Skip to main content

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).

Project details


Release history Release notifications

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
frankenz-0.1.5.tar.gz (5.4 MB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page