Time for action – filtering a detrended signal
We learned in the previous Time for action section how to detrend a signal. This detrended signal could have a cyclical component. Let's try to visualize this. Some of the steps are a repetition of steps in the previous Time for action section, such as downloading the data and setting up matplotlib
objects. These steps are omitted here.
- Apply the Fourier transform, giving us the frequency spectrum:
amps = np.abs(fftpack.fftshift(fftpack.rfft(y)))
- Filter out the noise. Let's say, if the magnitude of a frequency component is below
10
percent of the strongest component, throw it out:amps[amps < 0.1 * amps.max()] = 0
- Transform the filtered signal back to the original domain and plot it together with the detrended ...
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