Interpolation (scipy.interpolate)

When monotone samples are supplied, which interpolants preserve monotonicity and what approximation error do they incur?

Use evaluator-generated functions and dense independent probes to measure monotonicity, overshoot and approximation error.

What you could produce

• A versioned minimal protocol, evaluator-owned test cases, and a comparison report with numerical/statistical uncertainty and failures.

Before you use it

• SciPy 1.18.0
• Compatible NumPy and native numerical-library build; exact environment not prepared

Limits to keep in view

• No upstream example, source package, build hook or submitted code was executed.
• The current qualified pilot accepts only self-contained Python 3.13 with a 90-second deadline. This reference's package/runtime is not qualified for that path.
• The proposed protocol requires bounded resource estimates and an independently controlled evaluator before any scientific execution claim.

Source and permission context

SciPy documentation. Interpolation (scipy.interpolate). https://docs.scipy.org/doc/scipy-1.18.0/tutorial/interpolate.html; observed version 1.18.0.

Catalog listing reviewed. This review covers the description and source links displayed here.

Versioned SciPy 1.18.0 page and BSD-3-Clause documentation evidence reviewed.

Reviewed 2026-09-14. Copying or adapting source files remains subject to their own terms.

Before copying source material

• Third-party figures, linked papers, datasets and dependency licenses have not been assessed; no external content is copied into this catalog.