import argparse
from datetime import datetime, timedelta
from enum import Enum
import os
from typing import List

import numpy as np
import pandas as pd
import pydicom


class MyocardialProtocol(Enum):
    Stress = 1
    Rest = 2


headers = argparse.Namespace()
headers.id = "id"
headers.patient_id = "patient_id"
headers.age = "age"
headers.weight = "weight"
headers.size = "size"
headers.protocol = "protocol"
headers.datetime_acquisition = "datetime_acquisition"
headers.datetime_reconstruction = "datetime_reconstruction"
headers.pixel_spacing_x = "pixel_spacing_x"
headers.pixel_spacing_y = "pixel_spacing_y"
headers.pixel_spacing_z = "pixel_spacing_z"
headers.shape_x = "shape_x"
headers.shape_y = "shape_y"
headers.shape_z = "shape_z"
headers.radiopharmaceutical = "radiopharmaceutical"
headers.radionuclide_dose = "radionuclide_dose"
headers.radionuclide_code = "radionuclide_code"
headers.radionuclide_meaning = "radionuclide_meaning"
headers.energy_window_peak_lower = "energy_window_peak_lower"
headers.energy_window_peak_upper = "energy_window_peak_upper"
headers.energy_window_scatter_lower = "energy_window_scatter_lower"
headers.energy_window_scatter_upper = "energy_window_scatter_upper"
headers.detector_count = "detector_count"
headers.collimator_type = "collimator_type"
headers.rotation_start = "rotation_start"
headers.rotation_step = "rotation_step"
headers.rotation_scan_arc = "rotation_scan_arc"
headers.file_recon_ac = "file_recon_ac"
headers.file_recon_no_ac = "file_recon_no_ac"
headers.file_mu_map = "file_mu_map"



def parse_series_time(dicom_image: pydicom.dataset.FileDataset) -> datetime:
    """
    Parse the date and time of a DICOM series object into a datetime object.

    :param dicom_image: the dicom file to parse the series date and time from
    :return: an according python datetime object.
    """
    _date = dicom_image.SeriesDate
    _time = dicom_image.SeriesTime
    return datetime(
        year=int(_date[0:4]),
        month=int(_date[4:6]),
        day=int(_date[6:8]),
        hour=int(_time[0:2]),
        minute=int(_time[2:4]),
        second=int(_time[4:6]),
        microsecond=int(_time.split(".")[1]),
    )


def parse_age(patient_age: str) -> int:
    """
    Parse and age string as defined in the DICOM standard into an integer representing the age in years.

    :param patient_age: age string as defined in the DICOM standard
    :return: the age in years as a number
    """
    assert (
        type(patient_age) == str
    ), f"patient age needs to be a string and not {type(patient_age)}"
    assert (
        len(patient_age) == 4
    ), f"patient age [{patient_age}] has to be four characters long"
    _num, _format = patient_age[:3], patient_age[3]
    assert (
        _format == "Y"
    ), f"currently, only patient ages in years [Y] is supported, not [{_format}]"
    return int(_num)


def get_projection(dicom_images: List[pydicom.dataset.FileDataset], protocol: MyocardialProtocol) -> pydicom.dataset.FileDataset:
    """
    Extract the SPECT projection from a list of DICOM images belonging to a myocardial scintigraphy study given a study protocol.

    :param dicom_images: list of DICOM images of a study
    :param protocol: the protocol for which the projection images should be extracted
    :return: the extracted DICOM image
    """
    dicom_images = filter(lambda image: "TOMO" in image.ImageType, dicom_images)
    dicom_images = filter(lambda image: protocol.name in image.SeriesDescription, dicom_images)
    dicom_images = list(dicom_images)

    if len(dicom_images) != 1:
        raise ValueError(f"No or multiple projections {len(dicom_images)} for protocol {protocol.name} available")

    return dicom_images[0]


def get_reconstruction(dicom_images: List[pydicom.dataset.FileDataset], protocol: MyocardialProtocol, corrected:bool=True) -> pydicom.dataset.FileDataset:
    """
    Extract a SPECT reconstruction from a list of DICOM images belonging to a myocardial scintigraphy study given a study protocol.
    The corrected flag can be used to either extract an attenuation corrected or a non-attenuation corrected image.
    If there are multiple images, they are sorted by acquisition date and the newest is returned.

