Time for action – interpolating in one dimension

We will create data points using a sinc function and add some random noise to them. After that, we will do a linear and cubic interpolation, and plot the results. Perform the following steps to do so:

1. Create the data points and add noise to them.

   x = np.linspace(-18, 18, 36)
   noise = 0.1 * np.random.random(len(x))
   signal = np.sinc(x) + noise

2. 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)

3. Do the same as in the previous step, but with cubic interpolation.

   cubic = interpolate.interp1d(x, signal, kind=”cubic”)
   y2 = cubic(x2)

4. 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 screenshot is a plot of the data, linear, and cubic interpolation:

   

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