scisample 1.0.1

Parameter sampling for scientific computing

# scisample

scisample is a Python 3 package that implements a number of parameter sampling methods for scientific computing. Specifications for sampling are written in the YAML markup language.

# Installation with a python virtual environment

1. cd into the top level scisample directory 1. python3 -m venv venv 1. source venv/bin/activate 1. pip install -r requirements.txt 1. pip install -e .

# Documentation

1. cd docs into the top level scisample directory 1. make <documentation type>, where <documentation type> includes ‘html’, ‘latexpdf’, ‘text’, etc.

# Testing

1. cd into the top level scisample directory 1. pytest tests 1. pytest –cov=scisample tests/

# Community

scisample is an open source project. Questions, discussion, and contributions are welcome. Contributions can be anything from new packages to bugfixes, documentation, or even new core features.

# Contributing

Contributing to scisample is relatively easy. Just send us a pull request. When you send your request, make develop the destination branch on the scisample repository.

Your PR must pass scisamples’s unit tests and documentation tests, and must pass most flake8 and pylint tests. We enforce these guidelines with our CI process. Please see [CONTRIBUTING.md](./CONTRIBUTING.md) for more information.

# Code of Conduct

Please note that scisample has a [Code of Conduct](./CODE_OF_CONDUCT.md). By participating in the scisample community, you agree to abide by its rules.

# Authors

Current authors of scisample include Brian Daub, Chris Krenn, Cody Raskin, & Jessica Semler.

# License

scisample is distributed under the the MIT license.

All new contributions must be made under the MIT license.

Please see [LICENSE](./LICENSE) and [NOTICE](./NOTICE) for details.

SPDX-License-Identifier: MIT

LLNL-CODE-815909