NearestNDInterpolator — SciPy v1.15.3 Manual (original) (raw)

scipy.interpolate.

class scipy.interpolate.NearestNDInterpolator(x, y, rescale=False, tree_options=None)[source]#

NearestNDInterpolator(x, y).

Nearest-neighbor interpolator in N > 1 dimensions.

Added in version 0.9.

Parameters:

x(npoints, ndims) 2-D ndarray of floats

Data point coordinates.

y(npoints, ) 1-D ndarray of float or complex

Data values.

rescaleboolean, 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.

Added in version 0.14.0.

tree_optionsdict, optional

Options passed to the underlying cKDTree.

Added in version 0.17.0.

Notes

Uses scipy.spatial.cKDTree

Note

For data on a regular grid use interpn instead.

Examples

We can interpolate values on a 2D plane:

from scipy.interpolate import NearestNDInterpolator 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 = NearestNDInterpolator(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") plt.show()

Methods