removed old filename

GBI-16-4D.py

@@ -1,184 +0,0 @@
-import os
-import random
-from glob import glob
-import json
-from huggingface_hub import hf_hub_download
-
-
-from astropy.io import fits
-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) as hdul:
-                image_data = hdul[0].data.tolist()
-                yield task_instance_key, {**{"image": image_data}, **item}
-
-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 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[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]
-                    item = {"image_id": image_id, "image": file, "ra": ra, "dec": dec, 
-                            "pixscale": pixscale, "ntimes": ntimes, "nbands": nbands}
-                    out_f.write(json.dumps(item) + "\n")
-
-    create_jsonl(train_files, "train")
-    create_jsonl(test_files, "test")