bounded-rand-walkers
Author(s)
Kuhn-Regnier, Alexander
Cocconi, Luca
Neuss, Malte
Type
Software / Code
Abstract
This is the first release of the software used in our paper investigating bounded random walks.
We implemented an adaptive rejection sampling algorithm in both Python and C++, allowing the investigation of bounded random walks given user-specified intrinsic step distributions and convex geometries. Multiple binning techniques are used throughout in order to enable analysis of both 2D gridded and 1D radially-averaged data, and a custom integrator is used to achieve high numerical accuracy where needed.
We implemented an adaptive rejection sampling algorithm in both Python and C++, allowing the investigation of bounded random walks given user-specified intrinsic step distributions and convex geometries. Multiple binning techniques are used throughout in order to enable analysis of both 2D gridded and 1D radially-averaged data, and a custom integrator is used to achieve high numerical accuracy where needed.
Version
1.0.0
Date Issued
2020-10-22
Citation
2020
Copyright Statement
https://opensource.org/licenses/MIT
Subjects
bounded-rand-walkers