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import os
import random
from glob import glob
import json
from huggingface_hub import hf_hub_download
from astropy.io import fits
import datasets
from datasets import DownloadManager
from fsspec.core import url_to_fs
_DESCRIPTION = """
SBI-16-2D is a dataset which is part of the AstroCompress project.
It contains imaging data assembled from the Hubble Space Telescope (HST).
"""
_HOMEPAGE = "https://google.github.io/AstroCompress"
_LICENSE = "CC BY 4.0"
_URL = "https://huggingface.co/datasets/AstroCompress/SBI-16-2D/resolve/main/"
_URLS = {
"tiny": {
"train": "./splits/tiny_train.jsonl",
"test": "./splits/tiny_test.jsonl",
},
"full": {
"train": "./splits/full_train.jsonl",
"test": "./splits/full_test.jsonl",
},
}
_REPO_ID = "AstroCompress/SBI-16-2D"
class SBI_16_2D(datasets.GeneratorBasedBuilder):
"""SBI-16-2D Dataset"""
VERSION = datasets.Version("1.0.3")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="tiny",
version=VERSION,
description="A small subset of the data, to test downsteam workflows.",
),
datasets.BuilderConfig(
name="full",
version=VERSION,
description="The full dataset",
),
]
DEFAULT_CONFIG_NAME = "tiny"
def __init__(self, **kwargs):
super().__init__(version=self.VERSION, **kwargs)
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(decode=True, mode="I;16"),
"ra": datasets.Value("float64"),
"dec": datasets.Value("float64"),
"pixscale": datasets.Value("float64"),
"image_id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation="TBD",
)
def _split_generators(self, dl_manager: DownloadManager):
ret = []
base_path = dl_manager._base_path
locally_run = not base_path.startswith(datasets.config.HF_ENDPOINT)
_, path = url_to_fs(base_path)
for split in ["train", "test"]:
if locally_run:
split_file_location = os.path.normpath(
os.path.join(path, _URLS[self.config.name][split])
)
split_file = dl_manager.download_and_extract(split_file_location)
else:
split_file = hf_hub_download(
repo_id=_REPO_ID,
filename=_URLS[self.config.name][split],
repo_type="dataset",
)
with open(split_file, encoding="utf-8") as f:
data_filenames = []
data_metadata = []
for line in f:
item = json.loads(line)
data_filenames.append(item["image"])
data_metadata.append(
{
"ra": item["ra"],
"dec": item["dec"],
"pixscale": item["pixscale"],
"image_id": item["image_id"],
}
)
if locally_run:
data_urls = [
os.path.normpath(os.path.join(path, data_filename))
for data_filename in data_filenames
]
data_files = [
dl_manager.download(data_url) for data_url in data_urls
]
else:
data_urls = data_filenames
data_files = [
hf_hub_download(
repo_id=_REPO_ID, filename=data_url, repo_type="dataset"
)
for data_url in data_urls
]
ret.append(
datasets.SplitGenerator(
name=(
datasets.Split.TRAIN
if split == "train"
else datasets.Split.TEST
),
gen_kwargs={
"filepaths": data_files,
"split_file": split_file,
"split": split,
"data_metadata": data_metadata,
},
),
)
return ret
def _generate_examples(self, filepaths, split_file, split, data_metadata):
"""Generate SBI-16-2D examples"""
for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)):
with fits.open(filepath, memmap=False) as hdul:
# Process image data from HDU index 1
image_data_1 = hdul[1].data[:, :].tolist()
task_instance_key_1 = f"{self.config.name}-{split}-{idx}-HDU1"
yield task_instance_key_1, {**{"image": image_data_1}, **item}
# Process image data from HDU index 4
image_data_4 = hdul[4].data[:, :].tolist()
task_instance_key_4 = f"{self.config.name}-{split}-{idx}-HDU4"
yield task_instance_key_4, {**{"image": image_data_4}, **item} |