`str`
        This workflow's identifier (default: ``func_derivatives_wf``).

    """
    from niworkflows.engine.workflows import LiterateWorkflow as Workflow
    from niworkflows.interfaces.utility import KeySelect
    from smriprep.workflows.outputs import _bids_relative

    nonstd_spaces = set(spaces.get_nonstandard())
    workflow = Workflow(name=name)

    inputnode = pe.Node(niu.IdentityInterface(fields=[
        'aroma_noise_ics', 'bold_aparc_std', 'bold_aparc_t1', 'bold_aseg_std',
        'bold_aseg_t1', 'bold_cifti', 'bold_mask_std', 'bold_mask_t1', 'bold_std',
        'bold_std_ref', 'bold_t1', 'bold_t1_ref', 'bold_native', 'bold_native_ref',
        'bold_mask_native', 'cifti_variant', 'cifti_metadata', 'cifti_density',
        'confounds', 'confounds_metadata', 'melodic_mix', 'nonaggr_denoised_file',
        'source_file', 'surf_files', 'surf_refs', 'template', 'spatial_reference']),
        name='inputnode')

    raw_sources = pe.Node(niu.Function(function=_bids_relative), name='raw_sources')
    raw_sources.inputs.bids_root = bids_root

    ds_confounds = pe.Node(DerivativesDataSink(
        base_directory=output_dir, desc='confounds', suffix='regressors',
        dismiss_entities=("echo",)),
        name="ds_confounds", run_without_submitting=True,
        mem_gb=DEFAULT_MEMORY_MIN_GB)
    workflow.connect([
        (inputnode, raw_sources, [('source_file', 'in_files')]),
        (inputnode, ds_confounds, [('source_file', 'source_file'),
                                   ('confounds', 'in_file'),
                                   ('confounds_metadata', 'meta_dict')]),
    ])

    if nonstd_spaces.intersection(('func', 'run', 'bold', 'boldref', 'sbref')):
        ds_bold_native = pe.Node(
            DerivativesDataSink(
                base_directory=output_dir, desc='preproc', compress=True, SkullStripped=False,
                RepetitionTime=metadata.get('RepetitionTime'), TaskName=metadata.get('TaskName'),
                dismiss_entities=("echo",)),
            name='ds_bold_native', run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)
        ds_bold_native_ref = pe.Node(
            DerivativesDataSink(base_directory=output_dir, suffix='boldref', compress=True,
                                dismiss_entities=("echo",)),
            name='ds_bold_native_ref', run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)
        ds_bold_mask_native = pe.Node(
            DerivativesDataSink(base_directory=output_dir, desc='brain', suffix='mask',
                                compress=True, dismiss_entities=("echo",)),
            name='ds_bold_mask_native', run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)

        workflow.connect([
            (inputnode, ds_bold_native, [('source_file', 'source_file'),
                                         ('bold_native', 'in_file')]),
            (inputnode, ds_bold_native_ref, [('source_file', 'source_file'),
                                             ('bold_native_ref', 'in_file')]),
            (inputnode, ds_bold_mask_native, [('source_file', 'source_file'),
                                              ('bold_mask_native', 'in_file')]),
            (raw_sources, ds_bold_mask_native, [('out', 'RawSources')]),
        ])

    # Resample to T1w space
    if nonstd_spaces.intersection(('T1w', 'anat')):
        ds_bold_t1 = pe.Node(
            DerivativesDataSink(
                base_directory=output_dir, space='T1w', desc='preproc', compress=True,
                SkullStripped=False, RepetitionTime=metadata.get('RepetitionTime'),
                TaskName=metadata.get('TaskName'), dismiss_entities=("echo",)),
            name='ds_bold_t1', run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)
        ds_bold_t1_ref = pe.Node(
            DerivativesDataSink(base_directory=output_dir, space='T1w', suffix='boldref',
                                compress=True, dismiss_entities=("echo",)),
            name='ds_bold_t1_ref', run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)

