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 3.10 & 3.11 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-3.0.2.tar.gz (73.9 kB view details)

Uploaded Source

Built Distribution

wecopttool-3.0.2-py3-none-any.whl (54.0 kB view details)

Uploaded Python 3

File details

Details for the file wecopttool-3.0.2.tar.gz.

File metadata

  • Download URL: wecopttool-3.0.2.tar.gz
  • Upload date:
  • Size: 73.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.4

File hashes

Hashes for wecopttool-3.0.2.tar.gz
Algorithm Hash digest
SHA256 e1f650097348d4b364fe7b642dac8fbfb43d90e6b971e1f216bd707ee99f26e9
MD5 dc568a9527fdc83bdafb080e42c1e3ae
BLAKE2b-256 014c24c4dc79b331331bfbd80a8e77b01cabdd03a36beb1e35f673c94cbbefb9

See more details on using hashes here.

File details

Details for the file wecopttool-3.0.2-py3-none-any.whl.

File metadata

  • Download URL: wecopttool-3.0.2-py3-none-any.whl
  • Upload date:
  • Size: 54.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.4

File hashes

Hashes for wecopttool-3.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 a4a7531fe1c70e806e35b1af1977867fb84d561c76a1fdf99a4eae6eba51e255
MD5 e2257f65f0c0e6f74ab1e19d8db132e7
BLAKE2b-256 57766766fc2d08d00576b4591608df6e1599ff4c0b074a0ca8e0be2204858cb7

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