"""
workflows.py
=================
Description:

Author:

Usage:

"""
from nipype import Workflow, IdentityInterface, Node, Function
from .nipype_interfaces import (
    PredictEdgeProbability,
    CollectFeatureFiles,
    CreateReferenceImage,
    LOGISMOSBPreprocessing,
)
from nipype.interfaces.semtools import BRAINSResample
from nipype.interfaces.freesurfer import MRIsConvert

# from ..workflow import LOGISMOSB, read_json_config
# from ..utils.mesh_2_mask import Mesh2Mask
import os


def read_machine_learning_config():
    """
    This function...

    :return:
    """
    return read_json_config(os.path.join("maclearn", "logismosb_maclearn_config.json"))


def create_machine_learning_workflow(
    name="CreateEdgeProbabilityMap", resample=True, plugin_args=None
):
    """
    This function...

    :param name:
    :param resample:
    :param plugin_args:
    :return:
    """
    workflow = Workflow(name)
    input_spec = Node(
        IdentityInterface(
            [
                "rho",
                "phi",
                "theta",
                "posteriors",
                "t1_file",
                "acpc_transform",
                "gm_classifier_file",
                "wm_classifier_file",
            ]
        ),
        name="input_spec",
    )

    predict_edge_probability = Node(
        PredictEdgeProbability(), name="PredictEdgeProbability"
    )
    if plugin_args:
        predict_edge_probability.plugin_args = plugin_args
    workflow.connect(
        [
            (
                input_spec,
                predict_edge_probability,
                [
                    ("t1_file", "t1_file"),
                    ("gm_classifier_file", "gm_classifier_file"),
                    ("wm_classifier_file", "wm_classifier_file"),
                ],
            )
        ]
    )

    if resample:
        collect_features = Node(CollectFeatureFiles(), name="CollectFeatureFiles")
        collect_features.inputs.inverse_transform = True
        workflow.connect(
            [
                (
                    input_spec,
                    collect_features,
                    [
                        ("rho", "rho"),
                        ("phi", "phi"),
                        ("theta", "theta"),
                        ("posteriors", "posterior_files"),
                        ("t1_file", "reference_file"),
                        ("acpc_transform", "transform_file"),
                    ],
                )
            ]
        )

        workflow.connect(
            [
                (
                    collect_features,
                    predict_edge_probability,
                    [("feature_files", "additional_files")],
                )
            ]
        )
    else:
        print("workflow not yet created")
        # INFO: create workflow that does not resample the input images
        return

    output_spec = Node(
        IdentityInterface(["gm_probability_map", "wm_probability_map"]),
        name="output_spec",
    )
    workflow.connect(
        predict_edge_probability,
        "gm_edge_probability",
        output_spec,
        "gm_probability_map",
    )
    workflow.connect(
        predict_edge_probability,
        "wm_edge_probability",
        output_spec,
        "wm_probability_map",
    )

    return workflow


def set_inputs(node, input_dict):
    """
    This function...

    :param node:
    :param input_dict:
    :return:
    """
    for key in input_dict:
        node.set_input(key, input_dict[key])
    return node


def create_logismosb_node(name="LOGISMOSB"):
    """
    This function...

    :param name:
    :return:
    """
    node = Node(LOGISMOSB(), name=name)
    config = read_machine_learning_config()
    return set_inputs(node, config)


def create_workflow_to_resample_baw_files(name="ResampleBAWOutputs"):
    """
    This function...

    :param name:
    :return:
    """
    workflow = Workflow(name)
    inputs_to_resample = ["t1_file", "t2_file", "hncma_file", "abc_file"]
    other_inputs = ["reference_file", "acpc_transform"]
    label_maps = ["hncma_file", "abc_file"]
    input_spec = Node(
        IdentityInterface(inputs_to_resample + other_inputs), name="input_spec"
    )
    output_spec = Node(IdentityInterface(inputs_to_resample), name="output_spec")
    for input in inputs_to_resample:
        node = Node(BRAINSResample(), "Resample_{0}".format(input))
        node.inputs.pixelType = "short"
        node.inputs.inverseTransform = True
        node.inputs.outputVolume = input + ".nii.gz"
        if input in label_maps:
            node.inputs.interpolationMode = "NearestNeighbor"
        workflow.connect(
            [
                (
                    input_spec,
                    node,
                    [
                        ("reference_file", "referenceVolume"),
                        ("acpc_transform", "warpTransform"),
                        ("{0}".format(input), "inputVolume"),
                    ],
                ),
                (node, output_spec, [("outputVolume", "{0}".format(input))]),
            ]
        )
    return workflow


def create_identity_interface_node(inputs, name):
    """
    This function...

