hurricane/dataset.py

import os
import datasets

class HurricaneDetection(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    def _info(self):
        """
        Defines the dataset metadata and feature structure.
        """
        return datasets.DatasetInfo(
            description="Dataset containing .nc files for training.",
            features=datasets.Features({
                "file_path": datasets.Value("string"),  # Store file paths
            }),
            supervised_keys=None,  # Update if supervised task is defined
            homepage="https://huggingface.co/datasets/nasa-impact/WINDSET/tree/main/hurricane",
            license="MIT",
        )

    def _split_generators(self, dl_manager):
        """
        Define the dataset splits for train.
        """
        # Define the directory containing the dataset
        data_dir = os.path.join(os.getcwd(), "hurricane")  # Update with the actual directory

        # Get the directory for the train split (no validation or test splits)
        train_dir = os.path.join(data_dir)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"split_dir": train_dir},
            ),
        ]

    def _generate_data_from_files(self, data_dir):
        """
        Generate file paths for each .h5 file in the directory.
        """
        example_id = 0

        # Loop through the files in the directory
        for h5_file in os.listdir(data_dir):

            if h5_file.endswith(".h5"):  
                h5_file_path = os.path.join(data_dir, h5_file)

                yield example_id, {
                    "file_path": h5_file_path,
                }
                example_id += 1
            else:
                pass

    def _generate_examples(self, split_dir):
        """
        Generates examples for the dataset from the split directory.
        """
        # Call the data generator to get the file paths
        for example_id, example in self._generate_data_from_files(split_dir):
            yield example_id, example