• on a standard system:

git clone https://github.com/ngbusca/QuasarNET.git
cd QuasarNET
pip install -r requirements.txt --user
python setup.py install --user

• at NERSC (specially if you wish to run this notebook at jupyter.nersc.gov)

conda create -n qnet python=3 qnet scipy numpy fitsio h5py ipykernel
source activate qnet
python -m ipykernel install --user --name qnet --display-name qnet
pip install tensorflow
pip install keras>=2.2.4
git clone https://github.com/ngbusca/QuasarNET.git
cd QuasarNET
python setup.py install

These data are a reprocessing of data release 12 (DR12) of the Sloan Digital Sky Survey (https://www.sdss.org/dr12/)

They are available on Kaggle: https://www.kaggle.com/ngbusca/qnet_data

A practical way to download the data is to use the kaggle-api, which will allow you to do it from the command line. Otherwise you can simply click the download link on the website.

Download the data to the QuasarNET/data/ directory, unzip the file and set read/write permissions (skip the kaggle datasets... line if you've downloaded the data through the website).

cd data
kaggle datasets download ngbusca/qnet_data
unzip qnet_data.zip
chmod 600 *

The pre-trained weights are available at: https://www.kaggle.com/ngbusca/qnet_trained_models

Download the weights to the QuasarNET/weights/ directory, unzip the file and set read/write permissions (skip the kaggle datasets... line if you've downloaded the data through the website).

cd weights
kaggle datasets download ngbusca/qnet_trained_models
unzip qnet_trained_models.zip
chmod 600 *