Skip to main content

WEC Design Optimization Toolbox

Project description

Test-WecOptTool Coverage Status

WecOptTool

The Wave Energy Converter Design Optimization Toolbox (WecOptTool) allows users to perform wave energy converter (WEC) device design optimization studies with constrained optimal control.

NOTE: If you are looking for the WecOptTool code used in previous published work (MATLAB version) please see WecOptTool-MATLAB.

Project Information

Refer to WecOptTool documentation for more information, including project overview, tutorials, theory, and API documentation.

Getting started

If you are brand new to Python and/or want detailed installation instructions, click here.

WecOptTool requires Python >= 3.8. Python 3.11 & 3.12 are supported. It is strongly recommended you create a dedicated virtual environment (e.g., using conda, mamba, venv, etc.) before installing WecOptTool.

From your dedicated environment, you can install WecOptTool via conda, pip, or mamba:

Option 1 - using Conda:

conda install -c conda-forge wecopttool

Option 2 - using pip (requires Fortran compilers on your system):

pip install wecopttool

Option 3 - using Mamba:

mamba install wecopttool

Geometry module and tutorials

To use our geometry examples, including for running the tutorials, you will need to install some additional dependencies. For the tutorials you will also need to install jupyter.

pip install wecopttool[geometry] jupyter

or on a Mac (Zsh shell)

pip install wecopttool\[geometry] jupyter

Tutorials

The tutorials can be found in the examples directory and are written as Jupyter Notebooks. To run the tutorials, first download the notebook files and then, from the directory containing the notebooks, run jupyter notebook. Using git to obtain the notebooks this can be done by running

git clone https://github.com/sandialabs/WecOptTool.git
cd WecOptTool/examples
jupyter notebook

Getting help

To report bugs, use WecOptTool's issues page. For general discussion, use WecOptTool's discussion page

Contributing

If you are interested in contributing to WecOptTool, see our contribution guidelines.

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

wecopttool-3.1.1.tar.gz (73.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

wecopttool-3.1.1-py3-none-any.whl (55.5 kB view details)

Uploaded Python 3

File details

Details for the file wecopttool-3.1.1.tar.gz.

File metadata

  • Download URL: wecopttool-3.1.1.tar.gz
  • Upload date:
  • Size: 73.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for wecopttool-3.1.1.tar.gz
Algorithm Hash digest
SHA256 767270d4dc8ae0ea7b80329b15f0c05f8b24fb249b0d61d812e2d1ce0bb01771
MD5 7a97da18d246d3af05d7bf87f3745989
BLAKE2b-256 87b61ec1bc71e2cd84c0359f333e36b55a71adab554aa64b13ff0e598a43282d

See more details on using hashes here.

File details

Details for the file wecopttool-3.1.1-py3-none-any.whl.

File metadata

  • Download URL: wecopttool-3.1.1-py3-none-any.whl
  • Upload date:
  • Size: 55.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.13.7

File hashes

Hashes for wecopttool-3.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 70a910ccc9657cae8a1ea9235f43affa2d95caf9e264edb0abdedb9bf7fe7ad0
MD5 e09a0311494aa3ae02048dc2a3f94f54
BLAKE2b-256 9ce3689b3d6255bb1d466079805974a5e1bd1fc0e97e61cc2d5d13c9e6b73163

See more details on using hashes here.

Supported by

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