cubicmultispline 0.1.2

Generation of cubic, multivariate splines from samples with arbitrary boundary conditions

CubicMultiSpline documentation

Library for cubic, multivariate spline interpolation from samples with arbitrary boundary conditions for each dimension. Optional GPU backend available via PyTorch. The full documentation can be found at https://cubicmultispline.readthedocs.io.

Overview

This library implements the recursive algorithm by Habermann and Kindermann in the Spline class. The 1-dimensional base case, which is needed during recursion is implemented in the Spline1D class. In contrast to other multivariate spline implementations, this library allows for arbitrary boundary conditions for each dimension, that is

1. not-a-knot
2. first order (clamped)
3. second order (natural)
4. periodic

Additionally, the library provides an efficient function eval_spline to evaluate the spline at arbitrary points inside the domain. The GPU backend is accessed via the TorchSpline and TorchSpline1D classes.

Installation

The easiest way to install the library is to use pip:

pip install cubicmultispline

If you want to use the PyTorch backend, install with:

pip install cubicmultispline[torch]

Alternatively, you can install the library from source:

python setup.py install

License

This library is licensed under the MIT License.