Add dataset

.gitattributes

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 # 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: Raw space-based images from the Hubble Space Telescope 
+tags:
+- astronomy
+- compression
+- images
+---
+
+# SBI-16-2D Dataset
+
+SBI-16-2D is a dataset which is part of the AstroCompress project. It contains imaging data assembled from the Hubble Space Telescope (HST). <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 2D 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-16-2D
+```
+
+To pull all data files:
+```
+git lfs pull
+```
+
+Then `cd SBI-16-3D` and start python like:
+
+```python
+from datasets import load_dataset
+dataset = load_dataset("./SBI-16-2D.py", "tiny", data_dir="./data/", writer_batch_size=1, trust_remote_code=True)
+ds = dataset.with_format("np")
+```
+
+Now you should be able to use the `ds` variable like:
+
+```python
+ds["test"][0]["image"].shape # -> (TBD)
+```
+
+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
+dataset = load_dataset("AstroCompress/SBI-16-2D", "tiny", writer_batch_size=1, trust_remote_code=True)
+ds = dataset.with_format("np")
+```
+
+
+## Demo Colab Notebook
+We provide a demo collab notebook to get started on using the dataset [here](https://colab.research.google.com/drive/1SuFBPZiYZg9LH4pqypc_v8Sp99lShJqZ?usp=sharing).
+
+## 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-16-2D.py

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+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 = """
+SBI-16-2D is a dataset which is part of the AstroCompress project. 
+It contains imaging data assembled from the Hubble Space Telescope (HST).
+"""
+
+_HOMEPAGE = "https://google.github.io/AstroCompress"
+
+_LICENSE = "CC BY 4.0"
+
+_URL = "https://huggingface.co/datasets/AstroCompress/SBI-16-2D/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-16-2D"
+
+
+class SBI_16_2D(datasets.GeneratorBasedBuilder):
+    """SBI-16-2D Dataset"""
+
+    VERSION = datasets.Version("1.0.4")
+
+    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.Image(decode=True, mode="I;16"),
+                    "ra": datasets.Value("float64"),
+                    "dec": datasets.Value("float64"),
+                    "pixscale": datasets.Value("float64"),
+                    "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"],
+                            "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 SBI-16-2D examples"""
+
+        for idx, (filepath, item) in enumerate(zip(filepaths, data_metadata)):
+            with fits.open(filepath, memmap=False) as hdul:
+                # Process image data from HDU index 1
+                image_data_1 = hdul[1].data[:, :].tolist()
+                task_instance_key_1 = f"{self.config.name}-{split}-{idx}-HDU1"
+                yield task_instance_key_1, {**{"image": image_data_1}, **item}
+    
+                # Process image data from HDU index 4
+                image_data_4 = hdul[4].data[:, :].tolist()
+                task_instance_key_4 = f"{self.config.name}-{split}-{idx}-HDU4"
+                yield task_instance_key_4, {**{"image": image_data_4}, **item}