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https://github.com/johannesbuchner/lightrayrider

Ray tracing of hydrodynamic simulations to compute column densities
https://github.com/johannesbuchner/lightrayrider

astrophysics c intersection monte-carlo parallel-computing python raytracing

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Ray tracing of hydrodynamic simulations to compute column densities

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README 

        
LightRayRider

========================================================

 

A fast library for calculating intersections of a line with many spheres or inhomogeneous material.



Introduction

-------------



.. image:: https://johannesbuchner.github.io/LightRayRider/_static/logo.png



This small library computes line integrals through various three-dimensional geometric bodies.

For example, millions of rays can be sent through millions of spheres of various sizes and densities.

Three-dimensional uniform grids representing arbitrary density fields are also supported,

as well as voronoi tesselation of points.



The library was developed for X-ray ray tracing with `XARS `.

A point source can be obscured by a gas distribution along the line-of-sight.

For hydrodynamic simulations which produce such a gas distribution, this code can compute

the total density along a arbitrary ray. The output is a column density, 

also known as "N_H" if hydrogen gas is irradiated.



.. image:: https://img.shields.io/pypi/v/lightrayrider.svg

        :target: https://pypi.python.org/pypi/lightrayrider

.. image:: https://img.shields.io/badge/docs-published-ok.svg

        :target: https://johannesbuchner.github.io/LightRayRider/

        :alt: Documentation Status

.. image:: https://github.com/JohannesBuchner/LightRayRider/actions/workflows/tests.yml/badge.svg

	:target: https://github.com/JohannesBuchner/LightRayRider/actions

.. image:: https://coveralls.io/repos/github/JohannesBuchner/LightRayRider/badge.svg?branch=master

	:target: https://coveralls.io/github/JohannesBuchner/LightRayRider?branch=master



Line/Sphere cutting

--------------------



Input:



* Points in space representing sphere centres.

* Sphere radii and densities.

* One or more arbitrary lines from the origin.



Output:



* This computes the total length / column density cut.

* From distance 0 or another chosen minimal distance (or multiple).



Method:



* Simple quadratic equations.



Voronoi cutting

----------------------



Input:



* Points in space. 

* Densities.

* One or more arbitrary lines from the origin



Output:



* This computes the total length along the line,

  where every point on the line is assigned the density from the 

  nearest point (Voronoi segmentation).

* From distance 0 or another chosen minimal distance (or multiple).



Method:



* Segmentation of the line where points become equi-distant. 

  Performs approximately linearly with number of points.



Grid cutting

----------------------



Input:



* 3D Grid with densities at each location

* One or more arbitrary lines from a point



Output:



* This computes the total length along the line,

  where every point on the line is assigned the density from the 

  grid cell it passes through.

* From distance 0 or another chosen minimal distance (or multiple).



Method:



* Finding intersections of the cell borders (planes) with the lines, and

  checking which cell to consider next.



Usage

--------------



To use from Python, use raytrace.py::

	

	from lightrayrider import *



You can find the declaration of how to call these functions on the 

**`API documentation page `**.



Essentially, pass the coordinates of your objects, the associated

densities and the starting point and direction of your raytracing.



Example usage is demonstrated in irradiate.py. This was used for Illustris and 

EAGLE particle in the associated paper. 

There are additional unit test examples in test/.



Parallel processing

-----------------------



LightRayRider supports multiple processors through OpenMP.

Set the variable OMP_NUM_THREADS to the number of processors you want to use,

and the parallel library ray-parallel.so will be loaded.



License and Acknowledgements

--------------------------------



If you use this code, please cite "Galaxy gas as obscurer: II. Separating the galaxy-scale and

nuclear obscurers of Active Galactic Nuclei", by Buchner & Bauer (2017), https://arxiv.org/abs/1610.09380



Bibcode::



	@ARTICLE{2017MNRAS.465.4348B,

	   author = {{Buchner}, J. and {Bauer}, F.~E.},

	    title = "{Galaxy gas as obscurer - II. Separating the galaxy-scale and nuclear obscurers of active galactic nuclei}",

	  journal = {\mnras},

	archivePrefix = "arXiv",

	   eprint = {1610.09380},

	 primaryClass = "astro-ph.HE",

	 keywords = {dust, extinction, ISM: general, galaxies: active, galaxies: general, galaxies: ISM, X-rays: ISM},

	     year = 2017,

	    month = mar,

	   volume = 465,

	    pages = {4348-4362},

	      doi = {10.1093/mnras/stw2955},

	   adsurl = {http://adsabs.harvard.edu/abs/2017MNRAS.465.4348B},

	  adsnote = {Provided by the SAO/NASA Astrophysics Data System}

	}



The code is licensed under AGPLv3 (see LICENSE file).