def qap_anatomical_spatial_workflow(workflow, resource_pool, config, report=False):
    # resource pool should have:
    #     anatomical_reorient
    #     qap_head_mask
    #     anatomical_gm_mask
    #     anatomical_wm_mask
    #     anatomical_csf_mask

    import os
    import sys

    import nipype.interfaces.io as nio
    import nipype.pipeline.engine as pe
    import nipype.interfaces.utility as niu
    import nipype.algorithms.misc as nam
    from qap_workflows_utils import qap_anatomical_spatial
    from qap.viz.interfaces import PlotMosaic

    if "qap_head_mask" not in resource_pool.keys():

        from qap_workflows import qap_mask_workflow

        workflow, resource_pool = qap_mask_workflow(workflow, resource_pool, config)

    if (
        ("anatomical_gm_mask" not in resource_pool.keys())
        or ("anatomical_wm_mask" not in resource_pool.keys())
        or ("anatomical_csf_mask" not in resource_pool.keys())
    ):

        from anatomical_preproc import segmentation_workflow

        workflow, resource_pool = segmentation_workflow(workflow, resource_pool, config)

    if "anatomical_reorient" not in resource_pool.keys():
        from anatomical_preproc import anatomical_reorient_workflow

        workflow, resource_pool = anatomical_reorient_workflow(workflow, resource_pool, config)

    spatial = pe.Node(
        niu.Function(
            input_names=[
                "anatomical_reorient",
                "head_mask_path",
                "anatomical_gm_mask",
                "anatomical_wm_mask",
                "anatomical_csf_mask",
                "subject_id",
                "session_id",
                "scan_id",
                "site_name",
            ],
            output_names=["qc"],
            function=qap_anatomical_spatial,
        ),
        name="qap_anatomical_spatial",
    )

    # Subject infos
    spatial.inputs.subject_id = config["subject_id"]
    spatial.inputs.session_id = config["session_id"]
    spatial.inputs.scan_id = config["scan_id"]

    if "site_name" in config.keys():
        spatial.inputs.site_name = config["site_name"]

    if len(resource_pool["anatomical_reorient"]) == 2:
        node, out_file = resource_pool["anatomical_reorient"]
        workflow.connect(node, out_file, spatial, "anatomical_reorient")
    else:
        spatial.inputs.anatomical_reorient = resource_pool["anatomical_reorient"]

    if len(resource_pool["qap_head_mask"]) == 2:
        node, out_file = resource_pool["qap_head_mask"]
        workflow.connect(node, out_file, spatial, "head_mask_path")
    else:
        spatial.inputs.head_mask_path = resource_pool["qap_head_mask"]

    if len(resource_pool["anatomical_gm_mask"]) == 2:
        node, out_file = resource_pool["anatomical_gm_mask"]
        workflow.connect(node, out_file, spatial, "anatomical_gm_mask")
    else:
        spatial.inputs.anatomical_gm_mask = resource_pool["anatomical_gm_mask"]

    if len(resource_pool["anatomical_wm_mask"]) == 2:
        node, out_file = resource_pool["anatomical_wm_mask"]
        workflow.connect(node, out_file, spatial, "anatomical_wm_mask")
    else:
        spatial.inputs.anatomical_wm_mask = resource_pool["anatomical_wm_mask"]

    if len(resource_pool["anatomical_csf_mask"]) == 2:
        node, out_file = resource_pool["anatomical_csf_mask"]
        workflow.connect(node, out_file, spatial, "anatomical_csf_mask")
    else:
        spatial.inputs.anatomical_csf_mask = resource_pool["anatomical_csf_mask"]

    if config.get("write_report", False):
        plot = pe.Node(PlotMosaic(), name="plot_mosaic")
        plot.inputs.subject = config["subject_id"]

        metadata = [config["session_id"], config["scan_id"]]
        if "site_name" in config.keys():
            metadata.append(config["site_name"])

        plot.inputs.metadata = metadata
        plot.inputs.title = "Anatomical reoriented"

        if len(resource_pool["anatomical_reorient"]) == 2:
            node, out_file = resource_pool["anatomical_reorient"]
            workflow.connect(node, out_file, plot, "in_file")
        else:
            plot.inputs.in_file = resource_pool["anatomical_reorient"]

        # Enable this if we want masks
        # if len(resource_pool['qap_head_mask']) == 2:
        #     node, out_file = resource_pool['qap_head_mask']
        #     workflow.connect(node, out_file, plot, 'in_mask')
        # else:
        #     plot.inputs.in_mask = resource_pool['qap_head_mask']

        resource_pool["qap_mosaic"] = (plot, "out_file")

    out_csv = op.join(config["output_directory"], "qap_anatomical_spatial.csv")
    spatial_to_csv = pe.Node(nam.AddCSVRow(in_file=out_csv), name="qap_anatomical_spatial_to_csv")

    workflow.connect(spatial, "qc", spatial_to_csv, "_outputs")
    resource_pool["qap_anatomical_spatial"] = (spatial_to_csv, "csv_file")
    return workflow, resource_pool
def qap_anatomical_spatial_workflow(workflow, resource_pool, config):

