Note: The Legacy Survey of Space and Time Dark Energy Science Collaboration (LSST-DESC) is responsible for ongoing development of qp in a separate repository. This repository contains the code used in Malz, et. al. 2018. AJ. 156 1 35. for the sake of reproducibility (and if you want to reconstruct PDFs from the quantile parameterization, which is not yet implemented in the LSST-DESC version).

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qp

Quantile parametrization for probability distribution functions.

Motivation

This repository exists for two reasons.

1. To be the home of qp, a python package for handling probability distributions using various parametrizations, including a set of quantiles;
2. To help us learn best practices in software carpentry.

Examples

You can run the qp IPython notebooks live over the web at Binder:

To make sure you are running the latest version, you can rebuild the Binder qp installation here.

You can also browse the (un-executed) notebooks here in this repo:

• Basic Demo (raw notebook)
• KL Divergence Illustration (raw notebook)

Also: Read the Docs

People

• Alex Malz (NYU)
• Phil Marshall (SLAC)

License, Contributing etc

The code in this repo is available for re-use under the MIT license, which means that you can do whatever you like with it, just don't blame us. If you end up using any of the code or ideas you find here in your academic research, please cite us as [Malz, et. al. 2018. AJ. 156 1 35.](https://doi.org/10.3847/1538-3881/aac6b5)\footnote{\texttt{https://github.com/aimalz/qp}}. If you are interested in this project, please do drop us a line via the hyperlinked contact names above, or by writing us an issue. To get started contributing to the qp project, just fork the repo - pull requests are always welcome!