cupyx.scipy.interpolate.LinearNDInterpolator#

class cupyx.scipy.interpolate.LinearNDInterpolator(points, values, fill_value=cupy.nan, rescale=False)[source]#

Piecewise linear interpolant in N > 1 dimensions.

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, ...), optional) – 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.

Notes

The interpolant is constructed by triangulating the input data with GDel2D [1], and on each triangle performing linear barycentric interpolation.

Note

For data on a regular grid use interpn instead.

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()