WARNING: This project is under active development and not yet stable.
frankenz is a Pure Python implementation of a variety of methods to quickly
yet robustly perform (hierarchical) Bayesian inference using large
(but discrete) sets of (possibly noisy) models with (noisy) photometric data.
The code also contains a number of additional utilities, including:
- a module for generating quick mocks (along with filter curves and SEDs),
- several manifold-learning algorithms,
- a flexible set of photometric likelihoods,
- fast kernel density estimation,
- PDF-oriented plotting/processing functions, and
- population/hierarchical inference methods.
Paper forthcoming (Speagle et al. in prep.).
Currently nonexistent. See the demos for examples.
frankenz can be installed by running
python setup.py install
from inside the repository.
Several Jupyter notebooks that demonstrate most of the available features can be found here.