vitamin-b 0.2.2

A user-friendly machine learning Bayesian inference library

# [VItamin_B: A Machine Learning Library for Fast Gravitational Wave Posterior Generation](https://arxiv.org/abs/1909.06296)

Welcome to VItamin_B, a python toolkit for producing fast gravitational wave posterior samples.

This [repository](https://github.com/hagabbar/vitamin_b) is the official implementation of [Bayesian Parameter Estimation using Conditional Variational Autoencoders for Gravitational Wave Astronomy](https://arxiv.org/abs/1909.06296).

Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonlini, Roderick Murray-Smith

Official Documentation can be found at [https://hagabbar.github.io/vitamin_b](https://hagabbar.github.io/vitamin_b).

Check out our Blog (to be made), [Paper](https://arxiv.org/abs/1909.06296) and [Interactive Demo](https://colab.research.google.com/github/hagabbar/OzGrav_demo/blob/master/OzGrav_VItamin_demo.ipynb).

Note: This repository is a work in progress. No official release of code just yet.

## Requirements

VItamin requires python3.6. You may use python3.6 by initializing a virtual environment.

` virtualenv -p python3.6 myenv source myenv/bin/activate pip install --upgrade pip `

Make sure to install basemap prior to installing all other packages.

For installing basemap: - Install geos-3.3.3 from source - Once geos is installed, install basemap using pip install git+https://github.com/matplotlib/basemap.git

For other required packages: ` pip install -r requirements.txt `

Install VItamin using pip: ` pip install vitamin-b `