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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 tqdm import tqdm |
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import numpy as np |
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from astropy.io import fits |
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from astropy.wcs import WCS |
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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-16-3D is a dataset which is part of the AstroCompress project. """ |
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"""It contains data assembled from the James Webb Space Telescope (JWST). """ |
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"""<TODO>Describe data format</TODO>""" |
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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-16-3D/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-16-3D" |
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class SBI_16_4D(datasets.GeneratorBasedBuilder): |
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"""SBI-16-4D 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.Array3D(shape=(None, 2048, 2048), dtype="uint16"), |
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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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"ntimes": datasets.Value("int64"), |
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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(os.path.join(path, _URLS[self.config.name][split])) |
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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(repo_id=_REPO_ID, filename=_URLS[self.config.name][split], repo_type="dataset") |
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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({"ra": item["ra"], |
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"dec": item["dec"], |
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"pixscale": item["pixscale"], |
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"ntimes": item["ntimes"], |
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"image_id": item["image_id"]}) |
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if locally_run: |
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data_urls = [os.path.normpath(os.path.join(path,data_filename)) for data_filename in data_filenames] |
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data_files = [dl_manager.download(data_url) for data_url in data_urls] |
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else: |
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data_urls = data_filenames |
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data_files = [hf_hub_download(repo_id=_REPO_ID, filename=data_url, repo_type="dataset") for data_url in data_urls] |
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ret.append( |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN if split == "train" else datasets.Split.TEST, |
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gen_kwargs={"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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return ret |
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def _generate_examples(self, filepaths, split_file, split, data_metadata): |
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"""Generate GBI-16-4D examples""" |
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for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)): |
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task_instance_key = f"{self.config.name}-{split}-{idx}" |
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with fits.open(filepath, memmap=False) as hdul: |
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image_data = hdul["SCI"].data[0,:,:,:].tolist() |
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yield task_instance_key, {**{"image": image_data}, **item} |
