Fitting data using UnivariateSpline in scipy python

curve-fitting, numpy, python, scipy

Solution

There are a few issues.

The first issue is the order of the x values. From the documentation for `scipy.interpolate.UnivariateSpline` we find

x : (N,) array_like
    1-D array of independent input data. MUST BE INCREASING.

Stress added by me. For the data you have given the x is in the reversed order. To debug this it is useful to use a "normal" spline to make sure everything makes sense.

The second issue, and the one more directly relevant for your issue, relates to the s parameter. What does it do? Again from the documentation we find

s : float or None, optional
    Positive smoothing factor used to choose the number of knots.  Number
    of knots will be increased until the smoothing condition is satisfied:

    sum((w[i]*(y[i]-s(x[i])))**2,axis=0) <= s

    If None (default), s=len(w) which should be a good value if 1/w[i] is
    an estimate of the standard deviation of y[i].  If 0, spline will
    interpolate through all data points.

So s determines how close the interpolated curve must come to the data points, in the least squares sense. If we set the value very large then the spline does not need to come near the data points.

As a complete example consider the following

import scipy.interpolate as inter
import numpy as np
import pylab as plt

x = np.array([13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1])
y = np.array([2.404070, 1.588134, 1.760112, 1.771360, 1.860087,
          1.955789, 1.910408, 1.655911, 1.778952, 2.624719,
          1.698099, 3.022607, 3.303135])
xx = np.arange(1,13.01,0.1)
s1 = inter.InterpolatedUnivariateSpline (x, y)
s1rev = inter.InterpolatedUnivariateSpline (x[::-1], y[::-1])
# Use a smallish value for s
s2 = inter.UnivariateSpline (x[::-1], y[::-1], s=0.1)
s2crazy = inter.UnivariateSpline (x[::-1], y[::-1], s=5e8)
plt.plot (x, y, 'bo', label='Data')
plt.plot (xx, s1(xx), 'k-', label='Spline, wrong order')
plt.plot (xx, s1rev(xx), 'k--', label='Spline, correct order')
plt.plot (xx, s2(xx), 'r-', label='Spline, fit')
# Uncomment to get the poor fit.
#plt.plot (xx, s2crazy(xx), 'r--', label='Spline, fit, s=5e8')
plt.minorticks_on()
plt.legend()
plt.xlabel('x')
plt.ylabel('y')
plt.show()

Problem

I have a experimental data to which I am trying to fit a curve using UnivariateSpline function in scipy. The data looks like: ``` x y 13 2.404070 12 1.588134 11 1.760112 10 1.771360 09 1.860087 08 1.955789 07 1.910408 06 1.655911 05 1.778952 04 2.624719 03 1.698099 02 3.022607 01 3.303135 ``` Here is what I am doing: ``` import matplotlib.pyplot as plt from scipy import interpolate yinterp = interpolate.UnivariateSpline(x, y, s = 5e8)(x) plt.plot(x, y, 'bo', label = 'Original') plt.plot(x, yinterp, 'r', label = 'Interpolated') plt.show() ``` That's how it looks: I was wondering if anyone has thought on other curve fitting options which scipy might have? I am relatively new to scipy. Thanks!

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