changed setup code to calculate footprints,filename

GBI_16_4D.py

@@ -0,0 +1,208 @@
+import os
+import random
+from glob import glob
+import json
+from huggingface_hub import hf_hub_download
+from tqdm import tqdm
+
+from astropy.io import fits
+from astropy.wcs import WCS
+import datasets
+from datasets import DownloadManager
+from fsspec.core import url_to_fs
+
+
+_DESCRIPTION = (
+"GBI-16-4D is a dataset which is part of the AstroCompress project. It contains data "
+"assembled from the Sloan Digital SkySurvey (SDSS). Each FITS file contains a series "
+"of 800x800 pixel uint16 observations of the same portion of the Stripe82 field, "
+"taken in 5 bandpass filters (u, g, r, i, z) over time. The filenames give the "
+"starting run, field, camcol of the observations, the number of filtered images per "
+"timestep, and the number of timesteps. For example: "
+"`cube_center_run4203_camcol6_f44_35-5-800-800.fits` contains 35 frames of 800x800 "
+"pixel images in 5 bandpasses starting with run 4203, field 44, and camcol 6. "
+"The images are stored in the FITS standard."
+)
+
+_HOMEPAGE = "https://google.github.io/AstroCompress"
+
+_LICENSE = "CC BY 4.0"
+
+_URL = "https://huggingface.co/datasets/AstroCompress/GBI-16-4D/resolve/main/"
+
+_URLS = {
+    "tiny": {
+        "train": "./splits/tiny_train.jsonl",
+        "test": "./splits/tiny_test.jsonl",
+    },
+    "full": {
+        "train": "./splits/full_train.jsonl",
+        "test": "./splits/full_test.jsonl",
+    }
+}
+
+_REPO_ID = "AstroCompress/GBI-16-4D"
+
+class GBI_16_4D(datasets.GeneratorBasedBuilder):
+    """GBI-16-4D Dataset"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="tiny",
+            version=VERSION,
+            description="A small subset of the data, to test downsteam workflows.",
+        ),
+        datasets.BuilderConfig(
+            name="full",
+            version=VERSION,
+            description="The full dataset",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "tiny"
+
+    def __init__(self, **kwargs):
+        super().__init__(version=self.VERSION, **kwargs)
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image": datasets.Array4D(shape=(None, 5, 800, 800), dtype="uint16"),
+                    "ra": datasets.Value("float64"),
+                    "dec": datasets.Value("float64"),
+                    "pixscale": datasets.Value("float64"),
+                    "ntimes": datasets.Value("int64"),
+                    "nbands": datasets.Value("int64"),
+                    "image_id": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation="TBD",
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+
+        ret = []
+        base_path = dl_manager._base_path
+        locally_run = not base_path.startswith(datasets.config.HF_ENDPOINT) 
+        _, path = url_to_fs(base_path)
+
+        for split in ["train", "test"]:
+            if locally_run:
+                split_file_location = os.path.normpath(os.path.join(path, _URLS[self.config.name][split]))
+                split_file = dl_manager.download_and_extract(split_file_location)
+            else:
+                split_file = hf_hub_download(repo_id=_REPO_ID, filename=_URLS[self.config.name][split], repo_type="dataset")
+            with open(split_file, encoding="utf-8") as f:
+                data_filenames = []
+                data_metadata = []
+                for line in f:
+                    item = json.loads(line)
+                    data_filenames.append(item["image"])
+                    data_metadata.append({"ra": item["ra"], 
+                                          "dec": item["dec"], 
+                                          "pixscale": item["pixscale"], 
+                                          "ntimes": item["ntimes"], 
+                                          "nbands": item["nbands"],
+                                          "image_id": item["image_id"]})
+                if locally_run:
+                    data_urls = [os.path.normpath(os.path.join(path,data_filename)) for data_filename in data_filenames]
+                    data_files = [dl_manager.download(data_url) for data_url in data_urls]
+                else:
+                    data_urls = data_filenames
+                    data_files = [hf_hub_download(repo_id=_REPO_ID, filename=data_url, repo_type="dataset") for data_url in data_urls]
+            ret.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN if split == "train" else datasets.Split.TEST,
+                    gen_kwargs={"filepaths": data_files,
+                                "split_file": split_file,
+                                "split": split, 
+                                "data_metadata": data_metadata},
+                ),
+            )
+        return ret
+
+    def _generate_examples(self, filepaths, split_file, split, data_metadata):
+        """Generate GBI-16-4D examples"""
+
+        for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)):
+            task_instance_key = f"{self.config.name}-{split}-{idx}"
+            with fits.open(filepath, memmap=False, ignore_missing_simple=True) as hdul:
+                image_data = hdul[0].data.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[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 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', 0)
+                    dec = hdul[0].header.get('CRVAL2', 0)
+                    pixscale = hdul[0].header.get('CD1_2', 0.396)
+                    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")