---
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)

Alternatively, 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
dataset = load_dataset("./SBI-16-3D.py", "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 # -> (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

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")
ds = dataset.with_format("np", columns=["image"], dtype=numpy.uint8)

# or torch
import torch
dst = dataset.with_format("torch", columns=["image"], dtype=torch.uint8)

# or pandas
dsp = dataset.with_format("pandas", columns=["image"], dtype=numpy.uint8)
```