    # resource pool should have:
    #     anatomical_reorient
    #     qap_head_mask
    #     anatomical_gm_mask
    #     anatomical_wm_mask
    #     anatomical_csf_mask

    import os
    import sys

    import nipype.interfaces.io as nio
    import nipype.pipeline.engine as pe

    import nipype.interfaces.utility as util

    from qap_workflows_utils import qap_anatomical_spatial, write_to_csv

    if "qap_head_mask" not in resource_pool.keys():

        from qap_workflows import qap_mask_workflow

        workflow, resource_pool = qap_mask_workflow(workflow, resource_pool, config)

    if (
        ("anatomical_gm_mask" not in resource_pool.keys())
        or ("anatomical_wm_mask" not in resource_pool.keys())
        or ("anatomical_csf_mask" not in resource_pool.keys())
    ):

        from anatomical_preproc import segmentation_workflow

        workflow, resource_pool = segmentation_workflow(workflow, resource_pool, config)

    if "anatomical_reorient" not in resource_pool.keys():

        from anatomical_preproc import anatomical_reorient_workflow

        workflow, resource_pool = anatomical_reorient_workflow(workflow, resource_pool, config)

    spatial = pe.Node(
        util.Function(
            input_names=[
                "anatomical_reorient",
                "head_mask_path",
                "anatomical_gm_mask",
                "anatomical_wm_mask",
                "anatomical_csf_mask",
                "subject_id",
                "session_id",
                "scan_id",
                "site_name",
            ],
            output_names=["qc"],
            function=qap_anatomical_spatial,
        ),
        name="qap_anatomical_spatial",
    )

    spatial_to_csv = pe.Node(
        util.Function(input_names=["sub_qap_dict"], output_names=["outfile"], function=write_to_csv),
        name="qap_anatomical_spatial_to_csv",
    )

    if len(resource_pool["anatomical_reorient"]) == 2:
        node, out_file = resource_pool["anatomical_reorient"]
        workflow.connect(node, out_file, spatial, "anatomical_reorient")
    else:
        spatial.inputs.anatomical_reorient = resource_pool["anatomical_reorient"]

    if len(resource_pool["qap_head_mask"]) == 2:
        node, out_file = resource_pool["qap_head_mask"]
        workflow.connect(node, out_file, spatial, "head_mask_path")
    else:
        spatial.inputs.head_mask_path = resource_pool["qap_head_mask"]

    if len(resource_pool["anatomical_gm_mask"]) == 2:
        node, out_file = resource_pool["anatomical_gm_mask"]
        workflow.connect(node, out_file, spatial, "anatomical_gm_mask")
    else:
        spatial.inputs.anatomical_gm_mask = resource_pool["anatomical_gm_mask"]

    if len(resource_pool["anatomical_wm_mask"]) == 2:
        node, out_file = resource_pool["anatomical_wm_mask"]
        workflow.connect(node, out_file, spatial, "anatomical_wm_mask")
    else:
        spatial.inputs.anatomical_wm_mask = resource_pool["anatomical_wm_mask"]

    if len(resource_pool["anatomical_csf_mask"]) == 2:
        node, out_file = resource_pool["anatomical_csf_mask"]
        workflow.connect(node, out_file, spatial, "anatomical_csf_mask")
    else:
        spatial.inputs.anatomical_csf_mask = resource_pool["anatomical_csf_mask"]

    # Subject infos

    spatial.inputs.subject_id = config["subject_id"]
    spatial.inputs.session_id = config["session_id"]
    spatial.inputs.scan_id = config["scan_id"]

    if "site_name" in config.keys():
        spatial.inputs.site_name = config["site_name"]

    workflow.connect(spatial, "qc", spatial_to_csv, "sub_qap_dict")

    resource_pool["qap_anatomical_spatial"] = (spatial_to_csv, "outfile")

    return workflow, resource_pool
Пример #3
0
def qap_anatomical_spatial_workflow(workflow,
                                    resource_pool,
                                    config,
                                    report=False):
    # resource pool should have:
    #     anatomical_reorient
    #     qap_head_mask
    #     anatomical_gm_mask
    #     anatomical_wm_mask
    #     anatomical_csf_mask

