Below are a few methods to solve the task. In Python, we can use scipy's function CubicSpline to perform cubic spline interpolation. NearestNDInterpolator from scipy.interpolate import LinearNDInterpolator > import matplotlib.pyplot as plt Interpolation ( scipy.interpolate ) Rescale points to unit cube before performing interpolation. Import numpy as np import scipy.interpolate as interp # auxiliary function for mesh This is due to the fact that griddata only works inside the convex hull of the input produces well-behaved output even for crazy input data supports interpolation in higher Python Program to Check if a Number is Positive, Negative or 0. V : 1D array Array with the scalar value assigned to the data points (not vp ((x, y), v, (xp, yp), methodalgorithm).ravel() if 'nearest' and np.any(np.isnan(vp)): fill_nans(x, y, v, xp, yp, vp) return vp Utility function that generates a regular grid with :func:`~` and calls Piecewise linear from scipy.interpolate import CloughTocher2DInterpolator > import but for this smooth function the piecewise cubic interpolant gives the best results. Value used to fill in for requested points outside of the convex hull of the input points. (points, values, xi, method'linear', fill_valuenan, return the value at the data point closest to the point of interpolation.
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