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|CSDMS meeting abstract presentation=Writing the software to implement a two-dimensional numerical model can be a daunting exercise, even when the underlying discretization and numerical schemes are relatively simple. The challenge is even greater when the desired model includes ``advanced'' features such as an unstructured grid, a staggered-grid numerical solver, or input/output operations on gridded data. Landlab is a Python-language programming library that makes the process of 2D model-building simpler and more efficient. Landlab's core features include: (1) a gridding engine that lets you create and configure a structured or unstructured grid in just a few lines of code, and to attach data directly to the grid; (2) a library of pre-built process components that saves you from having to re-invent the wheel with common geoscience algorithms (such as flow routing on gridded terrain, linear and nonlinear diffusion, and elastic plate flexure); (3) a mechanism for coupling components together to create integrated model; and (4) a suite of tools for input/output and other common operations. Although Landlab's components are primarily related to earth-surface dynamics (including geomorphology and hydrology), its basic framework is applicable to many types of geophysical system. This clinic provides a hands-on tutorial introduction to Landlab. Participants will learn about Landlab's capabilities, and how to use it to build and run simple 2D models. Familiarity with the Python language and the Numpy library is helpful but not critical. | |CSDMS meeting abstract presentation=Writing the software to implement a two-dimensional numerical model can be a daunting exercise, even when the underlying discretization and numerical schemes are relatively simple. The challenge is even greater when the desired model includes ``advanced'' features such as an unstructured grid, a staggered-grid numerical solver, or input/output operations on gridded data. Landlab is a Python-language programming library that makes the process of 2D model-building simpler and more efficient. Landlab's core features include: (1) a gridding engine that lets you create and configure a structured or unstructured grid in just a few lines of code, and to attach data directly to the grid; (2) a library of pre-built process components that saves you from having to re-invent the wheel with common geoscience algorithms (such as flow routing on gridded terrain, linear and nonlinear diffusion, and elastic plate flexure); (3) a mechanism for coupling components together to create integrated model; and (4) a suite of tools for input/output and other common operations. Although Landlab's components are primarily related to earth-surface dynamics (including geomorphology and hydrology), its basic framework is applicable to many types of geophysical system. This clinic provides a hands-on tutorial introduction to Landlab. Participants will learn about Landlab's capabilities, and how to use it to build and run simple 2D models. Familiarity with the Python language and the Numpy library is helpful but not critical. | ||
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Revision as of 16:01, 28 May 2025
CSDMS 2015 annual Meeting - Models meet data, data meet models
Landlab: A Python library for building, exploring, and coupling 2D surface-process models
Abstract
Writing the software to implement a two-dimensional numerical model can be a daunting exercise, even when the underlying discretization and numerical schemes are relatively simple. The challenge is even greater when the desired model includes ``advanced features such as an unstructured grid, a staggered-grid numerical solver, or input/output operations on gridded data. Landlab is a Python-language programming library that makes the process of 2D model-building simpler and more efficient. Landlab's core features include: (1) a gridding engine that lets you create and configure a structured or unstructured grid in just a few lines of code, and to attach data directly to the grid; (2) a library of pre-built process components that saves you from having to re-invent the wheel with common geoscience algorithms (such as flow routing on gridded terrain, linear and nonlinear diffusion, and elastic plate flexure); (3) a mechanism for coupling components together to create integrated model; and (4) a suite of tools for input/output and other common operations. Although Landlab's components are primarily related to earth-surface dynamics (including geomorphology and hydrology), its basic framework is applicable to many types of geophysical system. This clinic provides a hands-on tutorial introduction to Landlab. Participants will learn about Landlab's capabilities, and how to use it to build and run simple 2D models. Familiarity with the Python language and the Numpy library is helpful but not critical.
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