class scipy.interpolate.LinearNDInterpolator(points, values, fill_value=np.nan, rescale=False)#

Piecewise linear interpolant in N > 1 dimensions.

New in version 0.9.

pointsndarray of floats, shape (npoints, ndims); or Delaunay

Data point coordinates, or a precomputed Delaunay triangulation.

valuesndarray of float or complex, shape (npoints, …)

Data values.

fill_valuefloat, optional

Value used to fill in for requested points outside of the convex hull of the input points. If not provided, then the default is nan.

rescalebool, optional

Rescale points to unit cube before performing interpolation. This is useful if some of the input dimensions have incommensurable units and differ by many orders of magnitude.

See also


Interpolate unstructured D-D data.


Nearest-neighbor interpolation in N dimensions.


Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D.


The interpolant is constructed by triangulating the input data with Qhull [1], and on each triangle performing linear barycentric interpolation.



We can interpolate values on a 2D plane:

>>> from scipy.interpolate import LinearNDInterpolator
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> rng = np.random.default_rng()
>>> x = rng.random(10) - 0.5
>>> y = rng.random(10) - 0.5
>>> z = np.hypot(x, y)
>>> X = np.linspace(min(x), max(x))
>>> Y = np.linspace(min(y), max(y))
>>> X, Y = np.meshgrid(X, Y)  # 2D grid for interpolation
>>> interp = LinearNDInterpolator(list(zip(x, y)), z)
>>> Z = interp(X, Y)
>>> plt.pcolormesh(X, Y, Z, shading='auto')
>>> plt.plot(x, y, "ok", label="input point")
>>> plt.legend()
>>> plt.colorbar()
>>> plt.axis("equal")



Evaluate interpolator at given points.