Thank you for your interest in contributing to BestFit-Python-Examples! We welcome bug reports, feature requests, validation results, and other feedback from the community.
This software is maintained by a small team within the U.S. Army Corps of Engineers Risk Management Center (USACE-RMC). Our capacity to review external pull requests is very limited. We prioritize issues and bug reports, which are always welcome and will be reviewed as resources permit.
If you plan to submit a pull request, please open an issue first to discuss your proposed change. This helps avoid duplicated effort and ensures your contribution aligns with the project's direction.
If you find a bug, please open an issue and include:
- Steps to reproduce the problem
- Input data and configuration (if applicable)
- Expected behavior vs. actual behavior
- Software version and operating system
- Any relevant error messages or screenshots
Feature requests are welcome. Please open an issue describing:
- The use case or problem you are trying to solve
- How you envision the feature working
- Any references to statistical methods or published literature
Given the flood-risk applications of BestFit and its Numerics dependency, independent validation is especially valuable. If you have compared this software's results against other software (e.g., R packages, published tables, or analytical solutions), we would appreciate hearing about it through an issue.
Pull requests may take several weeks or longer to review. Before submitting code:
- Open an issue first to discuss the proposed change
- Follow the coding standards, including docstrings for reusable Python interfaces
- Include unit tests that validate against known results (R, SciPy, Mathematica, or published tables)
- Ensure a clean build with zero errors and warnings
For this curriculum, retain the existing app example's raw observations, data provenance,
model settings and scientific context. Keep the teaching surface at 12 notebooks.
Preserve notebook narratives unless a source-verified technical correction is needed.
Upstream AI-generated narration is still awaiting author review.
Put reusable plotting changes in RMC-BestFit's canonical bestfit_plots package.
Edit scripts/notebook_sources/ and regenerate with scripts/create_notebooks.py.
Keep data/model/analysis construction, substantive configuration, execution calls and
result access visible in notebook cells. Helpers may handle loading, conversion and plotting.
Run the Python contract tests and scripts/validate_notebooks.py --write; validation
executes every selected notebook in an independent kernel with saved projects and
results unavailable. Never replace source data or shorten sampling to obtain a pass.
By submitting a pull request, you certify under the Developer Certificate of Origin (DCO) Version 1.1 that you have the right to submit the work under the license associated with this project and that you agree to the DCO.
All contributions will be released under the same license as the project (see LICENSE).
U.S. Federal law prevents the government from accepting gratuitous services unless certain conditions are met. By submitting a pull request, you acknowledge that your services are offered without expectation of payment and that you expressly waive any future pay claims against the U.S. Federal government related to your contribution.
If you are a U.S. Federal government employee and use a *.mil or *.gov email address, your contribution is understood to have been created in whole or in part as part of your official duties and is not subject to domestic copyright protection under 17 USC 105.
If you discover a security vulnerability, please do not open a public issue. Instead, contact the RMC team directly through official USACE-RMC channels.
See LICENSE for the Zero-Clause BSD terms.