How to make scipy.interpolate give an extrapolated result beyond the input range?
You can take a look at InterpolatedUnivariateSpline
Here an example using it:
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
# given values
xi = np.array([0.2, 0.5, 0.7, 0.9])
yi = np.array([0.3, -0.1, 0.2, 0.1])
# positions to inter/extrapolate
x = np.linspace(0, 1, 50)
# spline order: 1 linear, 2 quadratic, 3 cubic ...
order = 1
# do inter/extrapolation
s = InterpolatedUnivariateSpline(xi, yi, k=order)
y = s(x)
# example showing the interpolation for linear, quadratic and cubic interpolation
plt.figure()
plt.plot(xi, yi)
for order in range(1, 4):
s = InterpolatedUnivariateSpline(xi, yi, k=order)
y = s(x)
plt.plot(x, y)
plt.show()
As of SciPy version 0.17.0, there is a new option for scipy.interpolate.interp1d that allows extrapolation. Simply set fill_value='extrapolate' in the call. Modifying your code in this way gives:
import numpy as np
from scipy import interpolate
x = np.arange(0,10)
y = np.exp(-x/3.0)
f = interpolate.interp1d(x, y, fill_value='extrapolate')
print f(9)
print f(11)
and the output is:
0.0497870683679
0.010394302658