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---
license: cc-by-4.0
pretty_name: SDSS 4d data cubes
tags:
- astronomy
- compression
- images
dataset_info:
config_name: tiny
features:
- name: image
dtype:
array4_d:
shape:
- 5
- 800
- 800
dtype: uint16
- name: ra
dtype: float64
- name: dec
dtype: float64
- name: pixscale
dtype: float64
- name: ntimes
dtype: int64
- name: nbands
dtype: int64
splits:
- name: train
num_bytes: 558194176
num_examples: 2
- name: test
num_bytes: 352881364
num_examples: 1
download_size: 908845172
dataset_size: 911075540
---
# GBI-16-4D Dataset
GBI-16-4D is a dataset which is part of the AstroCompress project. It contains data assembled from the Sloan Digital SkySurvey (SDSS). Each FITS file contains a series of 800x800 pixel uint16 observations of the same portion of the Stripe82 field, taken in 5 bandpass filters (u, g, r, i, z) over time. The filenames give the
starting run, field, camcol of the observations, the number of filtered images per timestep, and the number of timesteps. For example:
```cube_center_run4203_camcol6_f44_35-5-800-800.fits```
contains 35 frames of 800x800 pixel images in 5 bandpasses starting with run 4203, field 44, and camcol 6. The images are stored in the FITS standard.
# 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/GBI-16-4D
```
```bash
git lfs pull
```
Then `cd GBI-16-4D` and start python like:
```python
from datasets import load_dataset
dataset = load_dataset("./GBI-16-4D.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 # -> (55, 5, 800, 800)
```
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
dataset = load_dataset("AstroCompress/GBI-16-4D", "tiny")
ds = dataset.with_format("np")
``` |