add dataloader and splits

GBI-16-4D.py

@@ -0,0 +1,157 @@
+import os
+import random
+from glob import glob
+import json
+
+from astropy.io import fits
+import datasets
+
+
+_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"
+
+_URLS = {
+    "tiny": {
+        "train": "./splits/tiny_train.jsonl",
+        "test": "./splits/tiny_test.jsonl",
+    },
+    "full": {
+        "train": "./splits/full_train.jsonl",
+        "test": "./splits/full_test.jsonl",
+    }
+}
+
+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,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+
+        urls = _URLS[self.config.name]
+
+        data_dir = dl_manager.download_and_extract(urls) 
+        ret = []
+        if "train" in data_dir:
+            ret.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={"filepath": data_dir["train"],
+                                "split": "train"},
+                ),
+            )
+        if "test" in data_dir:
+            ret.append(
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={"filepath": data_dir["test"],
+                                "split": "test"},
+                ),
+            )
+        return ret
+
+    def _generate_examples(self, filepath, split):
+        """Generate GBI-16-4D examples"""
+
+        with open(filepath, "r") as f:
+            idx = 0
+            for line in f:
+                task_instance_key = f"{self.config.name}-{split}-{idx}"
+                item = json.loads(line)
+                image_path = item["image"]
+                with fits.open(image_path, memmap=False) as hdul:
+                    image_data = hdul[0].data
+                    yield task_instance_key, {"image": image_data, 
+                                            "ra": item["ra"], 
+                                            "dec": item["dec"], 
+                                            "pixscale": item["pixscale"], 
+                                            "ntimes": item["ntimes"], 
+                                            "nbands": item["nbands"]}
+                idx += 1
+
+
+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")

README.md

@@ -1,9 +1,36 @@
 ---
 license: cc-by-4.0
+pretty_name: SDSS 4d data cubes
 tags:
 - astronomy
 - compression
 - images
-pretty_name: SDSS 4d data cubes
+dataset_info:
+  config_name: tiny
+  features:
+  - name: image
+    sequence:
+      sequence:
+        sequence:
+          sequence: uint16
+  - name: ra
+    dtype: float64
+  - name: dec
+    dtype: float64
+  - name: pixscale
+    dtype: float64
+  - name: ntimes
+    dtype: int64
+  - name: nbands
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 558194176
+    num_examples: 2
+  - name: test
+    num_bytes: 352881364
+    num_examples: 1
+  download_size: 692
+  dataset_size: 911075540
 ---
 GBI-16-4D Dataset

