scipy.interpolate.

NearestNDInterpolator#

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.

Methods

__call__(*args, **query_options)

Evaluate interpolator at given points.

See also

griddata: Interpolate unstructured D-D data.
LinearNDInterpolator: Piecewise linear interpolator in N dimensions.
CloughTocher2DInterpolator: Piecewise cubic, C1 smooth, curvature-minimizing interpolator in 2D.
interpn: Interpolation on a regular grid or rectilinear grid.
RegularGridInterpolator: Interpolator on a regular or rectilinear grid in arbitrary dimensions (interpn wraps this class).

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()

../../_images/scipy-interpolate-NearestNDInterpolator-1.png