    :param dicom_images: list of DICOM images of a study
    :param protocol: the protocol for which the projection images should be extracted
    :param corrected: extract an attenuation or non-attenuation corrected image
    :return: the extracted DICOM image
    """
    dicom_images = filter(lambda image: "RECON TOMO" in image.ImageType, dicom_images)
    dicom_images = filter(lambda image: protocol.name in image.SeriesDescription, dicom_images)

    if corrected:
        dicom_images = filter(
            lambda image: "AC" in image.SeriesDescription
            and "NoAC" not in image.SeriesDescription,
            dicom_images,
        )
        dicom_images = list(dicom_images)
    else:
        dicom_images = filter(
            lambda image: "NoAC" in image.SeriesDescription, dicom_images
        )

    # for SPECT reconstructions created in clinical studies this value exists and is set to 'APEX_TO_BASE'
    # for the reconstructions with attenuation maps it does not exist
    dicom_images = filter(
        lambda image: not hasattr(image, "SliceProgressionDirection"), dicom_images
    )

    dicom_images = list(dicom_images)
    dicom_images.sort(key=lambda image: parse_series_time(image), reverse=True)

    if len(dicom_images) == 0:
        _str = "AC" if corrected else "NoAC"
        raise ValueError(f"{_str} Reconstruction for protocol {protocol.name} is not available")

    return dicom_images[0]


def get_attenuation_map(dicom_images: List[pydicom.dataset.FileDataset], protocol: MyocardialProtocol) -> pydicom.dataset.FileDataset:
    """
    Extract an attenuation map from a list of DICOM images belonging to a myocardial scintigraphy study given a study protocol.
    If there are multiple attenuation maps, they are sorted by acquisition date and the newest is returned.

    :param dicom_images: list of DICOM images of a study
    :param protocol: the protocol for which the projection images should be extracted
    :return: the extracted DICOM image
    """
    dicom_images = filter(lambda image: "RECON TOMO" in image.ImageType, dicom_images)
    dicom_images = filter(lambda image: protocol.name in image.SeriesDescription, dicom_images)
    dicom_images = filter(lambda image: "µ-map" in image.SeriesDescription, dicom_images)
    dicom_images = list(dicom_images)
    dicom_images.sort(key=lambda image: parse_series_time(image), reverse=True)

    if len(dicom_images) == 0:
        raise ValueError(f"Attenuation map for protocol {protocol.name} is not available")

    return dicom_images[0]


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Prepare a dataset from DICOM directories",
        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
    )
    parser.add_argument(
        "dicom_dirs",
        type=str,
        nargs="+",
        help="paths to DICOMDIR files or directories containing one of them",
    )
    parser.add_argument("--dataset_dir", type=str, required=True, help="")
    parser.add_argument("--images_dir", type=str, default="images", help="")
    parser.add_argument("--csv", type=str, default="data.csv", help="")
    parser.add_argument("--prefix_projection", type=str, default="projection", help="")
    parser.add_argument("--prefix_mu_map", type=str, default="mu_map", help="")
    parser.add_argument("--prefix_recon_ac", type=str, default="recon_ac", help="")
    parser.add_argument("--prefix_recon_no_ac", type=str, default="recon_no_ac", help="")
    args = parser.parse_args()

    args.dicom_dirs = [
        (os.path.dirname(_file) if os.path.isfile(_file) else _file)
        for _file in args.dicom_dirs
    ]
    args.images_dir = os.path.join(args.dataset_dir, args.images_dir)
    args.csv = os.path.join(args.dataset_dir, args.csv)

    patients = []
    dicom_dir_by_patient = {}
    for dicom_dir in args.dicom_dirs:
        dataset = pydicom.dcmread(os.path.join(dicom_dir, "DICOMDIR"))
        for patient in dataset.patient_records:
            assert (
                patient.PatientID not in dicom_dir_by_patient
            ), f"Patient {patient.PatientID} is contained twice in the given DICOM directories ({dicom_dir} and {dicom_dir_by_patient[patient.PatientID]})"
            dicom_dir_by_patient[patient.PatientID] = dicom_dir
            patients.append(patient)

    if not os.path.exists(args.dataset_dir):
        os.mkdir(args.dataset_dir)

    if not os.path.exists(args.images_dir):
        os.mkdir(args.images_dir)

