Metadata-Version: 2.1
Name: vitamin-b
Version: 0.2.7
Summary: A user-friendly machine learning Bayesian inference library
Home-page: https://github.com/hagabbar/vitamin_b
Author: Hunter Gabbard, Chris Messenger, Ik Siong Heng, Francesco Tonolini, Roderick Murray-Smith
Author-email: h.gabbard.1@research.gla.ac.uk
License: GNU General Public License v3 (GPLv3)
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        # [VItamin_B: A Machine Learning Library for Fast Gravitational Wave Posterior Generation](https://arxiv.org/abs/1909.06296)
        :star: Star us on GitHub  it helps!
        
        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
        ```
        
        Optionally, install `basemap` and `geos` in order to produce sky plots of results.
        
        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`
        
        Install VItamin using pip:
        ```
        pip install vitamin-b
        ```
        
        ## Training
        
        To train an example model from the paper, try out the [demo](https://colab.research.google.com/github/hagabbar/OzGrav_demo/blob/master/OzGrav_VItamin_demo.ipynb).
        
        Full model definitions are given in `models` directory. Data is generated from `gen_benchmark_pe.py`.
        
        ## Results
        
        We train using a network derived from first principals:
        ![](images/network_setup.png)
        
        We track the performance of the model during training via loss curves:
        ![](images/inv_losses_log.png)
        
        Finally, we produce posteriors after training and other diagnostic tests comparing our approach with 4 other independent methods:
        
        Posterior example:
        ![](images/corner_testcase0.png)
        
        KL-Divergence between posteriors:
        ![](images/hist-kl.png)
        
        PP Tests:
        ![](images/latest_pp_plot.png)
        
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=3.5
Description-Content-Type: text/markdown
