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--- |
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license: cc-by-4.0 |
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pretty_name: Space-based (JWST) 3d data cubes |
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tags: |
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- astronomy |
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- compression |
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- images |
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dataset_info: |
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config_name: tiny |
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features: |
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- name: image |
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dtype: |
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array3_d: |
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shape: |
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- 2048 |
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- 2048 |
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dtype: uint8 |
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- name: ra |
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dtype: float64 |
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- name: dec |
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dtype: float64 |
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- name: pixscale |
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dtype: float64 |
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- name: ntimes |
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dtype: int64 |
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- name: image_id |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 100761802 |
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num_examples: 2 |
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- name: test |
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num_bytes: 75571313 |
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num_examples: 1 |
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download_size: 201496920 |
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dataset_size: 176333115 |
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--- |
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# SBI-16-3D Dataset |
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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> |
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# Usage |
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You first need to install the `datasets` and `astropy` packages: |
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```bash |
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pip install datasets astropy |
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``` |
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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. |
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## Local Use (RECOMMENDED) |
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Alternatively, you can clone this repo and use directly without connecting to hf: |
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```bash |
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git clone https://huggingface.co/datasets/AstroCompress/SBI-16-3D |
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``` |
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```bash |
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git lfs pull |
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``` |
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Then `cd SBI-16-3D` and start python like: |
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```python |
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from datasets import load_dataset |
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dataset = load_dataset("./SBI-16-3D.py", "tiny", data_dir="./data/") |
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ds = dataset.with_format("np") |
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``` |
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Now you should be able to use the `ds` variable like: |
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```python |
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ds["test"][0]["image"].shape # -> (5, 2048, 2048) |
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``` |
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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. |
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## Use from Huggingface Directly |
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To directly use from this data from Huggingface, you'll want to log in on the command line before starting python: |
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```bash |
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huggingface-cli login |
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``` |
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or |
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``` |
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import huggingface_hub |
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huggingface_hub.login(token=token) |
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``` |
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Then in your python script: |
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```python |
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from datasets import load_dataset |
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import numpy |
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dataset = load_dataset("AstroCompress/SBI-16-3D", "tiny") |
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ds = dataset.with_format("np", columns=["image"], dtype=numpy.uint8) |
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# or torch |
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import torch |
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dst = dataset.with_format("torch", columns=["image"], dtype=torch.uint8) |
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# or pandas |
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dsp = dataset.with_format("pandas", columns=["image"], dtype=numpy.uint8) |
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``` |
