metadata

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). Describe data format

Usage

You first need to install the datasets and astropy packages:

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:

git clone https://huggingface.co/datasets/AstroCompress/SBI-16-3D

git lfs pull

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

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:

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:

huggingface-cli login

import huggingface_hub
huggingface_hub.login(token=token)

Then in your python script:

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.