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from io import BytesIO |
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import datasets |
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import pandas as pd |
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import numpy as np |
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import json |
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from astropy.io import fits |
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from utils import ParallelZipFile |
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_DESCRIPTION = ( |
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"AstroM3 is a time-series astronomy dataset containing photometry, spectra, " |
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"and metadata features for variable stars. The dataset includes multiple " |
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"subsets (full, sub10, sub25, sub50) and supports different random seeds (42, 66, 0, 12, 123). " |
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"Each sample consists of:\n" |
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"- **Photometry**: Light curve data of shape `(N, 3)` (time, flux, flux_error).\n" |
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"- **Spectra**: Spectral observations of shape `(M, 3)` (wavelength, flux, flux_error).\n" |
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"- **Metadata**: Auxiliary features of shape `(25,)`.\n" |
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"- **Label**: The class name as a string." |
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) |
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_HOMEPAGE = "https://huggingface.co/datasets/AstroM3" |
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_LICENSE = "CC BY 4.0" |
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_URL = "https://huggingface.co/datasets/MeriDK/AstroM3Dataset/resolve/main" |
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_VERSION = datasets.Version("1.0.0") |
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_CITATION = """ |
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@article{AstroM3, |
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title={AstroM3: A Multi-Modal Astronomy Dataset}, |
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author={Your Name}, |
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year={2025}, |
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journal={AstroML Conference} |
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} |
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""" |
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class AstroM3Dataset(datasets.GeneratorBasedBuilder): |
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"""Hugging Face dataset for AstroM3 with configurable subsets and seeds.""" |
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DEFAULT_CONFIG_NAME = "full_42" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name=f"{sub}_{seed}", version=_VERSION, data_dir=None) |
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for sub in ["full", "sub10", "sub25", "sub50"] |
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for seed in [42, 66, 0, 12, 123] |
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] |
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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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"photometry": datasets.Array2D(shape=(None, 3), dtype="float32"), |
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"spectra": datasets.Array2D(shape=(None, 3), dtype="float32"), |
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"metadata": datasets.Sequence(datasets.Value("float32"), length=38), |
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"label": datasets.Value("string"), |
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} |
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), |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _get_photometry(self, file_name): |
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csv = BytesIO() |
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file_name = file_name.replace(' ', '') |
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data_path = f'vardb_files/{file_name}.dat' |
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csv.write(self.reader_v.read(data_path)) |
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csv.seek(0) |
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lc = pd.read_csv(csv, sep=r'\s+', skiprows=2, names=['HJD', 'MAG', 'MAG_ERR', 'FLUX', 'FLUX_ERR'], |
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dtype={'HJD': float, 'MAG': float, 'MAG_ERR': float, 'FLUX': float, 'FLUX_ERR': float}) |
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return lc[['HJD', 'FLUX', 'FLUX_ERR']].values |
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@staticmethod |
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def _get_spectra(file_name): |
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hdulist = fits.open(file_name) |
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len_list = len(hdulist) |
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if len_list == 1: |
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head = hdulist[0].header |
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scidata = hdulist[0].data |
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coeff0 = head['COEFF0'] |
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coeff1 = head['COEFF1'] |
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pixel_num = head['NAXIS1'] |
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specflux = scidata[0,] |
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ivar = scidata[1,] |
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wavelength = np.linspace(0, pixel_num - 1, pixel_num) |
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wavelength = np.power(10, (coeff0 + wavelength * coeff1)) |
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hdulist.close() |
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elif len_list == 2: |
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head = hdulist[0].header |
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scidata = hdulist[1].data |
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wavelength = scidata[0][2] |
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ivar = scidata[0][1] |
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specflux = scidata[0][0] |
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else: |
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raise ValueError(f'Wrong number of fits files. {len_list} should be 1 or 2') |
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return np.vstack((wavelength, specflux, ivar)).T |
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@staticmethod |
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def _transform_metadata(row, info): |
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row_copy = row.copy(deep=True) |
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for transformation_type, value in info["metadata_func"].items(): |
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if transformation_type == "abs": |
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for col in value: |
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row_copy[col] = ( |
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row_copy[col] - 10 + 5 * np.log10(np.where(row_copy["parallax"] <= 0, 1, row_copy["parallax"])) |
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) |
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elif transformation_type == "cos": |
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for col in value: |
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row_copy[col] = np.cos(np.radians(row_copy[col])) |
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elif transformation_type == "sin": |
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for col in value: |
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row_copy[col] = np.sin(np.radians(row_copy[col])) |
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elif transformation_type == "log": |
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for col in value: |
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row_copy[col] = np.log10(row_copy[col]) |
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row_copy = (row_copy - info["mean"]) / info["std"] |
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return row_copy |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators for train, val, and test.""" |
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sub, seed = self.config.name.split("_") |
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urls = { |
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"train": f"{_URL}/splits/{sub}/{seed}/train.csv", |
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"val": f"{_URL}/splits/{sub}/{seed}/val.csv", |
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"test": f"{_URL}/splits/{sub}/{seed}/test.csv", |
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"info": f"{_URL}/splits/{sub}/{seed}/info.json", |
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} |
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extracted_path = dl_manager.download(urls) |
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df1 = pd.read_csv(extracted_path["train"]) |
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df2 = pd.read_csv(extracted_path["val"]) |
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df3 = pd.read_csv(extracted_path["test"]) |
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df_combined = pd.concat([df1, df2, df3], ignore_index=True) |
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spectra_urls = {} |
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for _, row in df_combined.iterrows(): |
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spectra_urls[row["spec_filename"]] = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}" |
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spectra_files = dl_manager.download(spectra_urls) |
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photometry_path = dl_manager.download(f"{_URL}/photometry.zip") |
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self.reader_v = ParallelZipFile(photometry_path) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"csv_path": extracted_path["train"], |
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"info_path": extracted_path["info"], |
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"spectra_files": spectra_files, |
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"split": "train"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"csv_path": extracted_path["val"], |
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"info_path": extracted_path["info"], |
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"spectra_files": spectra_files, |
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"split": "val"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"csv_path": extracted_path["test"], |
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"info_path": extracted_path["info"], |
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"spectra_files": spectra_files, |
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"split": "test"} |
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), |
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] |
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def _generate_examples(self, csv_path, info_path, spectra_files, split): |
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"""Yields examples from a CSV file containing photometry, spectra, metadata, and labels.""" |
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df = pd.read_csv(csv_path) |
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with open(info_path) as f: |
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info = json.loads(f.read()) |
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for i, (idx, row) in enumerate(df.iterrows()): |
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photometry = self._get_photometry(row["name"]) |
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spectra = self._get_spectra(spectra_files[row["spec_filename"]]) |
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metadata = row[info["all_cols"]] |
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yield idx, { |
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"photometry": photometry, |
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"spectra": spectra, |
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"metadata": row[info["all_cols"]], |
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"label": row["target"], |
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} |
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