"""LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding."""


import datasets

_DESCRIPTION = """\
LyNoS: Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding.
"""

_HOMEPAGE = "https://github.com/raidionics/LyNoS"

_LICENSE = "MIT"

_CITATION = """\
@article{bouget2023mediastinal,
  title={Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding},
  author={Bouget, David and Pedersen, Andr{\'e} and Vanel, Johanna and Leira, Haakon O and Lang{\o}, Thomas},
  journal={Computer Methods in Biomechanics and Biomedical Engineering: Imaging \& Visualization},
  volume={11},
  number={1},
  pages={44--58},
  year={2023},
  publisher={Taylor \& Francis}
}

"""

_URLS = [
    {
        "ct": f"data/Pat{i}/Pat{i}_data.nii.gz",
        "azygos": f"data/Pat{i}/Pat{i}_labels_Azygos.nii.gz",
        "brachiocephalicveins": f"data/Pat{i}/Pat{i}_labels_BrachiocephalicVeins.nii.gz",
        "esophagus": f"data/Pat{i}/Pat{i}_labels_Esophagus.nii.gz",
        "lymphnodes": f"data/Pat{i}/Pat{i}_labels_LymphNodes.nii.gz",
        "subclaviancarotidarteries": f"data/Pat{i}/Pat{i}_labels_SubCarArt.nii.gz",
    }
    for i in range(1, 15)
]


class LyNoS(datasets.GeneratorBasedBuilder):
    """A segmentation benchmark dataset for enlarged lymph nodes in patients with primary lung cancer."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "ct": datasets.Value("string"),
                "lymphnodes": datasets.Value("string"),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dirs = dl_manager.download(_URLS)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "data_dirs": data_dirs,
                },
            ),
        ]

    def _generate_examples(self, data_dirs):
        for key, patient in enumerate(data_dirs):
            yield key, patient