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Volume 9, issue 2 | Copyright
Geosci. Model Dev., 9, 823-839, 2016
https://doi.org/10.5194/gmd-9-823-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model description paper 29 Feb 2016

Model description paper | 29 Feb 2016

CellLab-CTS 2015: continuous-time stochastic cellular automaton modeling using Landlab

Gregory E. Tucker1,2, Daniel E. J. Hobley1,2, Eric Hutton3, Nicole M. Gasparini4, Erkan Istanbulluoglu5, Jordan M. Adams4, and Sai Siddartha Nudurupati5 Gregory E. Tucker et al.
  • 1Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, USA
  • 2Department of Geological Sciences, University of Colorado, Boulder, USA
  • 3Community Surface Dynamics Modeling System (CSDMS), University of Colorado, Boulder, USA
  • 4Department of Earth and Environmental Sciences, Tulane University, New Orleans, USA
  • 5Department of Civil and Environmental Engineering, University of Washington, Seattle, USA

Abstract. CellLab-CTS 2015 is a Python-language software library for creating two-dimensional, continuous-time stochastic (CTS) cellular automaton models. The model domain consists of a set of grid nodes, with each node assigned an integer state code that represents its condition or composition. Adjacent pairs of nodes may undergo transitions to different states, according to a user-defined average transition rate. A model is created by writing a Python code that defines the possible states, the transitions, and the rates of those transitions. The code instantiates, initializes, and runs one of four object classes that represent different types of CTS models. CellLab-CTS provides the option of using either square or hexagonal grid cells. The software provides the ability to treat particular grid-node states as moving particles, and to track their position over time. Grid nodes may also be assigned user-defined properties, which the user can update after each transition through the use of a callback function. As a component of the Landlab modeling framework, CellLab-CTS models take advantage of a suite of Landlab's tools and capabilities, such as support for standardized input and output.

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This paper presents a new Python-language software library, called CellLab-CTS, that enables rapid creation of continuous-time stochastic (CTS) cellular automata models. These models are quite useful for simulating the behavior of natural systems, but can be time-consuming to program. CellLab-CTS allows users to set up models with a minimum of effort, and thereby focus on the science rather than the software.
This paper presents a new Python-language software library, called CellLab-CTS, that enables...
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