        ds_bold_mask_t1 = pe.Node(
            DerivativesDataSink(base_directory=output_dir, space='T1w', desc='brain',
                                suffix='mask', compress=True, dismiss_entities=("echo",)),
            name='ds_bold_mask_t1', run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)
        workflow.connect([
            (inputnode, ds_bold_t1, [('source_file', 'source_file'),
                                     ('bold_t1', 'in_file')]),
            (inputnode, ds_bold_t1_ref, [('source_file', 'source_file'),
                                         ('bold_t1_ref', 'in_file')]),
            (inputnode, ds_bold_mask_t1, [('source_file', 'source_file'),
                                          ('bold_mask_t1', 'in_file')]),
            (raw_sources, ds_bold_mask_t1, [('out', 'RawSources')]),
        ])
        if freesurfer:
            ds_bold_aseg_t1 = pe.Node(DerivativesDataSink(
                base_directory=output_dir, space='T1w', desc='aseg', suffix='dseg',
                compress=True, dismiss_entities=("echo",)),
                name='ds_bold_aseg_t1', run_without_submitting=True,
                mem_gb=DEFAULT_MEMORY_MIN_GB)
            ds_bold_aparc_t1 = pe.Node(DerivativesDataSink(
                base_directory=output_dir, space='T1w', desc='aparcaseg', suffix='dseg',
                compress=True, dismiss_entities=("echo",)),
                name='ds_bold_aparc_t1', run_without_submitting=True,
                mem_gb=DEFAULT_MEMORY_MIN_GB)
            workflow.connect([
                (inputnode, ds_bold_aseg_t1, [('source_file', 'source_file'),
                                              ('bold_aseg_t1', 'in_file')]),
                (inputnode, ds_bold_aparc_t1, [('source_file', 'source_file'),
                                               ('bold_aparc_t1', 'in_file')]),
            ])

    if use_aroma:
        ds_aroma_noise_ics = pe.Node(DerivativesDataSink(
            base_directory=output_dir, suffix='AROMAnoiseICs', dismiss_entities=("echo",)),
            name="ds_aroma_noise_ics", run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)
        ds_melodic_mix = pe.Node(DerivativesDataSink(
            base_directory=output_dir, desc='MELODIC', suffix='mixing',
            dismiss_entities=("echo",)),
            name="ds_melodic_mix", run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)
        ds_aroma_std = pe.Node(
            DerivativesDataSink(
                base_directory=output_dir, space='MNI152NLin6Asym', desc='smoothAROMAnonaggr',
                compress=True),
            name='ds_aroma_std', run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)

        workflow.connect([
            (inputnode, ds_aroma_noise_ics, [('source_file', 'source_file'),
                                             ('aroma_noise_ics', 'in_file')]),
            (inputnode, ds_melodic_mix, [('source_file', 'source_file'),
                                         ('melodic_mix', 'in_file')]),
            (inputnode, ds_aroma_std, [('source_file', 'source_file'),
                                       ('nonaggr_denoised_file', 'in_file')]),
        ])

    if getattr(spaces, '_cached') is None:
        return workflow

    # Store resamplings in standard spaces when listed in --output-spaces
    if spaces.cached.references:
        from niworkflows.interfaces.space import SpaceDataSource

        spacesource = pe.Node(SpaceDataSource(),
                              name='spacesource', run_without_submitting=True)
        spacesource.iterables = ('in_tuple', [
            (s.fullname, s.spec) for s in spaces.cached.get_standard(dim=(3,))
        ])

        select_std = pe.Node(KeySelect(
            fields=['template', 'bold_std', 'bold_std_ref', 'bold_mask_std']),
            name='select_std', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)

        ds_bold_std = pe.Node(
            DerivativesDataSink(
                base_directory=output_dir, desc='preproc', compress=True, SkullStripped=False,
                RepetitionTime=metadata.get('RepetitionTime'), TaskName=metadata.get('TaskName'),
                dismiss_entities=("echo",)),
            name='ds_bold_std', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
        ds_bold_std_ref = pe.Node(
            DerivativesDataSink(base_directory=output_dir, suffix='boldref', compress=True,
                                dismiss_entities=("echo",)),
            name='ds_bold_std_ref', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
        ds_bold_mask_std = pe.Node(
            DerivativesDataSink(base_directory=output_dir, desc='brain', suffix='mask',
                                compress=True, dismiss_entities=("echo",)),
            name='ds_bold_mask_std', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)

