Skip to content

swagnercarena/paltax

Repository files navigation

logo paltax

ci coverage license

paltax is a package for conducting simulation-based inference on strong gravitational lensing images.

Installation

paltax is installable via pip:

$ pip install paltax

For the most up-to-date version of paltax install directly from the git repository.

$ git clone https://github.com/swagnercarena/paltax.git
    $ cd path/to/paltax/
    $ pip install -e .

Usage

The main functionality of paltax is to train (sequential) neural posterior estimators with on-the-fly data generation. To train a model with paltax you need a training configuration file that is passed to main.py:

$ python main.py --workdir=path/to/model/output/folder --config=path/to/training/configuration

paltax comes preloaded with a number of training configuration files which are described in paltax/TrainConfigs/README.rst. These training configuration files require input configuration files, examples of which can be found in paltax/InputConfigs/.

Demos

paltax comes with a tutorial notebook for users interested in using the package.

Figures

Code for generating the plots included in some of the publications using paltax can be found under the corresponding arxiv number in the paltax/notebooks/papers/ folder.

Attribution

If you use paltax for your own research, please cite the paltax package (Wagner-Carena et al. 2024)

paltax builds off of the publically released Google DeepMind codebase jaxstronomy.