    :param inputs:
    :param name:
    :return:
    """
    return Node(IdentityInterface(inputs), name=name)


def create_workflow_to_mask_white_matter(name):
    """
    This function...

    :param name:
    :return:
    """
    workflow = Workflow(name)

    input_spec = create_identity_interface_node(["t1_file", "white"], "input_spec")

    mask_white_matter = Node(Mesh2Mask(), "MaskWhiteMatter")
    mask_white_matter.inputs.output_image = "white.nii.gz"

    workflow.connect(input_spec, "white", mask_white_matter, "input_mesh")
    workflow.connect(input_spec, "t1_file", mask_white_matter, "input_image")

    output_spec = create_identity_interface_node(["white_mask"], "output_spec")
    workflow.connect(mask_white_matter, "output_image", output_spec, "white_mask")

    return workflow


def create_logismosb_machine_learning_workflow(
    name="MachineLearningLOGISMOSB", resample=True, hemispheres=None, plugin_args=None
):
    """
    This function...

    :param name:
    :param resample:
    :param hemispheres:
    :param plugin_args:
    :return:
    """
    workflow = Workflow(name)
    input_spec = Node(
        IdentityInterface(
            [
                "rho",
                "phi",
                "theta",
                "posteriors",
                "t1_file",
                "t2_file",
                "acpc_transform",
                "gm_classifier_file",
                "wm_classifier_file",
                "orig_t1",
                "hncma_file",
                "abc_file",
                "lh_white_surface_file",
                "rh_white_surface_file",
            ]
        ),
        name="input_spec",
    )

    outputs = []
    surface_files = ["gmsurface_file", "wmsurface_file"]
    if not hemispheres:
        hemispheres = ["lh", "rh"]
    for hemi in hemispheres:
        for surface_name in surface_files:
            outputs.append(hemi + "_" + surface_name)
    output_spec = create_identity_interface_node(outputs, "output_spec")

    if resample:
        # resample reference image to spacing (1,1,1)
        reference_image = Node(CreateReferenceImage(), name="ResampleInputT1")
        workflow.connect(
            [
                (
                    input_spec,
                    reference_image,
                    [("orig_t1", "orig_t1"), ("t1_file", "baw_t1")],
                )
            ]
        )

        # resample input images that are not used in the feature data
        resample_baw = create_workflow_to_resample_baw_files()
        workflow.connect(
            [
                (
                    reference_image,
                    resample_baw,
                    [("reference_file", "input_spec.reference_file")],
                ),
                (
                    input_spec,
                    resample_baw,
                    [
                        ("hncma_file", "input_spec.hncma_file"),
                        ("abc_file", "input_spec.abc_file"),
                        ("t1_file", "input_spec.t1_file"),
                        ("t2_file", "input_spec.t2_file"),
                        ("acpc_transform", "input_spec.acpc_transform"),
                    ],
                ),
            ]
        )

        # create and connect machine learning
        predict_edges = create_machine_learning_workflow(
            resample=resample, plugin_args=plugin_args
        )
        feature_files = ["rho", "phi", "theta", "posteriors"]
        for feature in feature_files:
            workflow.connect(
                [
                    (
                        input_spec,
                        predict_edges,
                        [(feature, "input_spec.{0}".format(feature))],
                    )
                ]
            )
        workflow.connect(
            [
                (
                    reference_image,
                    predict_edges,
                    [("reference_file", "input_spec.t1_file")],
                ),
                (
                    input_spec,
                    predict_edges,
                    [
                        ("acpc_transform", "input_spec.acpc_transform"),
                        ("gm_classifier_file", "input_spec.gm_classifier_file"),
                        ("wm_classifier_file", "input_spec.wm_classifier_file"),
                    ],
                ),
            ]
        )

        for hemisphere in hemispheres:
            convert_white = Node(
                MRIsConvert(), name="{0}_Convert_White".format(hemisphere)
            )
            convert_white.inputs.out_file = "{0}_white.vtk".format(hemisphere)
            convert_white.inputs.to_scanner = True
            workflow.connect(
                [
                    (
                        input_spec,
                        convert_white,
                        [("{0}_white_surface_file".format(hemisphere), "in_file")],
                    )
                ]
            )

