scipy.interpolate.NearestNDInterpolator

class NearestNDInterpolator(x, y, rescale=False, tree_options=None)

NearestNDInterpolator(x, y).

Nearest-neighbor interpolator in N > 1 dimensions.

Added in version 0.9.

Methods

__call__

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.

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()

Check shape of points and values arrays, and reshape values to (npoints, nvalues). Ensure the points and values arrays are C-contiguous, and of correct type.

__init__(x, y, rescale=False, tree_options=None)

Check shape of points and values arrays, and reshape values to (npoints, nvalues). Ensure the points and values arrays are C-contiguous, and of correct type.

Methods

__init__(x, y[, rescale, tree_options])

Check shape of points and values arrays, and reshape values to (npoints, nvalues).