new utils and splits

splits/tiny_test.jsonl

@@ -1 +1 @@
-{"ra1": 203.7732409387, "dec1": 13.8489878334, "ra2": 203.7214653161, "dec2": 13.8777594581, "ra3": 203.7050942963, "dec3": 13.8539247351, "ra4": 203.7568666382, "dec4": 13.8251565401, "exposure_time": 390.0, "image": "./data/jdrz89fdq_raw.fits", "image_id": "jdrz89fdq_raw", "ra": 203.7436405194, "dec": 13.83161538103, "pixscale": -1.21332e-05}
+{"image_id": "jdrz11n7q_raw", "image": "../data/jdrz11n7q_raw.fits", "ra": 4.608953170523, "dec": 30.08292391635, "pixscale": 0.05008251063628878, "footprint": [[4.596537292754619, 30.114743173326985, 4.551509758403926, 30.071884367648646, 4.574426433594073, 30.05121263351455, 4.619454495882632, 30.09406362790967], [4.57446707043176, 30.093260596142066, 4.531317302276631, 30.051542448993004, 4.553651889505143, 30.030857573994318, 4.596802061267953, 30.072568429035663]]}

splits/tiny_train.jsonl

@@ -1,2 +1,2 @@
-{"ra1": 260.2513190778, "dec1": -19.6024730349, "ra2": 260.3018066706, "dec2": -19.6355333113, "ra3": 260.3208290377, "dec3": -19.6131799119, "ra4": 260.2703445491, "dec4": -19.5801249252, "exposure_time": 493.0, "image": "./data/jdig01dbq_raw.fits", "image_id": "jdig01dbq_raw", "ra": 260.2833237316, "dec": -19.58768272612, "pixscale": 1.14791e-05}
-{"ra1": 11.77879924, "dec1": -21.6292955803, "ra2": 11.7313561082, "dec2": -21.6668467959, "ra3": 11.7496899151, "dec3": -21.6898781547, "ra4": 11.7971358575, "dec4": -21.6523217318, "exposure_time": 279.0, "image": "./data/jeyv02heq_raw.fits", "image_id": "jeyv02heq_raw", "ra": 11.78591952774, "dec": -21.6622203537, "pixscale": -1.06419e-05}
+{"image_id": "jdne07fnq_raw", "image": "../data/jdne07fnq_raw.fits", "ra": 58.15894870986, "dec": 35.69119598576, "pixscale": 0.05011143018356258, "footprint": [[58.14032335605238, 35.66121385345633, 58.211555242689485, 35.66350683314965, 58.212584136362814, 35.69214259312979, 58.14132664729774, 35.68985021861018], [58.17565225163017, 35.66268076088015, 58.24449094130459, 35.6652962486248, 58.24598074938274, 35.6935841665628, 58.17711759640468, 35.690969525771244]]}
+{"image_id": "j9fc57s3q_raw", "image": "../data/j9fc57s3q_raw.fits", "ra": 194.5182600318, "dec": 47.0326648271, "pixscale": 0.050102208663596326, "footprint": [[194.56725398623578, 47.029107445340784, 194.53407080188407, 47.08243223084805, 194.4943150205125, 47.07306667853927, 194.527532100821, 47.01975336809004], [194.5503582969605, 47.05544175524446, 194.5176842457192, 47.10682001922871, 194.47820028351606, 47.097913156369316, 194.51090691984348, 47.04654611406288]]}

utils/create_splits.py

@@ -0,0 +1,121 @@
+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:
+        wcs = WCS(hdul[1].header)
+        shape = sorted(tuple(wcs.pixel_shape))[:2]
+        footprint = wcs.calc_footprint(axes=shape)
+        coords1 = list(footprint.flatten())
+        
+        wcs = WCS(hdul[4].header)
+        shape = sorted(tuple(wcs.pixel_shape))[:2]
+        footprint = wcs.calc_footprint(axes=shape)
+        coords2 = list(footprint.flatten())
+        
+    return coords1, coords2
+
+
+def calculate_pixel_scale(header):
+    """
+    Calculate the pixel scale separately for X and Y directions and return the mean pixel scale from a FITS header.
+    
+    Args:
+    header: A FITS header object containing CD1_1, CD1_2, CD2_1, and CD2_2.
+    
+    Returns:
+    mean_pixscale: The mean pixel scale in arcseconds per pixel.
+    """
+    # Extract CD matrix elements
+    CD1_1 = header['CD1_1']
+    CD1_2 = header['CD1_2']
+    CD2_1 = header['CD2_1']
+    CD2_2 = header['CD2_2']
+    
+    # Calculate pixel scales
+    pixscale_x = (CD1_1**2 + CD1_2**2)**0.5 * 3600  # Convert from degrees to arcseconds
+    pixscale_y = (CD2_1**2 + CD2_2**2)**0.5 * 3600  # Convert from degrees to arcseconds
+    
+    # Calculate mean pixel scale
+    mean_pixscale = (pixscale_x + pixscale_y) / 2
+    
+    return mean_pixscale
+
+
+def make_split_jsonl_files(
+    config_type="tiny", data_dir="../data", outdir="../splits", seed=42
+):
+    """
+    Create jsonl files for the SBI-16-2D 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) 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[1].header)
+                    footprint = get_fits_footprint(file)
+                    item = {
+                        "image_id": image_id,
+                        "image": file,
+                        "ra": ra,
+                        "dec": dec,
+                        "pixscale": pixscale,
+                        "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")