Low-Latency Algorithm for Multi-messenger Astrophysics (LLAMA)'s Documentation
==============================================================================

.. only:: html

   +--------------+-------------+-------------+
   | Docker Base  | Docker Env  | Docker Dev  |
   +==============+=============+=============+
   | |dockerbase| | |dockerenv| | |dockerdev| |
   +--------------+-------------+-------------+
   
   .. |dockerbase| image:: https://img.shields.io/travis/stefco/llama-base?style=flat-square
   .. |dockerenv| image:: https://img.shields.io/travis/stefco/llama-env?style=flat-square
   .. |dockerdev| image:: https://img.shields.io/travis/stefco/llama-dev?style=flat-square

LLAMA is a reliable and flexible multi-messenger astrophysics framework and
search pipeline. It identifies astrophysical signals by combining observations
from multiple types of astrophysical messengers and can either be run in online
mode as a self-contained low-latency pipeline or in offline mode for post-hoc
analyses. It was first used during Advanced LIGO's second observing run (O2)
for the joint online LIGO/Virgo/IceCube Gravitational Wave/High-Energy Neutrino
(GWHEN) search.

LLAMA's search alorithm has been upgraded to run during LIGO's third observing
run (O3).

.. only:: html

   .. figure:: _static/bayesian-calculation-diagram.png
      :figwidth: 80 %
      :align: center

      Flowchart describing the significance calculation used in the O3 version
      of the pipeline.

.. only:: latex

   .. figure:: _static/bayesian-calculation-diagram.pdf
      :figwidth: 70 %
      :align: center

      Flowchart describing the significance calculation used in the O3 version
      of the pipeline.

.. only:: html

   This full website can also be downloaded in 
   :download:`printer-friendly PDF format
   <../build/latex/llama.pdf>`.

*The LLAMA team thanks the many researchers working in LIGO/Virgo, IceCube, and
other astrophysics projects that enable this pipeline to operate successfully.
In particular, they thank Scott Barthelmy and Leo Singer of NASA for providing
helpful code and advice for working with GCN.*

*The authors are grateful to the IceCube Collaboration for providing neutrino
datasets and support for testing this algorithm. The Columbia
Experimental Gravity group is grateful for the generous support of Columbia
University in the City of New York and the National Science Foundation under
grants PHY-1404462 and PHY-1708028. The authors are thankful for the generous
support of the University of Florida and Columbia University in the City of New
York.*

.. toctree::
   :maxdepth: 5
   :caption: Info for Reviewers

   reviewers

.. toctree::
   :maxdepth: 5
   :caption: Quick Start Guide

   readme.rst

.. toctree::
   :maxdepth: 5
   :caption: Operators' Guide

   operators

.. toctree::
   :maxdepth: 5
   :caption: Developer's Guide

   developers

.. toctree::
   :maxdepth: 3
   :caption: Bibliography

   papers

.. toctree::
   :maxdepth: 6
   :caption: API Documentation

   llama

.. include:: llama-subcomponents.rst

.. toctree::
   :maxdepth: 8
   :caption: Code Reports

   Unit Tests <https://multimessenger.science/pytest/>
   Code Coverage  <https://multimessenger.science/coverage/>
   profiling
   reports

.. _LLAMA-Command-Line-Interface:

.. toctree::
   :maxdepth: 6
   :caption: Command Line Interface

   cli.llama.__main__

.. include:: cli.llama.rst


Indices and tables
------------------

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`