Add dataset

.gitattributes

@@ -56,3 +56,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.fits filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-4.0
+pretty_name: Space-based (JWST) 3d data cubes
+tags:
+- astronomy
+- compression
+- images
+dataset_info:
+  config_name: tiny
+  features:
+  - name: image
+    dtype:
+      array3_d:
+        shape:
+        - 2048
+        - 2048
+        dtype: uint8
+  - name: ra
+    dtype: float64
+  - name: dec
+    dtype: float64
+  - name: pixscale
+    dtype: float64
+  - name: ntimes
+    dtype: int64
+  - name: image_id
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 100761802
+    num_examples: 2
+  - name: test
+    num_bytes: 75571313
+    num_examples: 1
+  download_size: 201496920
+  dataset_size: 176333115
+---
+
+# SBI-16-3D Dataset
+
+SBI-16-3D is a dataset which is part of the AstroCompress project. It contains data assembled from the James Webb Space Telescope (JWST). <TODO>Describe data format</TODO>
+
+# Usage
+
+You first need to install the `datasets` and `astropy` packages:
+
+```bash
+pip install datasets astropy
+```
+
+There are two datasets: `tiny` and `full`, each with `train` and `test` splits. The `tiny` dataset has 2 4D images in the `train` and 1 in the `test`. The `full` dataset contains all the images in the `data/` directory.
+
+
+## Local Use (RECOMMENDED)
+
+You can clone this repo and use directly without connecting to hf:
+
+```bash
+git clone https://huggingface.co/datasets/AstroCompress/SBI-16-3D
+```
+
+```bash
+git lfs pull
+```
+
+Then `cd SBI-16-3D` and start python like:
+
+```python
+from datasets import load_dataset
+import numpy
+dataset = load_dataset("./SBI-16-3D.py", "tiny", data_dir="./data/", writer_batch_size=1, trust_remote_code=True)
+ds = dataset.with_format("np", dtype=numpy.uint16)
+```
+
+Now you should be able to use the `ds` variable like:
+
+```python
+ds["test"][0]["image"].shape # -> (5, 2048, 2048)
+```
+
+Note of course that it will take a long time to download and convert the images in the local cache for the `full` dataset. Afterward, the usage should be quick as the files are memory-mapped from disk.
+
+
+
+## Use from Huggingface Directly
+
+This method may only be an option when trying to access the "tiny" version of the dataset.
+
+To directly use from this data from Huggingface, you'll want to log in on the command line before starting python:
+
+```bash
+huggingface-cli login
+```
+
+or
+
+```
+import huggingface_hub
+huggingface_hub.login(token=token)
+```
+
+Then in your python script:
+
+```python
+from datasets import load_dataset
+import numpy
+dataset = load_dataset("AstroCompress/SBI-16-3D", "tiny", writer_batch_size=1, trust_remote_code=True)
+ds = dataset.with_format("np", columns=["image"], dtype=numpy.uint16)
+
+# or torch
+import torch
+dst = dataset.with_format("torch", columns=["image"], dtype=torch.uint16)
+
+# or pandas
+dsp = dataset.with_format("pandas", columns=["image"], dtype=numpy.uint16)
+```
+
+## Utils scripts
+Note that utils scripts such as `eval_baselines.py` must be run from the parent directory of `utils`, i.e. `python utils/eval_baselines.py`.

SBI-16-3D.py

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+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
+
+_DESCRIPTION = (
+    """SBI-16-3D is a dataset which is part of the AstroCompress project. """
+    """It contains data assembled from the James Webb Space Telescope (JWST). """
+    """<TODO>Describe data format</TODO>"""
+)
+
+_HOMEPAGE = "https://google.github.io/AstroCompress"
+
+_LICENSE = "CC BY 4.0"
+
+_URL = "https://huggingface.co/datasets/AstroCompress/SBI-16-3D/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/SBI-16-3D"
+
+
+class SBI_16_3D(datasets.GeneratorBasedBuilder):
+    """SBI-16-3D Dataset"""
+
+    VERSION = datasets.Version("1.0.3")
+
+    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.Array3D(shape=(None, 2048, 2048), dtype="uint16"),
+                    "ra": datasets.Value("float64"),
+                    "dec": datasets.Value("float64"),
+                    "pixscale": datasets.Value("float64"),
+                    "ntimes": 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"],
+                            "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:
+                # 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")

baseline_results_2d.csv

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