---
license: cc-by-4.0
pretty_name: Ground-based 2d images assembled in Maireles-González et al.
tags:
- astronomy
- compression
- images
dataset_info:
  config_name: tiny
  features:
  - name: image
    dtype:
      image:
        mode: I;16
  - name: telescope
    dtype: string
  - name: image_id
    dtype: string
  splits:
  - name: train
    num_bytes: 307620692
    num_examples: 10
  - name: test
    num_bytes: 168984694
    num_examples: 5
  download_size: 238361934
  dataset_size: 476605386
---

# GBI-16-2D-Legacy Dataset

GBI-16-2D-Legacy is a Huggingface `dataset` wrapper around a compression dataset assembled by Maireles-González et al. (Publications of the Astronomical Society of the Pacific, 135:094502, 2023 September; doi: [https://doi.org/10.1088/1538-3873/acf6e0](https://doi.org/10.1088/1538-3873/acf6e0)). It contains 226 FITS images from 5 different ground-based telescope/cameras with a varying amount of entropy per image.

# 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.

## Use from Huggingface Directly

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/GBI-16-2D-Legacy", "tiny")
ds = dataset.with_format("np")
```

## Local Use

Alternatively, you can clone this repo and use directly without connecting to hf:

```bash
git clone https://huggingface.co/datasets/AstroCompress/GBI-16-2D-Legacy
```

Then `cd SBI-16-3D` and start python like:

```python
from datasets import load_dataset
dataset = load_dataset("./GBI-16-2D-Legacy", "tiny", data_dir="./data/")
ds = dataset.with_format("np")
```

Now you should be able to use the `ds` variable like:

```python
ds["test"][0]["image"].shape # -> (9, 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.