# Barycentric Lagrange interpolation on Chebyshev points¶

Let us interpolate the Runge function on $[-1,+1]$ $$f(x) = \frac{1}{1 + 16 x^2}$$ using the Barycentric formula $$p(x) = \frac{ \sum\limits_{i=0}^N{}' \frac{(-1)^i}{x - x_i} f_i }{ \sum\limits_{i=0}^N{}' \frac{(-1)^i}{x - x_i} }$$ where the prime on the summation means that the first and last terms must be multiplied by a factor of half.

In :
import numpy as np
from matplotlib import pyplot as plt

In :
def fun(x):
f = 1.0/(1.0+16.0*x**2)
return f

In :
def BaryInterp(X,Y,x):
nx = np.size(x)
nX = np.size(X)
f  = 0*x
w  = (-1.0)**arange(0,nX)
w    = 0.5*w
w[nX-1] = 0.5*w[nX-1]
for i in range(nx):
num, den = 0.0, 0.0
for j in range(nX):
if np.abs(x[i]-X[j]) < 1.0e-15:
num = Y[j]
den = 1.0
break
else:
num += Y[j]*w[j]/((x[i]-X[j]))
den += w[j]/(x[i]-X[j])
f[i] = num/den
return f

In :
xmin, xmax = -1.0, +1.0
N = 20


Let us interpolate on Chebyshev points.

In :
X = cos(np.linspace(0.0,pi,N))
Y = fun(X)
x = np.linspace(xmin,xmax,100)
fi = BaryInterp(X,Y,x)
fe = fun(x)
plt.plot(x,fe,'b--',x,fi,'r-',X,Y,'o')
plt.legend(("True function","Interpolation","Data"),'upper right')
plt.axis([-1.0,+1.0,0.0,1.1])

Out:
[-1.0, 1.0, 0.0, 1.1]