| import os |
| from io import BytesIO |
| import datasets |
| import pandas as pd |
| import numpy as np |
| import json |
| from astropy.io import fits |
|
|
| from utils.parallelzipfile import ParallelZipFile as ZipFile |
|
|
| _DESCRIPTION = ( |
| "AstroM3 is a time-series astronomy dataset containing photometry, spectra, " |
| "and metadata features for variable stars. The dataset includes multiple " |
| "subsets (full, sub10, sub25, sub50) and supports different random seeds (42, 66, 0, 12, 123). " |
| "Each sample consists of:\n" |
| "- **Photometry**: Light curve data of shape `(N, 3)` (time, flux, flux_error).\n" |
| "- **Spectra**: Spectral observations of shape `(M, 3)` (wavelength, flux, flux_error).\n" |
| "- **Metadata**: Auxiliary features of shape `(25,)`.\n" |
| "- **Label**: The class name as a string." |
| ) |
|
|
| _HOMEPAGE = "https://huggingface.co/datasets/AstroM3" |
| _LICENSE = "CC BY 4.0" |
| _URL = "https://huggingface.co/datasets/MeriDK/AstroM3Dataset/resolve/main" |
| _VERSION = datasets.Version("1.0.0") |
|
|
| _CITATION = """ |
| @article{AstroM3, |
| title={AstroM3: A Multi-Modal Astronomy Dataset}, |
| author={Your Name}, |
| year={2025}, |
| journal={AstroML Conference} |
| } |
| """ |
|
|
|
|
| class AstroM3Dataset(datasets.GeneratorBasedBuilder): |
| """Hugging Face dataset for AstroM3 with configurable subsets and seeds.""" |
|
|
| DEFAULT_CONFIG_NAME = "full_42" |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig(name=f"{sub}_{seed}", version=_VERSION, data_dir=None) |
| for sub in ["full", "sub10", "sub25", "sub50"] |
| for seed in [42, 66, 0, 12, 123] |
| ] |
|
|
| def _info(self): |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=datasets.Features( |
| { |
| "photometry": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)), |
| "spectra": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), length=3)), |
| "metadata": datasets.Sequence(datasets.Value("float32"), length=25), |
| "label": datasets.Value("string"), |
| } |
| ), |
| homepage=_HOMEPAGE, |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _get_photometry(self, file_name): |
| csv = BytesIO() |
| file_name = file_name.replace(' ', '') |
| data_path = f'vardb_files/{file_name}.dat' |
|
|
| csv.write(self.reader_v.read(data_path)) |
| csv.seek(0) |
|
|
| lc = pd.read_csv(csv, sep=r'\s+', skiprows=2, names=['HJD', 'MAG', 'MAG_ERR', 'FLUX', 'FLUX_ERR'], |
| dtype={'HJD': float, 'MAG': float, 'MAG_ERR': float, 'FLUX': float, 'FLUX_ERR': float}) |
|
|
| return lc[['HJD', 'FLUX', 'FLUX_ERR']].values |
|
|
| @staticmethod |
| def _get_spectra(file_name): |
| hdulist = fits.open(file_name) |
| len_list = len(hdulist) |
|
|
| if len_list == 1: |
| head = hdulist[0].header |
| scidata = hdulist[0].data |
| coeff0 = head['COEFF0'] |
| coeff1 = head['COEFF1'] |
| pixel_num = head['NAXIS1'] |
| specflux = scidata[0,] |
| ivar = scidata[1,] |
| wavelength = np.linspace(0, pixel_num - 1, pixel_num) |
| wavelength = np.power(10, (coeff0 + wavelength * coeff1)) |
| hdulist.close() |
| elif len_list == 2: |
| head = hdulist[0].header |
| scidata = hdulist[1].data |
| wavelength = scidata[0][2] |
| ivar = scidata[0][1] |
| specflux = scidata[0][0] |
| else: |
| raise ValueError(f'Wrong number of fits files. {len_list} should be 1 or 2') |
|
|
| return np.vstack((wavelength, specflux, ivar)).T |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators for train, val, and test.""" |
|
|
| |
| sub, seed = self.config.name.split("_") |
|
|
| |
| urls = { |
| "train": f"{_URL}/splits/{sub}/{seed}/train.csv", |
| "val": f"{_URL}/splits/{sub}/{seed}/val.csv", |
| "test": f"{_URL}/splits/{sub}/{seed}/test.csv", |
| "info": f"{_URL}/splits/{sub}/{seed}/info.json", |
| } |
| extracted_path = dl_manager.download_and_extract(urls) |
|
|
| |
| spectra_urls = {} |
|
|
| for split in ["train", "val", "test"]: |
| df = pd.read_csv(extracted_path[split]) |
| for _, row in df.iterrows(): |
| spectra_url = f"{_URL}/spectra/{split}/{row['target']}/{row['spec_filename']}" |
| spectra_urls[row["spec_filename"]] = spectra_url |
|
|
| spectra = dl_manager.download_and_extract(spectra_urls) |
|
|
| |
| photometry_path = dl_manager.download(f"{_URL}/photometry.zip") |
| self.reader_v = ZipFile(photometry_path) |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"], |
| "info_path": extracted_path["info"], |
| "spectra": spectra, |
| "split": "train"} |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"], |
| "info_path": extracted_path["info"], |
| "spectra": spectra, |
| "split": "val"} |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"], |
| "info_path": extracted_path["info"], |
| "spectra": spectra, |
| "split": "test"} |
| ), |
| ] |
|
|
| def _generate_examples(self, csv_path, info_path, spectra, split): |
| """Yields examples from a CSV file containing photometry, spectra, metadata, and labels.""" |
|
|
| if not os.path.exists(csv_path): |
| raise FileNotFoundError(f"Missing dataset file: {csv_path}") |
|
|
| if not os.path.exists(info_path): |
| raise FileNotFoundError(f"Missing info file: {info_path}") |
|
|
| df = pd.read_csv(csv_path) |
|
|
| with open(info_path) as f: |
| info = json.loads(f.read()) |
|
|
| for idx, row in df.iterrows(): |
| photometry = self._get_photometry(row["name"]) |
| spectra = self._get_spectra(spectra[row['spec_filename']]) |
|
|
| yield idx, { |
| "photometry": photometry, |
| "spectra": spectra, |
| "metadata": row[info["all_cols"]], |
| "label": row["target"], |
| } |
|
|
