| import os |
| import random |
| from glob import glob |
| import json |
| from huggingface_hub import hf_hub_download |
| from tqdm import tqdm |
| import numpy as np |
|
|
| from astropy.io import fits |
| from astropy.wcs import WCS |
| import datasets |
| from datasets import DownloadManager |
| from fsspec.core import url_to_fs |
|
|
|
|
| def get_fits_footprint(fits_path): |
| """ |
| Process a FITS file to extract WCS information and calculate the footprint. |
| |
| Parameters: |
| fits_path (str): Path to the FITS file. |
| |
| Returns: |
| tuple: A tuple containing the WCS footprint coordinates. |
| """ |
| with fits.open(fits_path) as hdul: |
| hdul[1].data = hdul[1].data[0, 0] |
| wcs = WCS(hdul[1].header) |
| shape = sorted(tuple(wcs.pixel_shape))[:2] |
| footprint = wcs.calc_footprint(axes=shape) |
| coords = list(footprint.flatten()) |
| return coords |
|
|
|
|
| def calculate_pixel_scale(header): |
| """ |
| Calculate the pixel scale in arcseconds per pixel from a FITS header. |
| |
| Parameters: |
| header (astropy.io.fits.header.Header): The FITS header containing WCS information. |
| |
| Returns: |
| Mean of the pixel scales in x and y. |
| """ |
| |
| |
| pixscale_x = header.get('CDELT1', np.nan) |
| pixscale_y = header.get('CDELT2', np.nan) |
| |
| return np.mean([pixscale_x, pixscale_y]) |
|
|
|
|
| def make_split_jsonl_files(config_type="tiny", data_dir="./data", |
| outdir="./splits", seed=42): |
| """ |
| Create jsonl files for the SBI-16-3D dataset. |
| |
| config_type: str, default="tiny" |
| The type of split to create. Options are "tiny" and "full". |
| data_dir: str, default="./data" |
| The directory where the FITS files are located. |
| outdir: str, default="./splits" |
| The directory where the jsonl files will be created. |
| seed: int, default=42 |
| The seed for the random split. |
| """ |
| random.seed(seed) |
| os.makedirs(outdir, exist_ok=True) |
|
|
| fits_files = glob(os.path.join(data_dir, "*.fits")) |
| random.shuffle(fits_files) |
| if config_type == "tiny": |
| train_files = fits_files[:2] |
| test_files = fits_files[2:3] |
| elif config_type == "full": |
| split_idx = int(0.8 * len(fits_files)) |
| train_files = fits_files[:split_idx] |
| test_files = fits_files[split_idx:] |
| else: |
| raise ValueError("Unsupported config_type. Use 'tiny' or 'full'.") |
|
|
| def create_jsonl(files, split_name): |
| output_file = os.path.join(outdir, f"{config_type}_{split_name}.jsonl") |
| with open(output_file, "w") as out_f: |
| for file in tqdm(files): |
| |
| with fits.open(file, memmap=False) as hdul: |
| image_id = os.path.basename(file).split(".fits")[0] |
| ra = hdul["SCI"].header.get('CRVAL1', 0) |
| dec = hdul["SCI"].header.get('CRVAL2', 0) |
| pixscale = calculate_pixel_scale(hdul["SCI"].header) |
| footprint = get_fits_footprint(file) |
| read_pattern = hdul[0].header.get('READPATT', 0) |
| |
| ntimes = hdul["SCI"].data.shape[1] |
| item = {"image_id": image_id, "image": file, "ra": ra, "dec": dec, |
| "pixscale": pixscale, "ntimes": ntimes, "read_pattern": read_pattern, "footprint": footprint} |
| out_f.write(json.dumps(item) + "\n") |
|
|
| create_jsonl(train_files, "train") |
| create_jsonl(test_files, "test") |
|
|
| |
| if __name__ == "__main__": |
| make_split_jsonl_files("tiny") |
| make_split_jsonl_files("full") |
