We will create data points using a
sinc() function and add some random noise to it. After this, we will do a linear and cubic interpolation and plot the results.
Create the data points and add noise to it:
x = np.linspace(-18, 18, 36) noise = 0.1 * np.random.random(len(x)) signal = np.sinc(x) + noise
Create a linear interpolation function and apply it to an input array with five times as many data points:
interpreted = interpolate.interp1d(x, signal) x2 = np.linspace(-18, 18, 180) y = interpreted(x2)
Do the same as in the previous step, but with cubic interpolation:
cubic = interpolate.interp1d(x, signal, kind="cubic") y2 = cubic(x2)
Plot the results with
matplotlib:plt.plot(x, signal, 'o', label="data") plt.plot(x2, y, '-', label="linear") plt.plot(x2, y2, '-', lw=2, label="cubic") plt.legend() plt.show()
The following diagram is a plot of the data, linear, and cubic interpolation:
