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

Piecewise linear interpolator in N > 1 dimensions.

Added in version 0.9.

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

2-D array of data point coordinates, or a precomputed Delaunay triangulation.

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

N-D array of data values at points. The length of values along the first axis must be equal to the length of points. Unlike some interpolators, the interpolation axis cannot be changed.

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 interpolator in N dimensions.


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


Interpolation on a regular grid or rectilinear grid.


Interpolator on a regular or rectilinear grid in arbitrary dimensions (interpn wraps this class).


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


For data on a regular grid use interpn instead.



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.