Scipy UnivariateSpline exit code -1073741819 for some case

I use UnivariateSpline from scipy module to fit data.It works for almost all cases except for this one, which gives rise to Process finished with exit code -1073741819 (0xC0000005) error. If I change smoothing factor s to 0, it also works. Any suggestions to solve this problem will help.

Update1

My working environment is:

• python 3.7
• scipy 1.3.2
• numpy 1.17.4

import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import UnivariateSpline, InterpolatedUnivariateSpline

x = np.arange(78)
y = np.asarray([
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         5.03989319, 4.03191455, 4.03191455, 3.02393591,
 3.02393591, 2.01595727, 2.01595727, 1.00797864, 0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.,
 0.,         0.,         0.,         0.,         0.,         0.])

spl = UnivariateSpline(x, y, k=1, s=0.01)

knots = list(map(int, spl.get_knots()))
plt.plot(knots, y[knots], 'rx')
plt.plot(knots, y[knots], 'r-')
plt.plot(x, y, 'b-')
plt.show()

The combination of you s and k parameter are causing the issue.

According to the documentation, the number of knots increases until the condition sum((w[i] * (y[i]-spl(x[i])))**2, axis=0) <= s is met. However, because you have a limited number of non-zero data points, you can only add so many meaningful knots to the data set, and because you are doing k=1 spline (as opposed to cubic for example), the difference between the spline value and the data values is never reaching the prescribed s value.

Your options include increasing k (I tested with k=3 and it worked) or increase the s value to have a less strict condition (anything above s=0.08 worked for me). Note your code worked when s=0 because for that condition, instead of doing a smoothing, the algorithm just interpolates between each point and does no smoothing (which maybe is what you want).