            mask_wm = create_workflow_to_mask_white_matter(
                "{0}_MaskWhiteMatter".format(hemisphere)
            )
            workflow.connect(
                [
                    (convert_white, mask_wm, [("converted", "input_spec.white")]),
                    (
                        resample_baw,
                        mask_wm,
                        [("output_spec.t1_file", "input_spec.t1_file")],
                    ),
                ]
            )

            preproc = Node(
                LOGISMOSBPreprocessing(), "{0}_Preprocessing".format(hemisphere)
            )
            preproc.inputs.erode_mask = 0
            workflow.connect(
                [
                    (mask_wm, preproc, [("output_spec.white_mask", "white_mask")]),
                    (
                        predict_edges,
                        preproc,
                        [
                            ("output_spec.gm_probability_map", "gm_proba"),
                            ("output_spec.wm_probability_map", "wm_proba"),
                        ],
                    ),
                ]
            )

            logb = create_logismosb_node("ML_LOGISMOSB_{0}".format(hemisphere))
            logb.inputs.basename = hemisphere
            if plugin_args:
                logb.plugin_args = plugin_args
            # connect logb inputs
            workflow.connect(
                [
                    (
                        resample_baw,
                        logb,
                        [
                            ("output_spec.hncma_file", "atlas_file"),
                            ("output_spec.abc_file", "brainlabels_file"),
                            ("output_spec.t1_file", "t1_file"),
                            ("output_spec.t2_file", "t2_file"),
                        ],
                    ),
                    (convert_white, logb, [("converted", "mesh_file")]),
                    (preproc, logb, [("white_mask", "wm_file")]),
                    (preproc, logb, [("gm_proba", "gm_proba_file")]),
                    (preproc, logb, [("wm_proba", "wm_proba_file")]),
                ]
            )

            for surface_name in surface_files:
                workflow.connect(
                    logb, surface_name, output_spec, hemisphere + "_" + surface_name
                )

        return workflow

"""
workflows.py
=================
Description:

Author:

Usage:

"""
from nipype import Workflow, IdentityInterface, Node, Function
from .nipype_interfaces import (
    PredictEdgeProbability,
    CollectFeatureFiles,
    CreateReferenceImage,
    LOGISMOSBPreprocessing,
)
from nipype.interfaces.semtools import BRAINSResample
from nipype.interfaces.freesurfer import MRIsConvert

# from ..workflow import LOGISMOSB, read_json_config
# from ..utils.mesh_2_mask import Mesh2Mask
import os


def read_machine_learning_config():
    """
    This function...

    :return:
    """
    return read_json_config(os.path.join("maclearn", "logismosb_maclearn_config.json"))


def create_machine_learning_workflow(
    name="CreateEdgeProbabilityMap", resample=True, plugin_args=None
):
    """
    This function...

    :param name:
    :param resample:
    :param plugin_args:
    :return:
    """
    workflow = Workflow(name)
    input_spec = Node(
        IdentityInterface(
            [
                "rho",
                "phi",
                "theta",
                "posteriors",
                "t1_file",
                "acpc_transform",
                "gm_classifier_file",
                "wm_classifier_file",
            ]
        ),
        name="input_spec",
    )

    predict_edge_probability = Node(
        PredictEdgeProbability(), name="PredictEdgeProbability"
    )
    if plugin_args:
        predict_edge_probability.plugin_args = plugin_args
    workflow.connect(
        [
            (
                input_spec,
                predict_edge_probability,
                [
                    ("t1_file", "t1_file"),
                    ("gm_classifier_file", "gm_classifier_file"),
                    ("wm_classifier_file", "wm_classifier_file"),
                ],
            )
        ]
    )

    if resample:
        collect_features = Node(CollectFeatureFiles(), name="CollectFeatureFiles")
        collect_features.inputs.inverse_transform = True
        workflow.connect(
            [
                (
                    input_spec,
                    collect_features,
                    [
                        ("rho", "rho"),
                        ("phi", "phi"),
                        ("theta", "theta"),
                        ("posteriors", "posterior_files"),
                        ("t1_file", "reference_file"),
                        ("acpc_transform", "transform_file"),
                    ],
                )
            ]
        )

        workflow.connect(
            [
                (
                    collect_features,
                    predict_edge_probability,
                    [("feature_files", "additional_files")],
                )
            ]
        )
    else:
        print("workflow not yet created")
        # INFO: create workflow that does not resample the input images
        return

    output_spec = Node(
        IdentityInterface(["gm_probability_map", "wm_probability_map"]),
        name="output_spec",
    )
    workflow.connect(
        predict_edge_probability,
        "gm_edge_probability",
        output_spec,
        "gm_probability_map",
    )
    workflow.connect(
        predict_edge_probability,
        "wm_edge_probability",
        output_spec,
        "wm_probability_map",
    )

    return workflow


def set_inputs(node, input_dict):
    """
    This function...

