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import os |
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import random |
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from glob import glob |
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import json |
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from huggingface_hub import hf_hub_download |
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from astropy.io import fits |
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import datasets |
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from datasets import DownloadManager |
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from fsspec.core import url_to_fs |
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_DESCRIPTION = """ |
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SBI-16-2D is a dataset which is part of the AstroCompress project. |
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It contains imaging data assembled from the Hubble Space Telescope (HST). |
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""" |
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_HOMEPAGE = "https://google.github.io/AstroCompress" |
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_LICENSE = "CC BY 4.0" |
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_URL = "https://huggingface.co/datasets/AstroCompress/SBI-16-2D/resolve/main/" |
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_URLS = { |
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"tiny": { |
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"train": "./splits/tiny_train.jsonl", |
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"test": "./splits/tiny_test.jsonl", |
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}, |
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"full": { |
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"train": "./splits/full_train.jsonl", |
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"test": "./splits/full_test.jsonl", |
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}, |
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} |
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_REPO_ID = "AstroCompress/SBI-16-2D" |
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class SBI_16_2D(datasets.GeneratorBasedBuilder): |
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"""SBI-16-2D Dataset""" |
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VERSION = datasets.Version("1.0.1") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="tiny", |
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version=VERSION, |
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description="A small subset of the data, to test downsteam workflows.", |
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), |
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datasets.BuilderConfig( |
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name="full", |
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version=VERSION, |
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description="The full dataset", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "tiny" |
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def __init__(self, **kwargs): |
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super().__init__(version=self.VERSION, **kwargs) |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"image": datasets.Image(decode=True, mode="I;16"), |
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"ra": datasets.Value("float64"), |
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"dec": datasets.Value("float64"), |
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"pixscale": datasets.Value("float64"), |
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"image_id": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation="TBD", |
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) |
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def _split_generators(self, dl_manager: DownloadManager): |
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ret = [] |
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base_path = dl_manager._base_path |
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locally_run = not base_path.startswith(datasets.config.HF_ENDPOINT) |
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_, path = url_to_fs(base_path) |
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for split in ["train", "test"]: |
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if locally_run: |
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split_file_location = os.path.normpath( |
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os.path.join(path, _URLS[self.config.name][split]) |
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) |
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split_file = dl_manager.download_and_extract(split_file_location) |
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else: |
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split_file = hf_hub_download( |
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repo_id=_REPO_ID, |
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filename=_URLS[self.config.name][split], |
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repo_type="dataset", |
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) |
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with open(split_file, encoding="utf-8") as f: |
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data_filenames = [] |
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data_metadata = [] |
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for line in f: |
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item = json.loads(line) |
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data_filenames.append(item["image"]) |
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data_metadata.append( |
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{ |
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"ra": item["ra"], |
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"dec": item["dec"], |
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"pixscale": item["pixscale"], |
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"image_id": item["image_id"], |
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} |
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) |
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if locally_run: |
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data_urls = [ |
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os.path.normpath(os.path.join(path, data_filename)) |
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for data_filename in data_filenames |
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] |
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data_files = [ |
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dl_manager.download(data_url) for data_url in data_urls |
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] |
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else: |
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data_urls = data_filenames |
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data_files = [ |
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hf_hub_download( |
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repo_id=_REPO_ID, filename=data_url, repo_type="dataset" |
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) |
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for data_url in data_urls |
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] |
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ret.append( |
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datasets.SplitGenerator( |
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name=( |
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datasets.Split.TRAIN |
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if split == "train" |
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else datasets.Split.TEST |
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), |
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gen_kwargs={ |
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"filepaths": data_files, |
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"split_file": split_file, |
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"split": split, |
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"data_metadata": data_metadata, |
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}, |
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), |
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) |
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return ret |
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def _generate_examples(self, filepaths, split_file, split, data_metadata): |
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"""Generate SBI-16-2D examples""" |
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for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)): |
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with fits.open(filepath, memmap=False) as hdul: |
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image_data_1 = hdul[1].data[:, :].tolist() |
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task_instance_key_1 = f"{self.config.name}-{split}-{idx}-HDU1" |
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yield task_instance_key_1, {**{"image": image_data_1}, **item} |
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image_data_4 = hdul[4].data[:, :].tolist() |
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task_instance_key_4 = f"{self.config.name}-{split}-{idx}-HDU4" |
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yield task_instance_key_4, {**{"image": image_data_4}, **item} |
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def make_split_jsonl_files( |
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config_type="tiny", data_dir="./data", outdir="./splits", seed=42 |
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): |
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""" |
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Create jsonl files for the SBI-16-2D dataset. |
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config_type: str, default="tiny" |
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The type of split to create. Options are "tiny" and "full". |
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data_dir: str, default="./data" |
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The directory where the FITS files are located. |
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outdir: str, default="./splits" |
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The directory where the jsonl files will be created. |
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seed: int, default=42 |
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The seed for the random split. |
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""" |
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random.seed(seed) |
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os.makedirs(outdir, exist_ok=True) |
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fits_files = glob(os.path.join(data_dir, "*.fits")) |
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random.shuffle(fits_files) |
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if config_type == "tiny": |
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train_files = fits_files[:2] |
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test_files = fits_files[2:3] |
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elif config_type == "full": |
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split_idx = int(0.8 * len(fits_files)) |
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train_files = fits_files[:split_idx] |
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test_files = fits_files[split_idx:] |
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else: |
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raise ValueError("Unsupported config_type. Use 'tiny' or 'full'.") |
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def create_jsonl(files, split_name): |
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output_file = os.path.join(outdir, f"{config_type}_{split_name}.jsonl") |
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with open(output_file, "w") as out_f: |
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for file in files: |
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print(file, flush=True, end="...") |
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with fits.open(file, memmap=False) as hdul: |
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image_id = os.path.basename(file).split(".fits")[0] |
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ra = hdul["SCI"].header.get("CRVAL1", 0) |
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dec = hdul["SCI"].header.get("CRVAL2", 0) |
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pixscale = hdul["SCI"].header.get("CD1_2", 0.396) |
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item = { |
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"image_id": image_id, |
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"image": file, |
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"ra": ra, |
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"dec": dec, |
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"pixscale": pixscale, |
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} |
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out_f.write(json.dumps(item) + "\n") |
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create_jsonl(train_files, "train") |
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create_jsonl(test_files, "test") |
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