utils folder

SBI-16-3D.py

@@ -129,96 +129,4 @@ class SBI_16_4D(datasets.GeneratorBasedBuilder):
                 # the first axis is integrations one, so we take the first element
                 # the second axis is the groups (time) axis and varies between images
                 image_data = hdul["SCI"].data[0,:,:,:].tolist()
-                yield task_instance_key, {**{"image": image_data}, **item}
-
-
-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.
-    """
-    
-    # Calculate the pixel scales in arcseconds per pixel
-    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):
-                #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["SCI"].header)
-                    footprint = get_fits_footprint(file)
-                    # get the number of groups per int
-                    ntimes = hdul["SCI"].data.shape[1]
-                    item = {"image_id": image_id, "image": file, "ra": ra, "dec": dec, 
-                            "pixscale": pixscale, "ntimes": ntimes, "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")
+                yield task_instance_key, {**{"image": image_data}, **item}

utils/create_splits.py

@@ -0,0 +1,105 @@
+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.
+    """
+    
+    # Calculate the pixel scales in arcseconds per pixel
+    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):
+                #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["SCI"].header)
+                    footprint = get_fits_footprint(file)
+                    # get the number of groups per int
+                    ntimes = hdul["SCI"].data.shape[1]
+                    item = {"image_id": image_id, "image": file, "ra": ra, "dec": dec, 
+                            "pixscale": pixscale, "ntimes": ntimes, "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")