To help you get started, we've collected the most common ways that chaospy is being used within popular public projects.

simetenn / uncertainpy / tests / test_parameters.py View on Github

def test_iter(self):
        parameter_list = [["gbar_Na", 120, cp.Uniform(110, 130)],
                         ["gbar_K", 36, cp.Normal(36, 1)],
                         ["gbar_L", 0.3, cp.Chi(1, 1, 0.3)]]

        parameters = Parameters(parameter_list)

        result = [parameter for parameter in parameters]

        self.assertEqual(len(result), 3)

        self.assertIsInstance(result[0], Parameter)
        self.assertIsInstance(result[1], Parameter)
        self.assertIsInstance(result[2], Parameter)

View more ways to use chaospy

• Documentation
• Interactive tutorials with Binder
• Code of conduct
• Contribution guideline
• Changelog
• License

Chaospy is a numerical toolbox designed for performing uncertainty quantification through polynomial chaos expansions and advanced Monte Carlo methods implemented in Python. It includes a comprehensive suite of tools for low-discrepancy sampling, quadrature creation, polynomial manipulations, and much more.

The philosophy behind chaospy is not to serve as a single solution for all uncertainty quantification challenges, but rather to provide specific tools that empower users to solve problems themselves. This approach accommodates well-established problems but also serves as a foundry for experimenting with new, emerging problems. Emphasis is placed on the following:

• Focus on an easy-to-use interface that embraces the pythonic code style <https://docs.python-guide.org/writing/style/&gt;.
• Ensure the code is "composable," meaning it's designed so that users can easily and effectively modify parts of the code with their own solutions.
• Strive to support a broad range of methods for uncertainty quantification where it makes sense to use chaospy.
• Ensure that chaospy integrates well with a wide array of other projects, including numpy <https://numpy.org/&gt;, scipy <https://scipy.org/&gt;, scikit-learn <https://scikit-learn.org>, statsmodels <https://statsmodels.org/&gt;, openturns <https://openturns.org/&gt;, and gstools <https://geostat-framework.org/&gt;, among others.
• Contribute all code as open source to the community.

Installation

Installation is straightforward via pip:

pip install chaospy

Alternatively, if you prefer Conda:

conda install -c conda-forge chaospy

After installation, visit the documentation to learn how to use the toolbox.

Development

To install chaospy and its dependencies in developer mode:

pip install -e .[dev]

Testing

To run tests on your local system:

pytest --doctest-modules chaospy/ tests/ README.rst

Documentation

Ensure that pandoc is installed and available in your path to build the documentation.

From the docs/ directory, build the documentation locally using:

cd docs/
make html

Run make without arguments to view other build targets. The HTML documentation will be output to doc/.build/html.