from io import BytesIO import datasets import pandas as pd import numpy as np import json from astropy.io import fits from utils import ParallelZipFile _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.Array2D(shape=(None, 3), dtype="float32"), "spectra": datasets.Array2D(shape=(None, 3), dtype="float32"), "metadata": datasets.Sequence(datasets.Value("float32"), length=38), "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 @staticmethod def _transform_metadata(row, info): row_copy = row.copy(deep=True) for transformation_type, value in info["metadata_func"].items(): if transformation_type == "abs": for col in value: row_copy[col] = ( row_copy[col] - 10 + 5 * np.log10(np.where(row_copy["parallax"] <= 0, 1, row_copy["parallax"])) ) elif transformation_type == "cos": for col in value: row_copy[col] = np.cos(np.radians(row_copy[col])) elif transformation_type == "sin": for col in value: row_copy[col] = np.sin(np.radians(row_copy[col])) elif transformation_type == "log": for col in value: row_copy[col] = np.log10(row_copy[col]) row_copy = (row_copy - info["mean"]) / info["std"] return row_copy def _split_generators(self, dl_manager): """Returns SplitGenerators for train, val, and test.""" # Get subset and seed info from the name sub, seed = self.config.name.split("_") # Load the splits and info files 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(urls) df1 = pd.read_csv(extracted_path["train"]) df2 = pd.read_csv(extracted_path["val"]) df3 = pd.read_csv(extracted_path["test"]) df_combined = pd.concat([df1, df2, df3], ignore_index=True) # Load all spectra files spectra_urls = {} for _, row in df_combined.iterrows(): spectra_urls[row["spec_filename"]] = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}" spectra_files = dl_manager.download(spectra_urls) # Load photometry and init reader photometry_path = dl_manager.download(f"{_URL}/photometry.zip") self.reader_v = ParallelZipFile(photometry_path) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"], "info_path": extracted_path["info"], "spectra_files": spectra_files, "split": "train"} ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"], "info_path": extracted_path["info"], "spectra_files": spectra_files, "split": "val"} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"], "info_path": extracted_path["info"], "spectra_files": spectra_files, "split": "test"} ), ] def _generate_examples(self, csv_path, info_path, spectra_files, split): """Yields examples from a CSV file containing photometry, spectra, metadata, and labels.""" df = pd.read_csv(csv_path) with open(info_path) as f: info = json.loads(f.read()) for i, (idx, row) in enumerate(df.iterrows()): photometry = self._get_photometry(row["name"]) spectra = self._get_spectra(spectra_files[row["spec_filename"]]) metadata = row[info["all_cols"]] # metadata_norm = self._transform_metadata(metadata, info) # yield idx, { # "photometry": photometry, # "spectra": spectra, # "metadata": { # "original": { # "photometry": metadata[info["photo_cols"]].to_dict(), # "metadata": metadata[info["meta_cols"]].to_dict() # }, # "transformed": { # "photometry": metadata_norm[info["photo_cols"]].to_dict(), # "metadata": metadata_norm[info["meta_cols"]].to_dict() # } # }, # "label": row["target"], # } yield idx, { "photometry": photometry, "spectra": spectra, "metadata": row[info["all_cols"]], "label": row["target"], }