README.md

Optimized interpolation routines in Python

This library provides fast interpolation routines (with Numba and optional Cython) for the following cases:

multilinear and cubic splines in any dimension

Here is an example in 3 dimensions:

from interpolation.splines import CubicSpline
a = np.array([0.0,0.0,0.0])         # lower boundaries
b = np.array([1.0,1.0,1.0])         # upper boundaries
orders = np.array([10,10,10])       # 10 points along each dimension
values = np.random.random(orders)   # values at each node of the grid
spline = CubicSpline(a,b,orders,values) # filter the coefficients
S = np.random.random((100000,3))    # coordinates at which to evaluate the spline
V = spline(S)                       # interpolates -> (100000,) array

More details are available as an example notebook

Other available features are:

• linear extrapolation
• computation of derivatives
• interpolate many functions at once (multi-splines or vector valued splines)
• evaluation on the GPU, with a few adjustments

smolyak interpolation

See testfile for examples.