    :param node:
    :param input_dict:
    :return:
    """
    for key in input_dict:
        node.set_input(key, input_dict[key])
    return node


def create_logismosb_node(name="LOGISMOSB"):
    """
    This function...

    :param name:
    :return:
    """
    node = Node(LOGISMOSB(), name=name)
    config = read_machine_learning_config()
    return set_inputs(node, config)


def create_workflow_to_resample_baw_files(name="ResampleBAWOutputs"):
    """
    This function...

    :param name:
    :return:
    """
    workflow = Workflow(name)
    inputs_to_resample = ["t1_file", "t2_file", "hncma_file", "abc_file"]
    other_inputs = ["reference_file", "acpc_transform"]
    label_maps = ["hncma_file", "abc_file"]
    input_spec = Node(
        IdentityInterface(inputs_to_resample + other_inputs), name="input_spec"
    )
    output_spec = Node(IdentityInterface(inputs_to_resample), name="output_spec")
    for input in inputs_to_resample:
        node = Node(BRAINSResample(), "Resample_{0}".format(input))
        node.inputs.pixelType = "short"
        node.inputs.inverseTransform = True
        node.inputs.outputVolume = input + ".nii.gz"
        if input in label_maps:
            node.inputs.interpolationMode = "NearestNeighbor"
        workflow.connect(
            [
                (
                    input_spec,
                    node,
                    [
                        ("reference_file", "referenceVolume"),
                        ("acpc_transform", "warpTransform"),
                        ("{0}".format(input), "inputVolume"),
                    ],
                ),
                (node, output_spec, [("outputVolume", "{0}".format(input))]),
            ]
        )
    return workflow


def create_identity_interface_node(inputs, name):
    """
    This function...

    :param inputs:
    :param name:
    :return:
    """
    return Node(IdentityInterface(inputs), name=name)


def create_workflow_to_mask_white_matter(name):
    """
    This function...

    :param name:
    :return:
    """
    workflow = Workflow(name)

    input_spec = create_identity_interface_node(["t1_file", "white"], "input_spec")

    mask_white_matter = Node(Mesh2Mask(), "MaskWhiteMatter")
    mask_white_matter.inputs.output_image = "white.nii.gz"

    workflow.connect(input_spec, "white", mask_white_matter, "input_mesh")
    workflow.connect(input_spec, "t1_file", mask_white_matter, "input_image")

    output_spec = create_identity_interface_node(["white_mask"], "output_spec")
    workflow.connect(mask_white_matter, "output_image", output_spec, "white_mask")

    return workflow


def create_logismosb_machine_learning_workflow(
    name="MachineLearningLOGISMOSB", resample=True, hemispheres=None, plugin_args=None
):
    """
    This function...

    :param name:
    :param resample:
    :param hemispheres:
    :param plugin_args:
    :return:
    """
    workflow = Workflow(name)
    input_spec = Node(
        IdentityInterface(
            [
                "rho",
                "phi",
                "theta",
                "posteriors",
                "t1_file",
                "t2_file",
                "acpc_transform",
                "gm_classifier_file",
                "wm_classifier_file",
                "orig_t1",
                "hncma_file",
                "abc_file",
                "lh_white_surface_file",
                "rh_white_surface_file",
            ]
        ),
        name="input_spec",
    )

    outputs = []
    surface_files = ["gmsurface_file", "wmsurface_file"]
    if not hemispheres:
        hemispheres = ["lh", "rh"]
    for hemi in hemispheres:
        for surface_name in surface_files:
            outputs.append(hemi + "_" + surface_name)
    output_spec = create_identity_interface_node(outputs, "output_spec")

    if resample:
        # resample reference image to spacing (1,1,1)
        reference_image = Node(CreateReferenceImage(), name="ResampleInputT1")
        workflow.connect(
            [
                (
                    input_spec,
                    reference_image,
                    [("orig_t1", "orig_t1"), ("t1_file", "baw_t1")],
                )
            ]
        )