splits/full_test.jsonl

@@ -0,0 +1,105 @@
+{"image_id": "cube_center_run4128_camcol6_f166_82-5-800-800", "image": "./data/cube_center_run4128_camcol6_f166_82-5-800-800.fits", "ra": 6.43794501, "dec": 1.15403631, "pixscale": 0.000109963839839238, "ntimes": 82, "nbands": 5}
+{"image_id": "cube_center_run3434_camcol1_f94_67-5-800-800", "image": "./data/cube_center_run3434_camcol1_f94_67-5-800-800.fits", "ra": 321.26536628, "dec": -1.15549284, "pixscale": 0.000109943915220771, "ntimes": 67, "nbands": 5}
+{"image_id": "cube_center_run5823_camcol1_f213_66-5-800-800", "image": "./data/cube_center_run5823_camcol1_f213_66-5-800-800.fits", "ra": 331.18075629, "dec": -0.94539302, "pixscale": 0.000109971661072827, "ntimes": 66, "nbands": 5}
+{"image_id": "cube_center_run3437_camcol6_f214_70-5-800-800", "image": "./data/cube_center_run3437_camcol6_f214_70-5-800-800.fits", "ra": 340.65681324, "dec": 1.15301095, "pixscale": 0.00010999502906958, "ntimes": 70, "nbands": 5}
+{"image_id": "cube_center_run5754_camcol5_f712_2-5-800-800", "image": "./data/cube_center_run5754_camcol5_f712_2-5-800-800.fits", "ra": 48.29634962, "dec": 0.5231281, "pixscale": 0.000110003069344113, "ntimes": 2, "nbands": 5}
+{"image_id": "cube_center_run5800_camcol3_f594_14-5-800-800", "image": "./data/cube_center_run5800_camcol3_f594_14-5-800-800.fits", "ra": 28.82331428, "dec": -0.10597264, "pixscale": 0.000110014603491111, "ntimes": 14, "nbands": 5}
+{"image_id": "cube_center_run3360_camcol5_f420_2-5-800-800", "image": "./data/cube_center_run3360_camcol5_f420_2-5-800-800.fits", "ra": 8.82325207, "dec": 0.52369036, "pixscale": 0.000110022084225373, "ntimes": 2, "nbands": 5}
+{"image_id": "cube_center_run5781_camcol2_f370_76-5-800-800", "image": "./data/cube_center_run5781_camcol2_f370_76-5-800-800.fits", "ra": 358.57743806, "dec": -0.52555038, "pixscale": 0.00011001864440601, "ntimes": 76, "nbands": 5}
+{"image_id": "cube_center_run3438_camcol4_f127_81-5-800-800", "image": "./data/cube_center_run3438_camcol4_f127_81-5-800-800.fits", "ra": 49.04622027, "dec": 0.10404842, "pixscale": 0.000110007237963072, "ntimes": 81, "nbands": 5}
+{"image_id": "cube_center_run4128_camcol4_f455_55-5-800-800", "image": "./data/cube_center_run4128_camcol4_f455_55-5-800-800.fits", "ra": 49.70542726, "dec": 0.31410984, "pixscale": 0.000109980712831407, "ntimes": 55, "nbands": 5}
+{"image_id": "cube_center_run4128_camcol1_f383_87-5-800-800", "image": "./data/cube_center_run4128_camcol1_f383_87-5-800-800.fits", "ra": 38.92614585, "dec": -0.94495368, "pixscale": 0.000109990153654142, "ntimes": 87, "nbands": 5}
+{"image_id": "cube_center_run1752_camcol1_f360_2-5-800-800", "image": "./data/cube_center_run1752_camcol1_f360_2-5-800-800.fits", "ra": 74.74870406, "dec": -0.94386558, "pixscale": 0.000110013907986703, "ntimes": 2, "nbands": 5}
+{"image_id": "cube_center_run4253_camcol6_f61_78-5-800-800", "image": "./data/cube_center_run4253_camcol6_f61_78-5-800-800.fits", "ra": 352.95477395, "dec": 1.15358716, "pixscale": 0.000109957608467609, "ntimes": 78, "nbands": 5}
+{"image_id": "cube_center_run5603_camcol4_f305_69-5-800-800", "image": "./data/cube_center_run5603_camcol4_f305_69-5-800-800.fits", "ra": 338.5747629, "dec": 0.3128843, "pixscale": 0.000109981142363597, "ntimes": 69, "nbands": 5}
+{"image_id": "cube_center_run3465_camcol3_f167_50-5-800-800", "image": "./data/cube_center_run3465_camcol3_f167_50-5-800-800.fits", "ra": 350.23429417, "dec": -0.31572122, "pixscale": 0.000109979929713267, "ntimes": 50, "nbands": 5}
+{"image_id": "cube_center_run5853_camcol4_f572_1-5-800-800", "image": "./data/cube_center_run5853_camcol4_f572_1-5-800-800.fits", "ra": 21.59348881, "dec": 0.1037275, "pixscale": 0.000109999080491509, "ntimes": 1, "nbands": 5}
+{"image_id": "cube_center_run5744_camcol2_f66_19-5-800-800", "image": "./data/cube_center_run5744_camcol2_f66_19-5-800-800.fits", "ra": 338.63724219, "dec": -0.52523829, "pixscale": 0.000109984652673999, "ntimes": 19, "nbands": 5}
+{"image_id": "cube_center_run2873_camcol6_f116_73-5-800-800", "image": "./data/cube_center_run2873_camcol6_f116_73-5-800-800.fits", "ra": 30.9762162, "dec": 1.15308714, "pixscale": 0.000109959300834713, "ntimes": 73, "nbands": 5}
+{"image_id": "cube_center_run4203_camcol6_f379_69-5-800-800", "image": "./data/cube_center_run4203_camcol6_f379_69-5-800-800.fits", "ra": 356.13411982, "dec": 0.94254374, "pixscale": 0.000109982697281122, "ntimes": 69, "nbands": 5}
+{"image_id": "cube_center_run4207_camcol1_f16_55-5-800-800", "image": "./data/cube_center_run4207_camcol1_f16_55-5-800-800.fits", "ra": 306.79757367, "dec": -0.94504091, "pixscale": 0.000109979581093385, "ntimes": 55, "nbands": 5}
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+{"image_id": "cube_center_run5823_camcol6_f428_76-5-800-800", "image": "./data/cube_center_run5823_camcol6_f428_76-5-800-800.fits", "ra": 3.36636487, "dec": 1.15280914, "pixscale": 0.000109985677202215, "ntimes": 76, "nbands": 5}
+{"image_id": "cube_center_run4192_camcol3_f303_65-5-800-800", "image": "./data/cube_center_run4192_camcol3_f303_65-5-800-800.fits", "ra": 352.09804338, "dec": -0.31629877, "pixscale": 0.000110003485067679, "ntimes": 65, "nbands": 5}
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+{"image_id": "cube_center_run3434_camcol2_f243_66-5-800-800", "image": "./data/cube_center_run3434_camcol2_f243_66-5-800-800.fits", "ra": 343.56997808, "dec": -0.73581212, "pixscale": 0.000110000538628022, "ntimes": 66, "nbands": 5}
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splits/tiny_test.jsonl

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splits/tiny_train.jsonl

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