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-1.3.5.tar.gz (2.6 MB view details)

Uploaded Source

Built Distribution

ocsmesh-1.3.5-py3-none-any.whl (2.6 MB view details)

Uploaded Python 3

File details

Details for the file ocsmesh-1.3.5.tar.gz.

File metadata

  • Download URL: ocsmesh-1.3.5.tar.gz
  • Upload date:
  • Size: 2.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for ocsmesh-1.3.5.tar.gz
Algorithm Hash digest
SHA256 81d87a347c33943618f586fcf47357e5a5f85d2200cd0774981f32d1fa25a28d
MD5 e1d7234fdcf0101c5a00438f476beaf1
BLAKE2b-256 df7ab7c7cfc6b8d28e1c0690f6d4736983dfd7a99d3b75f5fc7cda1816d35f83

See more details on using hashes here.

File details

Details for the file ocsmesh-1.3.5-py3-none-any.whl.

File metadata

  • Download URL: ocsmesh-1.3.5-py3-none-any.whl
  • Upload date:
  • Size: 2.6 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for ocsmesh-1.3.5-py3-none-any.whl
Algorithm Hash digest
SHA256 f7805c95fecd8a1306fac924c19990c100a2575a19bb07726aae992118a3b972
MD5 ef7cb0575cdb6026f435bf1847bc9f4f
BLAKE2b-256 67a170f18db8ad4e7d32da0c77bcb05b49e767adf6f96af6bbb0f06f6ce6a04c

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