Skip to main content

Package to generate computational unstructured meshes from planetary modeling.

Project description

lint workflow fnc workflow fnc2 workflow

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

  • 3.9 <= 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

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

ocsmesh-1.5.4.tar.gz (28.3 MB view details)

Uploaded Source

Built Distribution

ocsmesh-1.5.4-py3-none-any.whl (28.5 MB view details)

Uploaded Python 3

File details

Details for the file ocsmesh-1.5.4.tar.gz.

File metadata

  • Download URL: ocsmesh-1.5.4.tar.gz
  • Upload date:
  • Size: 28.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.9.19

File hashes

Hashes for ocsmesh-1.5.4.tar.gz
Algorithm Hash digest
SHA256 42f5787f13b7dea7b5f73346ecb1a93eb6f28cbaccbb2fc114aad6bc75fb2e2b
MD5 d75d4387c405eefe6aae2cd5a8a97f69
BLAKE2b-256 6e8a7e0d8fd5bfefebe78b6522289a06d988d6101687115e5c4092def10cce91

See more details on using hashes here.

File details

Details for the file ocsmesh-1.5.4-py3-none-any.whl.

File metadata

  • Download URL: ocsmesh-1.5.4-py3-none-any.whl
  • Upload date:
  • Size: 28.5 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.9.19

File hashes

Hashes for ocsmesh-1.5.4-py3-none-any.whl
Algorithm Hash digest
SHA256 d11f86dabc75d02f5d3302427f2be8933c481587c17b69caf6b0e2e6e9213a7a
MD5 686361fb285d00c434e0de473b74ea4e
BLAKE2b-256 0480f2606f813c8720aa7383776df2f11014af0304f6c87413468672420ff2f4

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page