5.11.3. Exercise: Indexing/slicing/filtering unidimensional arrays 3

You recorded a noisy signal:

signal = np.array(
    [
        0.436, 0.493, 0.467, 0.467, 0.482, 0.497, 0.474, 0.493, 0.474, 0.467,
        0.470, 0.455, 0.482, 0.501, 0.470, 0.482, 0.467, 0.467, 0.558, 0.413,
        0.463, 0.444, 0.463, 0.417, 0.528, 0.455, 0.486, 0.459, 0.490, 0.459,
    ]
)

You want to smooth this signal using a moving average with a window of three samples. This means that you want to create a new array where each value is the average of the three neighbour values of the raw signal, as illustrated in Fig. 5.10.

Fig. 5.10 Filtering using a moving average.

Using only one line, apply such a filter on the raw, noisy signal. Then, plot both the raw and filtered signal on the same figure to verify your result.

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import numpy as np
import matplotlib.pyplot as plt


filtered = (signal[0:-2] + signal[1:-1] + signal[2:]) / 3

plt.plot(signal, label="raw")
plt.plot(filtered, label="filtered")
plt.legend();