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import os |
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import random |
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from glob import glob |
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import json |
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from huggingface_hub import hf_hub_download |
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from tqdm import tqdm |
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import numpy as np |
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from astropy.io import fits |
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from astropy.wcs import WCS |
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import datasets |
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from datasets import DownloadManager |
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from fsspec.core import url_to_fs |
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def get_fits_footprint(fits_path): |
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""" |
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Process a FITS file to extract WCS information and calculate the footprint. |
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Parameters: |
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fits_path (str): Path to the FITS file. |
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Returns: |
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tuple: A tuple containing the WCS footprint coordinates. |
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""" |
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with fits.open(fits_path) as hdul: |
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hdul[0].data = hdul[0].data[0, 0] |
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wcs = WCS(hdul[0].header) |
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shape = sorted(tuple(wcs.pixel_shape))[:2] |
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footprint = wcs.calc_footprint(axes=shape) |
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coords = list(footprint.flatten()) |
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return coords |
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def calculate_pixel_scale(header): |
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""" |
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Calculate the pixel scale in arcseconds per pixel from a FITS header. |
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Parameters: |
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header (astropy.io.fits.header.Header): The FITS header containing WCS information. |
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Returns: |
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Mean of the pixel scales in x and y. |
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""" |
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cd1_1 = header.get('CD1_1', np.nan) |
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cd1_2 = header.get('CD1_2', np.nan) |
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cd2_1 = header.get('CD2_1', np.nan) |
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cd2_2 = header.get('CD2_2', np.nan) |
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pixscale_x = np.sqrt(cd1_1**2 + cd1_2**2) * 3600 |
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pixscale_y = np.sqrt(cd2_1**2 + cd2_2**2) * 3600 |
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return np.mean([pixscale_x, pixscale_y]) |
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def make_split_jsonl_files(config_type="tiny", data_dir="./data", |
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outdir="./splits", seed=42): |
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""" |
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Create jsonl files for the GBI-16-4D dataset. |
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config_type: str, default="tiny" |
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The type of split to create. Options are "tiny" and "full". |
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data_dir: str, default="./data" |
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The directory where the FITS files are located. |
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outdir: str, default="./splits" |
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The directory where the jsonl files will be created. |
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seed: int, default=42 |
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The seed for the random split. |
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""" |
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random.seed(seed) |
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os.makedirs(outdir, exist_ok=True) |
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fits_files = glob(os.path.join(data_dir, "*.fits")) |
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random.shuffle(fits_files) |
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if config_type == "tiny": |
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train_files = fits_files[:2] |
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test_files = fits_files[2:3] |
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elif config_type == "full": |
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split_idx = int(0.8 * len(fits_files)) |
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train_files = fits_files[:split_idx] |
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test_files = fits_files[split_idx:] |
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else: |
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raise ValueError("Unsupported config_type. Use 'tiny' or 'full'.") |
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def create_jsonl(files, split_name): |
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output_file = os.path.join(outdir, f"{config_type}_{split_name}.jsonl") |
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with open(output_file, "w") as out_f: |
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for file in tqdm(files): |
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with fits.open(file, memmap=False, ignore_missing_simple=True) as hdul: |
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image_id = os.path.basename(file).split(".fits")[0] |
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ra = hdul[0].header.get('CRVAL1', np.nan) |
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dec = hdul[0].header.get('CRVAL2', np.nan) |
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pixscale = calculate_pixel_scale(hdul[0].header) |
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ntimes = hdul[0].data.shape[0] |
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nbands = hdul[0].data.shape[1] |
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footprint = get_fits_footprint(file) |
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item = {"image_id": image_id, "image": file, "ra": ra, "dec": dec, |
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"pixscale": pixscale, "ntimes": ntimes, "nbands": nbands, "footprint": footprint} |
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out_f.write(json.dumps(item) + "\n") |
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create_jsonl(train_files, "train") |
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create_jsonl(test_files, "test") |
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if __name__ == "__main__": |
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make_split_jsonl_files("tiny") |
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make_split_jsonl_files("full") |