        # resample input images that are not used in the feature data
        resample_baw = create_workflow_to_resample_baw_files()
        workflow.connect(
            [
                (
                    reference_image,
                    resample_baw,
                    [("reference_file", "input_spec.reference_file")],
                ),
                (
                    input_spec,
                    resample_baw,
                    [
                        ("hncma_file", "input_spec.hncma_file"),
                        ("abc_file", "input_spec.abc_file"),
                        ("t1_file", "input_spec.t1_file"),
                        ("t2_file", "input_spec.t2_file"),
                        ("acpc_transform", "input_spec.acpc_transform"),
                    ],
                ),
            ]
        )

        # create and connect machine learning
        predict_edges = create_machine_learning_workflow(
            resample=resample, plugin_args=plugin_args
        )
        feature_files = ["rho", "phi", "theta", "posteriors"]
        for feature in feature_files:
            workflow.connect(
                [
                    (
                        input_spec,
                        predict_edges,
                        [(feature, "input_spec.{0}".format(feature))],
                    )
                ]
            )
        workflow.connect(
            [
                (
                    reference_image,
                    predict_edges,
                    [("reference_file", "input_spec.t1_file")],
                ),
                (
                    input_spec,
                    predict_edges,
                    [
                        ("acpc_transform", "input_spec.acpc_transform"),
                        ("gm_classifier_file", "input_spec.gm_classifier_file"),
                        ("wm_classifier_file", "input_spec.wm_classifier_file"),
                    ],
                ),
            ]
        )

        for hemisphere in hemispheres:
            convert_white = Node(
                MRIsConvert(), name="{0}_Convert_White".format(hemisphere)
            )
            convert_white.inputs.out_file = "{0}_white.vtk".format(hemisphere)
            convert_white.inputs.to_scanner = True
            workflow.connect(
                [
                    (
                        input_spec,
                        convert_white,
                        [("{0}_white_surface_file".format(hemisphere), "in_file")],
                    )
                ]
            )

            mask_wm = create_workflow_to_mask_white_matter(
                "{0}_MaskWhiteMatter".format(hemisphere)
            )
            workflow.connect(
                [
                    (convert_white, mask_wm, [("converted", "input_spec.white")]),
                    (
                        resample_baw,
                        mask_wm,
                        [("output_spec.t1_file", "input_spec.t1_file")],
                    ),
                ]
            )

            preproc = Node(
                LOGISMOSBPreprocessing(), "{0}_Preprocessing".format(hemisphere)
            )
            preproc.inputs.erode_mask = 0
            workflow.connect(
                [
                    (mask_wm, preproc, [("output_spec.white_mask", "white_mask")]),
                    (
                        predict_edges,
                        preproc,
                        [
                            ("output_spec.gm_probability_map", "gm_proba"),
                            ("output_spec.wm_probability_map", "wm_proba"),
                        ],
                    ),
                ]
            )

            logb = create_logismosb_node("ML_LOGISMOSB_{0}".format(hemisphere))
            logb.inputs.basename = hemisphere
            if plugin_args:
                logb.plugin_args = plugin_args
            # connect logb inputs
            workflow.connect(
                [
                    (
                        resample_baw,
                        logb,
                        [
                            ("output_spec.hncma_file", "atlas_file"),
                            ("output_spec.abc_file", "brainlabels_file"),
                            ("output_spec.t1_file", "t1_file"),
                            ("output_spec.t2_file", "t2_file"),
                        ],
                    ),
                    (convert_white, logb, [("converted", "mesh_file")]),
                    (preproc, logb, [("white_mask", "wm_file")]),
                    (preproc, logb, [("gm_proba", "gm_proba_file")]),
                    (preproc, logb, [("wm_proba", "wm_proba_file")]),
                ]
            )

            for surface_name in surface_files:
                workflow.connect(
                    logb, surface_name, output_spec, hemisphere + "_" + surface_name
                )

        return workflow

workflows.py ================= Description:  Author:  Usage: # from ..workflow import LOGISMOSB, read_json_config # from ..utils.mesh_2_mask import Mesh2Mask This function...      :return: This function...      :param name:     :param resample:     :param plugin_args:     :return: # INFO: create workflow that does not resample the input images This function...      :param node:     :param input_dict:     :return: This function...      :param name:     :return: This function...      :param name:     :return: This function...      :param inputs:     :param name:     :return: This function...      :param name:     :return: This function...      :param name:     :param resample:     :param hemispheres:     :param plugin_args:     :return: # resample reference image to spacing (1,1,1) # resample input images that are not used in the feature data # create and connect machine learning # connect logb inputs