from pathlib import Path

import datasets
import numpy as np
from PIL import Image

project_name = 'xiazeyu/WildfireSimMaps'

map_names = sorted([x.name for x in Path('dataset').iterdir() if x.is_dir()])

_CITATION = """\
"""

_DESCRIPTION = 'A real-world dataset for wildfire simulation.'

_HOMEPAGE = 'https://huggingface.co/datasets/xiazeyu/WildfireSimMaps'

_LICENSE = 'CC BY-NC 4.0'


def load_map(map_name):
    map_root = Path('dataset') / map_name

    return {'canopy': np.array(Image.open(map_root / 'canopy.tif')),
            'density': np.array(Image.open(map_root / 'density.tif')),
            'slope': np.array(Image.open(map_root / 'slope.tif')), }


data = {'name': [], 'canopy': [], 'density': [], "slope": [], 'shape': [], }

for name in map_names:
    map_data = load_map(name)
    data['name'].append(name)
    data['canopy'].append(map_data['canopy'].flatten())
    data['density'].append(map_data['density'].flatten())
    data['slope'].append(map_data['slope'].flatten())
    data['shape'].append(map_data['canopy'].shape)

features = datasets.Features({'name': datasets.Value('string'), 'canopy': datasets.Sequence(datasets.Value('int8')),
                              'density': datasets.Sequence(datasets.Value('float32')),
                              'slope': datasets.Sequence(datasets.Value('int8')),
                              'shape': datasets.Sequence(datasets.Value('int16'), length=2), })
data_info = datasets.DatasetInfo(description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE,
                                 citation=_CITATION, )

ds = datasets.Dataset.from_dict(data, features=features, info=data_info, )

ds.VERSION = datasets.Version("1.0.0")

ds.push_to_hub(project_name)