# scipy.interpolate.NearestNDInterpolator

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

  NearestNDInterpolator(x, y).

  Nearest-neighbor interpolation in N > 1 dimensions.

  New 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.

    rescale boolean, 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.

      New in version 0.14.0.

    tree_options dict, optional

      Options passed to the underlying `cKDTree`.

      New in version 0.17.0.

  See also

  `griddata`

    Interpolate unstructured D-D data.

  `LinearNDInterpolator`

    Piecewise linear interpolant in N dimensions.

  `CloughTocher2DInterpolator`

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

  `interpn`

    Interpolation on a regular grid or rectilinear grid.

  `RegularGridInterpolator`

    Interpolation 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()

  Methods

  `__call__`(*args) | Evaluate interpolator at given points.