    import os
    import sys

    import nipype.interfaces.io as nio
    import nipype.pipeline.engine as pe
    import nipype.interfaces.utility as niu
    import nipype.algorithms.misc as nam
    from qap_workflows_utils import qap_anatomical_spatial
    from qap.viz.interfaces import PlotMosaic

    if 'qap_head_mask' not in resource_pool.keys():

        from qap_workflows import qap_mask_workflow

        workflow, resource_pool = \
            qap_mask_workflow(workflow, resource_pool, config)

    if ('anatomical_gm_mask' not in resource_pool.keys()) or \
            ('anatomical_wm_mask' not in resource_pool.keys()) or \
            ('anatomical_csf_mask' not in resource_pool.keys()):

        from anatomical_preproc import segmentation_workflow

        workflow, resource_pool = \
            segmentation_workflow(workflow, resource_pool, config)

    if 'anatomical_reorient' not in resource_pool.keys():
        from anatomical_preproc import anatomical_reorient_workflow
        workflow, resource_pool = \
            anatomical_reorient_workflow(workflow, resource_pool, config)

    spatial = pe.Node(niu.Function(input_names=[
        'anatomical_reorient', 'head_mask_path', 'anatomical_gm_mask',
        'anatomical_wm_mask', 'anatomical_csf_mask', 'subject_id',
        'session_id', 'scan_id', 'site_name'
    ],
                                   output_names=['qc'],
                                   function=qap_anatomical_spatial),
                      name='qap_anatomical_spatial')

    # Subject infos
    spatial.inputs.subject_id = config['subject_id']
    spatial.inputs.session_id = config['session_id']
    spatial.inputs.scan_id = config['scan_id']

    if 'site_name' in config.keys():
        spatial.inputs.site_name = config['site_name']

    if len(resource_pool['anatomical_reorient']) == 2:
        node, out_file = resource_pool['anatomical_reorient']
        workflow.connect(node, out_file, spatial, 'anatomical_reorient')
    else:
        spatial.inputs.anatomical_reorient = \
            resource_pool['anatomical_reorient']

    if len(resource_pool['qap_head_mask']) == 2:
        node, out_file = resource_pool['qap_head_mask']
        workflow.connect(node, out_file, spatial, 'head_mask_path')
    else:
        spatial.inputs.head_mask_path = resource_pool['qap_head_mask']

    if len(resource_pool['anatomical_gm_mask']) == 2:
        node, out_file = resource_pool['anatomical_gm_mask']
        workflow.connect(node, out_file, spatial, 'anatomical_gm_mask')
    else:
        spatial.inputs.anatomical_gm_mask = \
            resource_pool['anatomical_gm_mask']

    if len(resource_pool['anatomical_wm_mask']) == 2:
        node, out_file = resource_pool['anatomical_wm_mask']
        workflow.connect(node, out_file, spatial, 'anatomical_wm_mask')
    else:
        spatial.inputs.anatomical_wm_mask = \
            resource_pool['anatomical_wm_mask']

    if len(resource_pool['anatomical_csf_mask']) == 2:
        node, out_file = resource_pool['anatomical_csf_mask']
        workflow.connect(node, out_file, spatial, 'anatomical_csf_mask')
    else:
        spatial.inputs.anatomical_csf_mask = \
            resource_pool['anatomical_csf_mask']

    if config.get('write_report', False):
        plot = pe.Node(PlotMosaic(), name='plot_mosaic')
        plot.inputs.subject = config['subject_id']

        metadata = [config['session_id'], config['scan_id']]
        if 'site_name' in config.keys():
            metadata.append(config['site_name'])

        plot.inputs.metadata = metadata
        plot.inputs.title = 'Anatomical reoriented'

        if len(resource_pool['anatomical_reorient']) == 2:
            node, out_file = resource_pool['anatomical_reorient']
            workflow.connect(node, out_file, plot, 'in_file')
        else:
            plot.inputs.in_file = resource_pool['anatomical_reorient']

        # Enable this if we want masks
        # if len(resource_pool['qap_head_mask']) == 2:
        #     node, out_file = resource_pool['qap_head_mask']
        #     workflow.connect(node, out_file, plot, 'in_mask')
        # else:
        #     plot.inputs.in_mask = resource_pool['qap_head_mask']

        resource_pool['qap_mosaic'] = (plot, 'out_file')

    out_csv = op.join(config['output_directory'], 'qap_anatomical_spatial.csv')
    spatial_to_csv = pe.Node(nam.AddCSVRow(in_file=out_csv),
                             name='qap_anatomical_spatial_to_csv')

    workflow.connect(spatial, 'qc', spatial_to_csv, '_outputs')
    resource_pool['qap_anatomical_spatial'] = (spatial_to_csv, 'csv_file')
    return workflow, resource_pool