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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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wcs = WCS(hdul[1].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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coords1 = list(footprint.flatten()) |
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wcs = WCS(hdul[4].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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coords2 = list(footprint.flatten()) |
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return coords1, coords2 |
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def calculate_pixel_scale(header): |
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""" |
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Calculate the pixel scale separately for X and Y directions and return the mean pixel scale from a FITS header. |
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Args: |
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header: A FITS header object containing CD1_1, CD1_2, CD2_1, and CD2_2. |
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Returns: |
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mean_pixscale: The mean pixel scale in arcseconds per pixel. |
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""" |
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CD1_1 = header['CD1_1'] |
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CD1_2 = header['CD1_2'] |
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CD2_1 = header['CD2_1'] |
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CD2_2 = header['CD2_2'] |
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pixscale_x = (CD1_1**2 + CD1_2**2)**0.5 * 3600 |
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pixscale_y = (CD2_1**2 + CD2_2**2)**0.5 * 3600 |
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mean_pixscale = (pixscale_x + pixscale_y) / 2 |
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return mean_pixscale |
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def make_split_jsonl_files( |
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config_type="tiny", data_dir="./data", outdir="./splits", seed=42 |
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): |
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""" |
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Create jsonl files for the SBI-16-2D 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) as hdul: |
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image_id = os.path.basename(file).split(".fits")[0] |
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ra = hdul["SCI"].header.get("CRVAL1", 0) |
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dec = hdul["SCI"].header.get("CRVAL2", 0) |
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exptime = hdul[0].header.get("EXPTIME", -1) |
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pixscale = calculate_pixel_scale(hdul[1].header) |
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footprint = get_fits_footprint(file) |
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item = { |
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"image_id": image_id, |
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"image": file, |
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"ra": ra, |
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"dec": dec, |
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"pixscale": pixscale, |
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"exposure_time": exptime, |
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"footprint": footprint, |
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} |
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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") |