        workflow.connect([
            (inputnode, ds_bold_std, [('source_file', 'source_file')]),
            (inputnode, ds_bold_std_ref, [('source_file', 'source_file')]),
            (inputnode, ds_bold_mask_std, [('source_file', 'source_file')]),
            (inputnode, select_std, [('bold_std', 'bold_std'),
                                     ('bold_std_ref', 'bold_std_ref'),
                                     ('bold_mask_std', 'bold_mask_std'),
                                     ('template', 'template'),
                                     ('spatial_reference', 'keys')]),
            (spacesource, select_std, [('uid', 'key')]),
            (select_std, ds_bold_std, [('bold_std', 'in_file')]),
            (spacesource, ds_bold_std, [('space', 'space'),
                                        ('cohort', 'cohort'),
                                        ('resolution', 'resolution'),
                                        ('density', 'density')]),
            (select_std, ds_bold_std_ref, [('bold_std_ref', 'in_file')]),
            (spacesource, ds_bold_std_ref, [('space', 'space'),
                                            ('cohort', 'cohort'),
                                            ('resolution', 'resolution'),
                                            ('density', 'density')]),
            (select_std, ds_bold_mask_std, [('bold_mask_std', 'in_file')]),
            (spacesource, ds_bold_mask_std, [('space', 'space'),
                                             ('cohort', 'cohort'),
                                             ('resolution', 'resolution'),
                                             ('density', 'density')]),
            (raw_sources, ds_bold_mask_std, [('out', 'RawSources')]),
        ])

        if freesurfer:
            select_fs_std = pe.Node(KeySelect(
                fields=['bold_aseg_std', 'bold_aparc_std', 'template']),
                name='select_fs_std', run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
            ds_bold_aseg_std = pe.Node(DerivativesDataSink(
                base_directory=output_dir, desc='aseg', suffix='dseg', compress=True,
                dismiss_entities=("echo",)),
                name='ds_bold_aseg_std', run_without_submitting=True,
                mem_gb=DEFAULT_MEMORY_MIN_GB)
            ds_bold_aparc_std = pe.Node(DerivativesDataSink(
                base_directory=output_dir, desc='aparcaseg', suffix='dseg', compress=True,
                dismiss_entities=("echo",)),
                name='ds_bold_aparc_std', run_without_submitting=True,
                mem_gb=DEFAULT_MEMORY_MIN_GB)
            workflow.connect([
                (spacesource, select_fs_std, [('uid', 'key')]),
                (inputnode, select_fs_std, [('bold_aseg_std', 'bold_aseg_std'),
                                            ('bold_aparc_std', 'bold_aparc_std'),
                                            ('template', 'template'),
                                            ('spatial_reference', 'keys')]),
                (select_fs_std, ds_bold_aseg_std, [('bold_aseg_std', 'in_file')]),
                (spacesource, ds_bold_aseg_std, [('space', 'space'),
                                                 ('cohort', 'cohort'),
                                                 ('resolution', 'resolution'),
                                                 ('density', 'density')]),
                (select_fs_std, ds_bold_aparc_std, [('bold_aparc_std', 'in_file')]),
                (spacesource, ds_bold_aparc_std, [('space', 'space'),
                                                  ('cohort', 'cohort'),
                                                  ('resolution', 'resolution'),
                                                  ('density', 'density')]),
                (inputnode, ds_bold_aseg_std, [('source_file', 'source_file')]),
                (inputnode, ds_bold_aparc_std, [('source_file', 'source_file')])
            ])

    fs_outputs = spaces.cached.get_fs_spaces()
    if freesurfer and fs_outputs:
        from niworkflows.interfaces.surf import Path2BIDS

        select_fs_surf = pe.Node(KeySelect(
            fields=['surfaces', 'surf_kwargs']), name='select_fs_surf',
            run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)
        select_fs_surf.iterables = [('key', fs_outputs)]
        select_fs_surf.inputs.surf_kwargs = [{'space': s} for s in fs_outputs]

        name_surfs = pe.MapNode(Path2BIDS(pattern=r'(?P<hemi>[lr])h.\w+'),
                                iterfield='in_file', name='name_surfs',
                                run_without_submitting=True)

        ds_bold_surfs = pe.MapNode(DerivativesDataSink(
            base_directory=output_dir, extension="func.gii", dismiss_entities=("echo",)),
            iterfield=['in_file', 'hemi'], name='ds_bold_surfs',
            run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)

        workflow.connect([
            (inputnode, select_fs_surf, [
                ('surf_files', 'surfaces'),
                ('surf_refs', 'keys')]),
            (select_fs_surf, name_surfs, [('surfaces', 'in_file')]),
            (inputnode, ds_bold_surfs, [('source_file', 'source_file')]),
            (select_fs_surf, ds_bold_surfs, [('surfaces', 'in_file'),
                                             ('key', 'space')]),
            (name_surfs, ds_bold_surfs, [('hemi', 'hemi')]),
        ])