    _id = 1
    if os.path.exists(args.csv):
        data = pd.read_csv(args.csv)
        _id = int(data[headers.id].max())
    else:
        data = pd.DataFrame(dict([(key, []) for key in vars(headers).keys()]))


    for i, patient in enumerate(patients, start=1):
        print(f"Process patient {str(i):>3}/{len(patients)}:")

        # get all myocardial scintigraphy studies
        studies = list(
            filter(
                lambda child: child.DirectoryRecordType == "STUDY"
                and child.StudyDescription == "Myokardszintigraphie",
                patient.children,
            )
        )

        # extract all dicom images
        dicom_images = []
        for study in studies:
            series = list(
                filter(lambda child: child.DirectoryRecordType == "SERIES", study.children)
            )
            for _series in series:
                images = list(
                    filter(
                        lambda child: child.DirectoryRecordType == "IMAGE", _series.children
                    )
                )

                # all SPECT data is stored as a single 3D array which means that it is a series with a single image
                # this is not the case for CTs, which are skipped here
                if len(images) != 1:
                    continue

                images = list(
                    map(
                        lambda image: pydicom.dcmread(
                            os.path.join(dicom_dir_by_patient[patient.PatientID], *image.ReferencedFileID),
                            stop_before_pixels=True,
                        ),
                        images,
                    )
                )

                if len(images) == 0:
                    continue

                dicom_images.append(images[0])


            for protocol in MyocardialProtocol:
                if len(data[(data[headers.patient_id] == patient.PatientID) & (data[headers.protocol] == protocol.name)]) > 0:
                    print(f"Skip {patient.PatientID}:{protocol.name} since it is already contained in the dataset")
                    continue

                try:
                    projection_image = get_projection(dicom_images, protocol=protocol)
                    recon_ac = get_reconstruction(dicom_images, protocol=protocol, corrected=True)
                    recon_noac = get_reconstruction(dicom_images, protocol=protocol, corrected=False)
                    attenuation_map = get_attenuation_map(dicom_images, protocol=protocol)
                except ValueError as e:
                    print(f"Skip {patient.PatientID}:{protocol.name} because {e}")
                    continue

                recon_images = [recon_ac, recon_noac, attenuation_map]

                # extract date times and assert that they are equal for all reconstruction images
                datetimes = list(map(parse_series_time, recon_images))
                _datetimes = sorted(datetimes, reverse=True)
                _datetimes_delta = list(map(lambda dt: _datetimes[0] - dt, _datetimes))
                _equal = all(map(lambda dt: dt < timedelta(seconds=300), _datetimes_delta))
                assert (
                    _equal
                ), f"Not all dates and times of the reconstructions are equal: {datetimes}"

                # extract pixel spacings and assert that they are equal for all reconstruction images
                pixel_spacings = map(
                    lambda image: [*image.PixelSpacing, image.SliceThickness], recon_images
                )
                pixel_spacings = map(
                    lambda pixel_spacing: list(map(float, pixel_spacing)), pixel_spacings
                )
                pixel_spacings = map(
                    lambda pixel_spacing: np.array(pixel_spacing), pixel_spacings
                )
                pixel_spacings = list(pixel_spacings)
                _equal = all(
                    map(
                        lambda pixel_spacing: (pixel_spacing == pixel_spacings[0]).all(),
                        pixel_spacings,
                    )
                )
                assert (
                    _equal
                ), f"Not all pixel spacings of the reconstructions are equal: {pixel_spacings}"
                pixel_spacing = pixel_spacings[0]

                # extract shapes and assert that they are equal for all reconstruction images
                shapes = map(
                    lambda image: [image.Rows, image.Columns, image.NumberOfSlices],
                    recon_images,
                )
                shapes = map(lambda shape: list(map(int, shape)), shapes)
                shapes = map(lambda shape: np.array(shape), shapes)
                shapes = list(shapes)
                _equal = all(map(lambda shape: (shape == shapes[0]).all(), shapes))
                # assert _equal, f"Not all shapes of the reconstructions are equal: {shapes}"
                # print(shapes)
                shape = shapes[0]

