ocsmesh 1.7.0

Package to generate computational unstructured meshes from planetary modeling.

OCSMesh

OCSMesh is a Python package for processing DEM data into georeferenced unstructured meshes using the jigsaw-python library.

Installation

Two ways of installing OCSMesh are described below:

Using conda

The recommended way to setup the environment for installing OCSMesh is to use conda with the environment.yml file provided in the repo to install required libraries.

The Jigsaw library and its Python wrapper must be instlled before OCSMesh can be used. Jigsaw is available on conda-forge channel.

First you need to download the environment.yml file.

wget https://raw.githubusercontent.com/noaa-ocs-modeling/OCSMesh/main/environment.yml

conda env create -f environment.yml -n your-env-name
conda activate your-env-name

conda install -y -c conda-forge jigsawpy
pip install ocsmesh

From GitHub repo

OCSMesh can be installed from the GitHub repository as well. After downloading the repo, you need to first install Jigsaw using the script provided in OCSMesh repo by calling: ./setup.py install_jigsaw in the OCSMesh root directory. Then OCSMesh can be installed.

git clone https://github.com/noaa-ocs-modeling/ocsmesh
cd ocsmesh
python ./setup.py install_jigsaw # To install latest Jigsaw from GitHub
python ./setup.py install # Installs the OCSMesh library to the current Python environment
# OR
python ./setup.py develop # Run this if you are a developer.

Requirements

• 3.10 <= Python
• CMake
• C/C++ compilers

How to Cite

Title : OCSMesh: a data-driven automated unstructured mesh generation software for coastal ocean modeling
Personal Author(s) : Mani, Soroosh;Calzada, Jaime R.;Moghimi, Saeed;Zhang, Y. Joseph;Myers, Edward;Pe’eri, Shachak;
Corporate Authors(s) : Coast Survey Development Laboratory (U.S.)
Published Date : 2021
Series : NOAA Technical Memorandum NOS CS ; 47
DOI : https://doi.org/10.25923/csba-m072

Title : OCSMesh and an end-to-end workflow for fully automatic mesh generation with application to compound flood studies
Personal Author(s) : Cassalho, Felicio;Mani, Soroosh;Ye, Fei;Zhang, Y. Joseph;Moghimi, Saeed;
Published Date : Pre-print(2025)
DOI : http://dx.doi.org/10.2139/ssrn.5226658