utils/create_splits.py

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[0].data = hdul[0].data[0, 0]
 wcs = WCS(hdul[0].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.
 """
 # Extract the CD matrix elements
 cd1_1 = header.get('CD1_1', np.nan)
 cd1_2 = header.get('CD1_2', np.nan)
 cd2_1 = header.get('CD2_1', np.nan)
 cd2_2 = header.get('CD2_2', np.nan)
 
 # Calculate the pixel scales in arcseconds per pixel
 pixscale_x = np.sqrt(cd1_1**2 + cd1_2**2) * 3600
 pixscale_y = np.sqrt(cd2_1**2 + cd2_2**2) * 3600
 
 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 GBI-16-4D 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):
 #print(file, flush=True, end="...")
 with fits.open(file, memmap=False, ignore_missing_simple=True) as hdul:
 image_id = os.path.basename(file).split(".fits")[0]
 ra = hdul[0].header.get('CRVAL1', np.nan)
 dec = hdul[0].header.get('CRVAL2', np.nan)
 pixscale = calculate_pixel_scale(hdul[0].header)
 ntimes = hdul[0].data.shape[0]
 nbands = hdul[0].data.shape[1]
 footprint = get_fits_footprint(file)
 item = {"image_id": image_id, "image": file, "ra": ra, "dec": dec, 
 "pixscale": pixscale, "ntimes": ntimes, "nbands": nbands, "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")