Spline Interpolation with Python

Question

I wrote the following code to perform a spline interpolation:

import numpy as np
import scipy as sp

x1 = [1., 0.88,  0.67,  0.50,  0.35,  0.27, 0.18,  0.11,         


        

Answer 1

From the scipy documentation on scipy.interpolate.interp1d:

scipy.interpolate.interp1d(x, y, kind='linear', axis=-1, copy=True, bounds_error=True, fill_value=np.nan)

x : array_like. A 1-D array of monotonically increasing real values.

...

The problem is that the x values are not monotonically increasing. In fact they are monotonically decreasing. Let me know if this works and if its still the computation you are looking for.:

import numpy as np
import scipy as sp
from scipy.interpolate import interp1d

x1 = sorted([1., 0.88, 0.67, 0.50, 0.35, 0.27, 0.18, 0.11, 0.08, 0.04, 0.04, 0.02])
y1 = [0., 13.99, 27.99, 41.98, 55.98, 69.97, 83.97, 97.97, 111.96, 125.96, 139.95, 153.95]

new_length = 25
new_x = np.linspace(x.min(), x.max(), new_length)
new_y = sp.interpolate.interp1d(x, y, kind='cubic')(new_x)