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()