interlib
interlib is a Python library for interpolation methods designed as an alternative to scipy.interpolate. Being built on Rust, it provides reliable and in some cases faster solutions to unknown data point problems. It includes polynomial, piecewise, approximation-based and advanced interpolators for all of your needs.
Installation
pip install interlib-rs
The PyPI distribution name is interlib-rs, but the Python import name is still
interlib:
from interlib import LinearInterpolator
Local development (recommended)
From interlib/:
.venv/bin/python -m maturin develop
This rebuilds and installs the extension in editable mode inside the project venv.
Python API Quickstart
from interlib import LinearInterpolator
interp = LinearInterpolator()
interp.fit([0.0, 1.0, 2.0], [0.0, 1.0, 4.0])
# scalar input -> float
y1 = interp(1.5)
# sequence input -> ordered list[float]
y_many = interp([0.5, 1.5])
Common user errors are reported as ValueError (for example: unfitted usage,
length mismatch, invalid constructor parameters, out-of-range Chebyshev input).
Tutorial by Method Family
- Polynomial exact-fit (
LagrangeInterpolator,NewtonInterpolator): good for small clean datasets; can oscillate at high degree/uniform nodes. - Piecewise local (
LinearInterpolator,QuadraticInterpolator,CubicSplineInterpolator): robust defaults for many engineering signals; cubic spline is the smoothest common default. - Derivative-constrained (
HermiteInterpolator): use when you knowdyat sample points. - Approximation/noisy data (
LeastSquaresInterpolator): fit trend instead of exact point pass-through. - Kernel/global (
RBFInterpolator): flexible smooth interpolation; kernel/epsilon tuning matters. - Chebyshev function approximation (
ChebyshevInterpolator): stable high-accuracy approximation over fixed interval.
See GUIDE.md for a fuller selection guide and pitfalls.
Real-Data Benchmarks
The repository includes cached real-data benchmark entrypoints:
python python/benches/real_data_bench.py --dataset noaa --station KSFO --field temperature
python python/benches/real_data_bench.py --dataset nasa --command 499 --axis x
These benchmark against held-out real observations (NOAA/NASA) instead of only synthetic functions.
Case Studies
Runnable case studies:
python/case_studies/function_approx.py(includescos(x)and Runge function)python/case_studies/signal_rec.py(sampled signal reconstruction)python/case_studies/engineering.py(engineering-style datasets, including temperature profiles)
MATLAB Integration (important distinction)
MATLAB integration is not the Python wheel path.
- Python uses PyO3 + maturin wheels.
- MATLAB uses standalone Rust FFI shared library + MATLAB
.mwrappers.
MATLAB build path:
make matlab-build
MATLAB docs:
matlab/README.mdmatlab/MATLAB_DOCKER.md
MATLAB Release Notes
GitHub Actions currently targets standalone Rust MATLAB/FFI binaries.
The .mltbx toolbox package is built locally from a licensed MATLAB runtime:
MATLAB_IMAGE=my-matlab-image:auth make matlab-toolbox-package-batch
This writes dist/interlib.mltbx.
1 maintainer
Metadata
1 maintainer
Release files for interlib-rs 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| interlib_rs-0.1.0.tar.gz | 51.1 kB | Details |
Built distributions (wheels)
Total release size: 32.9 MB
Release files / interlib_rs-0.1.0.tar.gz
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