README.md

metadata

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: full
    features:
      - name: image
        dtype:
          image:
            mode: I;16
      - name: telescope
        dtype: string
      - name: image_id
        dtype: string
    splits:
      - name: train
        num_bytes: 3509045373
        num_examples: 120
      - name: test
        num_bytes: 970120060
        num_examples: 32
    download_size: 2240199274
    dataset_size: 4479165433
  - 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: 307620695
        num_examples: 10
      - name: test
        num_bytes: 168984698
        num_examples: 5
    download_size: 238361934
    dataset_size: 476605393

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). 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:

pip install datasets astropy PIL

There are two datasets: tiny and full, each with train and test splits. The tiny dataset has 5 2D 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:

huggingface-cli login

or

import huggingface_hub
huggingface_hub.login(token=token)

Then in your python script:

from datasets import load_dataset
dataset = load_dataset("AstroCompress/GBI-16-2D-Legacy", "tiny", \ 
                       trust_remote_code=True)
ds = dataset.with_format("np")

Local Use

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

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

Then cd GBI-16-2D-Legacy and start python like:

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

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

ds["test"][0]["image"].shape # -> (4200, 2154)

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. If you run into issues with downloading the full dataset, try changing num_proc in load_dataset to >1 (e.g. 5). You can also set the writer_batch_size to ~10-20.