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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=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
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 = dl_manager.download(spectra_urls)
# Load photometry and init reader
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"],
}
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