This library provides fast numba-accelerated interpolation routines for the following cases:
Here is an example in 3 dimensions:
from interpolation.splines import LinearSpline, 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([50,50,50]) # 10 points along each dimension
values = np.random.random(orders) # values at each node of the grid
S = np.random.random((10^6,3)) # coordinates at which to evaluate the splines
# multilinear
lin = LinearSpline(a,b,orders,values)
V = lin(S)
# cubic
spline = CubicSpline(a,b,orders,values) # filter the coefficients
V = spline(S) # interpolates -> (100000,) arrayUnfair timings: (from misc/speed_comparison.py)
# interpolate 10^6 points on a 50x50x50 grid.
Cubic: 0.11488723754882812
Linear: 0.03426337242126465
Scipy (linear): 0.6502540111541748
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)
Experimental
- evaluation on the GPU (with numba.cuda)
- parallel evaluation (with guvectorize)
In the near future:
- JIT classes for all interpolation objects
There is a simple interp function with a flexible API which does multinear on uniform or non uniform cartesian grids.
### 1d grid
from interpolation import interp
x = np.linspace(0,1,100)**2 # non-uniform points
y = np.linspace(0,1,100) # values
# interpolate at one point:
interp(x,y,0.5)
# or at many points:
u = np.linspace(0,1,1000) # points
interp(x,y,u)
See testfile for examples.