Optimized interpolation routines in Python / numba
The library contains:
splines.*: fast numba-compatible multilinear and cubic interpolation
multilinear.*: fast numba-compatible multilinear interpolation (alternative implementation)
smolyak.*: smolyak polynomials
- complete polynomials
Install latest version:
- from conda:
conda install -c conda-forge interpolation
- from PyPI:
pip install interpolation
Latest development version from git:
pip install poetry # if not already installed pip install git+https://github.com/econforge/interpolation.py.git/
The project uses a
pyproject.toml file instead of
setup.py and other legacy configuration files. For those used to development installation, this is feasible using
pip install dephell # if not already installed dephell --from=pyproject.toml --to=setup.py # only once pip install -e . # like old times
multilinear and cubic interpolation
Fast numba-accelerated interpolation routines for multilinear and cubic interpolation, with any number of dimensions. Several interfaces are provided.
Preferred interface for multilinear interpolation. It can interpolate on uniform and nonuniform cartesian grids. Several extrapolation options are available.
import numpy as np from interpolation.splines import UCGrid, CGrid, nodes # we interpolate function f = lambda x,y: np.sin(np.sqrt(x**2+y**2+0.00001))/np.sqrt(x**2+y**2+0.00001) # uniform cartesian grid grid = UCGrid((-1.0, 1.0, 10), (-1.0, 1.0, 10)) # get grid points gp = nodes(grid) # 100x2 matrix # compute values on grid points values = f(gp[:,0], gp[:,1]).reshape((10,10)) from interpolation.splines import eval_linear # interpolate at one point point = np.array([0.1,0.45]) # 1d array val = eval_linear(grid, values, point) # float # interpolate at many points: points = np.random.random((10000,2)) eval_linear(grid, values, points) # 10000 vector # output can be preallocated out = np.zeros(10000) eval_linear(grid, values, points, out) # 10000 vector ## jitted, non-uniform multilinear interpolation # default calls extrapolate data by using the nearest value inside the grid # other extrapolation options can be chosen among NEAREST, LINEAR, CONSTANT from interpolation.splines import extrap_options as xto points = np.random.random((100,2))*3-1 eval_linear(grid, values, points, xto.NEAREST) # 100 eval_linear(grid, values, points, xto.LINEAR) # 10000 vector eval_linear(grid, values, points, xto.CONSTANT) # 10000 vector # one can also approximate on nonuniform cartesian grids grid = CGrid(np.linspace(-1,1,100)**3, (-1.0, 1.0, 10)) points = np.random.random((10000,2)) eval_linear(grid, values, points) # 10000 vector # it is also possible to interpolate vector-valued functions in the following way f = lambda x,y: np.sin(x**3+y**2+0.00001)/np.sqrt(x**2+y**2+0.00001) g = lambda x,y: np.sin(x**3+y**2+0.00001)/np.sqrt(x**2+y**2+0.00001) grid = UCGrid((-1.0, 1.0, 10), (-1.0, 1.0, 10)) gp = nodes(grid) # 100x2 matrix mvalues = np.concatenate([ f(gp[:,0], gp[:,1]).reshape((10,10))[:,:,None], g(gp[:,0], gp[:,1]).reshape((10,10))[:,:,None] ],axis=2) # 10x10x2 array points = np.random.random((1000,2)) eval_linear(grid, mvalues, points[:,1]) # 2 elements vector eval_linear(grid, mvalues, points) # 1000x2 matrix out = np.zeros((1000,2)) eval_linear(grid, mvalues, points, out) # 1000x2 matrix # finally, the same syntax can be used to interpolate using cubic splines # one just needs to prefilter the coefficients first # the same set of options apply but nonuniform grids are not supported (yet) f = lambda x,y: np.sin(x**3+y**2+0.00001)/np.sqrt(x**2+y**2+0.00001) grid = UCGrid((-1.0, 1.0, 10), (-1.0, 1.0, 10)) gp = nodes(grid) # 100x2 matrix values = f(gp[:,0], gp[:,1]).reshape((10,10)) # filter values from interpolation.splines import filter_cubic coeffs = filter_cubic(grid, values) # a 12x12 array from interpolation.splines import eval_cubic points = np.random.random((1000,2)) eval_cubic(grid, coeffs, points[:,1]) # 2 elements vector eval_cubic(grid, coeffs, points) # 1000x2 matrix out = np.zeros((1000,2)) eval_cubic(grid, coeffs, points, out) # 1000x2 matrix
Remark: the arguably strange syntax for the extapolation option comes from the fact the actualy method called must be determined by type inference. So
eval_linear(..., extrap_method='linear') would not work because the type of the last argument is always a string. Instead, we use opts.CONSTANT and opts.LINEAR for instance which have different numba types.
Despite what it looks
CGRid are not objects but functions which return very simple python structures that is a tuple of its arguments. For instance,
((0.0,1.0, 10), (0.0,1.0,20)) represents a 2d square discretized with 10 points along the first dimension and 20 along the second dimension. Similarly
(np.array([0.0, 0.1, 0.3, 1.0]), (0.0, 1.0, 20)) represents a square nonuniformly discretized along the first dimension (with 3 points) but uniformly along the second one. Now type dispatch is very sensitive to the exact types (floats vs ints), (tuple vs lists) which is potentially error-prone. Eventually, the functions
CGrid will provide some type check and sensible conversions where it applies. This may change when if a parameterized structure-like object appear in numba.
Simpler interface. Mimmicks default
scipy.interp: mutlilinear interpolation with constant extrapolation.
### 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)
This is for compatibility purpose only, until a new jittable model object is found.
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]) # 50 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,) array
Old, unfair timings: (from
# 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 (outdated)
Missing but available soon: - splines at any order - derivative
Feasible (some experiments) - evaluation on the GPU (with numba.cuda) - parallel evaluation (with guvectorize)
See testfile for examples.