    # CIFTI output
    if cifti_output:
        ds_bold_cifti = pe.Node(DerivativesDataSink(
            base_directory=output_dir, suffix='bold', compress=False, dismiss_entities=("echo",)),
            name='ds_bold_cifti', run_without_submitting=True,
            mem_gb=DEFAULT_MEMORY_MIN_GB)
        workflow.connect([
            (inputnode, ds_bold_cifti, [(('bold_cifti', _unlist), 'in_file'),
                                        ('source_file', 'source_file'),
                                        (('cifti_metadata', _get_surface), 'space'),
                                        ('cifti_density', 'density'),
                                        (('cifti_metadata', _read_json), 'meta_dict')])
        ])

    return workflow


def init_bold_preproc_report_wf(mem_gb, reportlets_dir, name='bold_preproc_report_wf'):
    """
    Generate a visual report.

    This workflow generates and saves a reportlet showing the effect of resampling
    the BOLD signal using the standard deviation maps.

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

            from fmriprep.workflows.bold.resampling import init_bold_preproc_report_wf
            wf = init_bold_preproc_report_wf(mem_gb=1, reportlets_dir='.')

    Parameters
    ----------
    mem_gb : :obj:`float`
        Size of BOLD file in GB
    reportlets_dir : :obj:`str`
        Directory in which to save reportlets
    name : :obj:`str`, optional
        Workflow name (default: bold_preproc_report_wf)

    Inputs
    ------
    in_pre
        BOLD time-series, before resampling
    in_post
        BOLD time-series, after resampling
    name_source
        BOLD series NIfTI file
        Used to recover original information lost during processing

    """
    from nipype.algorithms.confounds import TSNR
    from niworkflows.engine.workflows import LiterateWorkflow as Workflow
    from niworkflows.interfaces import SimpleBeforeAfter
    from ...interfaces import DerivativesDataSink

    workflow = Workflow(name=name)

    inputnode = pe.Node(niu.IdentityInterface(
        fields=['in_pre', 'in_post', 'name_source']), name='inputnode')

    pre_tsnr = pe.Node(TSNR(), name='pre_tsnr', mem_gb=mem_gb * 4.5)
    pos_tsnr = pe.Node(TSNR(), name='pos_tsnr', mem_gb=mem_gb * 4.5)

    bold_rpt = pe.Node(SimpleBeforeAfter(), name='bold_rpt',
                       mem_gb=0.1)
    ds_report_bold = pe.Node(
        DerivativesDataSink(base_directory=reportlets_dir, desc='preproc',
                            datatype="figures", dismiss_entities=("echo",)),
        name='ds_report_bold', mem_gb=DEFAULT_MEMORY_MIN_GB,
        run_without_submitting=True
    )

    workflow.connect([
        (inputnode, ds_report_bold, [('name_source', 'source_file')]),
        (inputnode, pre_tsnr, [('in_pre', 'in_file')]),
        (inputnode, pos_tsnr, [('in_post', 'in_file')]),
        (pre_tsnr, bold_rpt, [('stddev_file', 'before')]),
        (pos_tsnr, bold_rpt, [('stddev_file', 'after')]),
        (bold_rpt, ds_report_bold, [('out_report', 'in_file')]),
    ])

    return workflow


def _unlist(in_file):
    while isinstance(in_file, (list, tuple)) and len(in_file) == 1:
        in_file = in_file[0]
    return in_file


def _get_surface(in_file):
    from pathlib import Path
    from json import loads
    return loads(Path(in_file).read_text())["surface"]


def _read_json(in_file):
    from pathlib import Path
    from json import loads
    return loads(Path(in_file).read_text())