                # exctract and sort energy windows
                energy_windows = projection_image.EnergyWindowInformationSequence
                energy_windows = map(
                    lambda ew: ew.EnergyWindowRangeSequence[0], energy_windows
                )
                energy_windows = map(
                    lambda ew: (
                        float(ew.EnergyWindowLowerLimit),
                        float(ew.EnergyWindowUpperLimit),
                    ),
                    energy_windows,
                )
                energy_windows = list(energy_windows)
                energy_windows.sort(key=lambda ew: ew[0], reverse=True)

                # re-read images with pixel-level data and save accordingly
                projection_image = pydicom.dcmread(projection_image.filename)
                recon_ac = pydicom.dcmread(recon_ac.filename)
                recon_noac = pydicom.dcmread(recon_noac.filename)
                attenuation_map = pydicom.dcmread(attenuation_map.filename)

                pydicom.dcmwrite(os.path.join(args.images_dir, f"{_id:04d}-{protocol.name.lower()}-{args.prefix_projection}.dcm"), projection_image)
                pydicom.dcmwrite(os.path.join(args.images_dir, f"{_id:04d}-{protocol.name.lower()}-{args.prefix_recon_ac}.dcm"), recon_ac)
                pydicom.dcmwrite(os.path.join(args.images_dir, f"{_id:04d}-{protocol.name.lower()}-{args.prefix_recon_no_ac}.dcm"), recon_noac)
                pydicom.dcmwrite(os.path.join(args.images_dir, f"{_id:04d}-{protocol.name.lower()}-{args.prefix_mu_map}.dcm"), attenuation_map)

                row = {
                    headers.id: _id,
                    headers.patient_id: projection_image.PatientID,
                    headers.age: parse_age(projection_image.PatientAge),
                    headers.weight: float(projection_image.PatientWeight),
                    headers.size: float(projection_image.PatientSize),
                    headers.protocol: protocol.name,
                    headers.datetime_acquisition: parse_series_time(projection_image),
                    headers.datetime_reconstruction: datetimes[0],
                    headers.pixel_spacing_x: pixel_spacing[0],
                    headers.pixel_spacing_y: pixel_spacing[1],
                    headers.pixel_spacing_z: pixel_spacing[2],
                    headers.shape_x: shape[0],
                    headers.shape_y: shape[1],
                    headers.shape_z: shape[2],
                    headers.radiopharmaceutical: projection_image.RadiopharmaceuticalInformationSequence[
                        0
                    ].Radiopharmaceutical,
                    headers.radionuclide_dose: projection_image.RadiopharmaceuticalInformationSequence[
                        0
                    ].RadionuclideTotalDose,
                    headers.radionuclide_code: projection_image.RadiopharmaceuticalInformationSequence[
                        0
                    ]
                    .RadionuclideCodeSequence[0]
                    .CodeValue,
                    headers.radionuclide_meaning: projection_image.RadiopharmaceuticalInformationSequence[
                        0
                    ]
                    .RadionuclideCodeSequence[0]
                    .CodeMeaning,
                    headers.energy_window_peak_lower: energy_windows[0][0],
                    headers.energy_window_peak_upper: energy_windows[0][1],
                    headers.energy_window_scatter_lower: energy_windows[1][0],
                    headers.energy_window_scatter_upper: energy_windows[1][1],
                    headers.detector_count: len(projection_image.DetectorInformationSequence),
                    headers.collimator_type: projection_image.DetectorInformationSequence[
                        0
                    ].CollimatorType,
                    headers.rotation_start: float(
                        projection_image.RotationInformationSequence[0].StartAngle
                    ),
                    headers.rotation_step: float(
                        projection_image.RotationInformationSequence[0].AngularStep
                    ),
                    headers.rotation_scan_arc: float(
                        projection_image.RotationInformationSequence[0].ScanArc
                    ),
                    headers.file_recon_ac: "filename_recon_ac.dcm",
                    headers.file_recon_no_ac: "filename_recon_no_ac.dcm",
                    headers.file_mu_map: "filanem_mu_map.dcm",
                }
                _id += 1

                row = pd.DataFrame(row, index=[0])
                data = pd.concat((data, row), ignore_index=True)

data.to_csv(args.csv, index=False)