quends 0.1.2

A Python package for quantifying uncertainty in ensembles of data streams.

Quantification of Uncertainty in ENsembles of Data Streams (QUENDS)

Evans Etrue Howard, Abeyah Calpatura, Pieterjan Robbe, Bert Debusschere

Overview

This project focuses on uncertainty quantification in plasma turbulent simulations. It includes modules for loading and processing NetCDF and CSV datasets, estimating steady states, computing effective sample sizes, and running uncertainty quantification analyses. The project is structured into multiple Python scripts, each handling different aspects of the analysis. To see more information on documentation, etc... visit our website here.

Table of Contents

• Installation
• Usage
• For Developers
• Documentation
• Examples
• Summary
• Contributing
• License

Installation

1. Install the package and dependencies: You can install the package along with its dependencies using pip:

   pip install quends
   

2. Verify the installation: To ensure that the installation was successful, you can run a simple test:

   python -c "import quends; print('quends installed successfully')"
   

Usage

Examples are shown in the examples/notebooks directories.

• cgyro: Contains all CGYRO data
• gx: Contains all gx data
• gx/ensemble: Contains all ensemble data
• DataStream_Guide-CGRYO.ipynb: DataStream guide for CGYRO data
• DataStream_Guide-GX.ipynb: DataStream guide for GX data
• DataStream_Guide-Ensemble.ipynb: DataStream guide for Ensembles
• DataStream_Guide.ipynb: DataStream guide

For Developers

1. Clone the repository:

   • Using SSH:

   git clone [e-mail]:sandialabs/quends.git
   cd quends
   

   • Using HTTPS:

   git clone https://github.com/sandialabs/quends.git
   cd quends
   

2. Install the package and dependencies: You can install the package along with its dependencies using pip:

   pip install -e .\[dev\]
   

3. Install pre-commit hooks To ensure code quality and consistency, install:

   pre-commit install
   

4. Run Ruff: For linting and fixing issues:

   ruff check --fix
   

5. Run isort: To format your code with Black:

   isort .
   

6. Run Black: To format your code with Black:

   black .
   

Developers: Publishing quends to PyPI

This section is for maintainers who publish new releases of quends.

1. Update the version Update the version in pyproject.oml before each release.

2. Build the package

   python3 -m build
   

3. Test on TestPyPI

   python3 -m twine upload --repository testpypi dist/*
   pip install -i https://test.pypi.org/simple/ quends
   

4. Upload to PyPI

   python3 -m twine upload dist/*
   

Documentation

For comprehensive information on how to use the QUENDS package, please refer to our official documentation.

Summary

Key functionalities include:

• Data Handling: Seamlessly load and preprocess data from various formats, including CSV, JSON, and NetCDF.
• Statistical Analysis: Compute essential statistics and assess data quality with built-in methods for effective sample size estimation and confidence interval calculations.
• Visualization: Create informative plots to visualize trends, correlations, and patterns in time series data.

Contributing

Feel free to submit issues and merge requests. For major changes, please open an issue first to discuss what you would like to change.

License

BSD 3-Clause License

Copyright 2025 National Technology & Engineering Solutions of Sandia, LLC (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights in this software.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.