This package contains functionality to
- postprocess the coarse convergence values of an existing simulation to introduce finer fluctuations at galaxy-galaxy lensing scales
- train a Bayesian graph neural network to infer convergence given photometric measurements of galaxies around a line of sight
- hierarchically infer the mean and standard deviation of convergence in the population
- Virtual environments are strongly recommended, to prevent dependencies with conflicting versions. Create a conda virtual environment and activate it:
$conda create -n n2j python=3.8 -y $conda activate n2j
- Clone the repo and install.
$git clone https://github.com/jiwoncpark/node-to-joy.git $cd node-to-joy $pip install -e . -r requirements.txt
- (Optional) To run the notebooks, add the Jupyter kernel.
$python -m ipykernel install --user --name n2j --display-name "Python (n2j)"