Nearest-neighbor interpolation in N > 1 dimensions.
Uses scipy.spatial.cKDTree
Data point coordinates.
Data values.
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.
Options passed to the underlying cKDTree
.
NearestNDInterpolator(x, y).
CloughTocher2DInterpolator
Piecewise cubic, C1 smooth, curvature-minimizing interpolant in 2D.
LinearNDInterpolator
Piecewise linear interpolant in N dimensions.
griddata
Interpolate unstructured D-D data.
We can interpolate values on a 2D plane:
>>> from scipy.interpolate import NearestNDInterpolator
... 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()
The following pages refer to to this document either explicitly or contain code examples using this.
scipy.interpolate._interpolate.interpn
scipy.interpolate._ndgriddata.griddata
scipy.interpolate._interpolate.RegularGridInterpolator
scipy.interpolate.interpnd.LinearNDInterpolator
scipy.interpolate._rbfinterp.RBFInterpolator
scipy.interpolate._ndgriddata.NearestNDInterpolator
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