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---
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)
You can clone this repo and use directly without connecting to hf:
```bash
git clone https://huggingface.co/datasets/AnonAstroData/SBI-16-3D
```
```bash
git lfs pull
```
Then `cd SBI-16-3D` and start python like:
```python
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:
```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
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
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)
```
## Demo Colab Notebook
We provide a demo collab notebook to get started on using the dataset [here](https://colab.research.google.com/drive/1wcz7qMqSAMST2kXFlL-TbwpYR26gYIYy?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`.
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