# scipy.interpolate.LinearNDInterpolator

class scipy.interpolate.LinearNDInterpolator(points, values, fill_value=np.nan, rescale=False)

  Piecewise linear interpolant in N > 1 dimensions.

  New in version 0.9.

  Parameters:
    points ndarray of floats, shape (npoints, ndims); or Delaunay

      2-D array of data point coordinates, or a precomputed Delaunay triangulation.

    values ndarray of float or complex, shape (npoints, …)

      N-D array of data values at points. The length of values along the first axis must be equal to the length of points. Unlike some interpolators, the interpolation axis cannot be changed.

    fill_value float, optional

      Value used to fill in for requested points outside of the convex hull of the input points. If not provided, then the default is `nan`.

    rescale bool, 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.

  See also

  `griddata`

    Interpolate unstructured D-D data.

  `NearestNDInterpolator`

    Nearest-neighbor interpolation 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

  The interpolant is constructed by triangulating the input data with Qhull [1], and on each triangle performing linear barycentric interpolation.

  Note

  For data on a regular grid use `interpn` instead.

  References

  [1]

  http://www.qhull.org/

  Examples

  We can interpolate values on a 2D plane:

      >>> from scipy.interpolate import LinearNDInterpolator
      >>> 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 = LinearNDInterpolator(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__`(xi) | Evaluate interpolator at given points.