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-1.5.0.tar.gz (2.6 MB view details)

Uploaded Source

Built Distribution

ocsmesh-1.5.0-py3-none-any.whl (2.6 MB view details)

Uploaded Python 3

File details

Details for the file ocsmesh-1.5.0.tar.gz.

File metadata

  • Download URL: ocsmesh-1.5.0.tar.gz
  • Upload date:
  • Size: 2.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for ocsmesh-1.5.0.tar.gz
Algorithm Hash digest
SHA256 e07d38c29a5f9e9e3dc408ce4edfc1d8d5097f53d890f7a2628b1ec0ffd109a0
MD5 c529543d41da693bb9c573038adc4a67
BLAKE2b-256 e7247239a40cae7ac2c38923e1e6908d30456d564fe95e3e32a66bdcf407c815

See more details on using hashes here.

File details

Details for the file ocsmesh-1.5.0-py3-none-any.whl.

File metadata

  • Download URL: ocsmesh-1.5.0-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/3.9.18

File hashes

Hashes for ocsmesh-1.5.0-py3-none-any.whl
Algorithm Hash digest
SHA256 8b9412936ca228a4582906e37d617c93f861c3b3bcc6c4125f2e8049090a9f60
MD5 76a4df9a13f70324a3e01683ee175ef0
BLAKE2b-256 5323f6dc86c2754612c272a5ae67564c945ef29510f21b6190b51469e1a5fbd1

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