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A photometric redshift monstrosity

Project description

#### A photometric redshift monstrosity.

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, and - PDF-oriented plotting utilities.

Paper forthcoming.

### Documentation Currently nonexistent. See the demos for examples.

### Installation frankenz can be installed via ` pip install frankenz ` Alternately, it can also be installed by running ` python setup.py install ` from inside the repository.

### Demos Several Jupyter notebooks that demonstrate most of the available features can be found [here](https://github.com/joshspeagle/frankenz/tree/master/demos).

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