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pythonnumpyscipysmoothing

How to smoothen data in Python?

I am trying to smoothen a scatter plot shown below using SciPy's B-spline representation of 1-D curve. The data is available here.

enter image description here

The code I used is:

import matplotlib.pyplot as plt
import numpy as np
from scipy import interpolate

data = np.genfromtxt("spline_data.dat", delimiter = '\t')
x = 1000 / data[:, 0]
y = data[:, 1]
x_int = np.linspace(x[0], x[-1], 100)
tck = interpolate.splrep(x, y, k = 3, s = 1)
y_int = interpolate.splev(x_int, tck, der = 0)

fig = plt.figure(figsize = (5.15,5.15))
plt.subplot(111)
plt.plot(x, y, marker = 'o', linestyle='')
plt.plot(x_int, y_int, linestyle = '-', linewidth = 0.75, color='k')
plt.xlabel("X")
plt.ylabel("Y")
plt.show()

I tried changing the order of the spline and the smoothing condition, but I am not getting a smooth plot.

B-spline interpolation should be able to smoothen the data but what is wrong? Any alternate method to smoothen this data?

Solution

  • Use a larger smoothing parameter. For example, s=1000:

    tck = interpolate.splrep(x, y, k=3, s=1000)

    This produces:

    interpolation