Filtering of data curves
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## Filtering of data curves

In this section, it will be assumed that you have the following data curve:

This signal has a power spectrum with high and low frequencies. We can analyze this by doing a FFT on the data curve, this leads to the following figure:

The newt sections will show the influence of the different filters on this data curve.

### FFT low pass filter

This filter allows to cut the high frequencies of a signal. You just have to select the cut-off frequency of the filter. Let us assume that we want to keep the frequencies below 1 Hz, we will obtain:

Figure 6.10. Signal after a FFT low pass filter

The power spectrum of this new signal shows that the frequencies below 1 Hz have been kept.

### FFT high pass filter

This filter allows to cut the low frequencies of a signal. You just have to select the cut-off frequency of the filter. Let us assume that we want to keep the frequencies above 1 Hz, we will obtain:

Figure 6.11. Signal after a FFT high pass filter

The power spectrum of this new signal shows that the frequencies above 1 Hz have been kept.

### FFT band pass filter

This filter allows to cut the low and high frequencies of a signal. You just have to select the high and low cut-off frequencies of the filter. Let us assume that we want to keep the frequencies between 1.5 and 3.5 Hz, we will obtain:

Figure 6.12. Signal after a FFT band pass filter

The power spectrum of this new signal shows that only the frequencies at 2 and 3 Hz have been kept.

### FFT block band filter

This filter allows to keep the low and high frequencies of a signal. You just have to select the high and low cut-off frequencies of the filter. Let us assume that we want to remove the frequencies between 1.5 and 3.5 Hz, we will obtain:

Figure 6.13. Signal after a FFT block band filter

The power spectrum of this new signal shows that only the frequencies below 1.5 Hz and above 3.5 Hz have been kept.

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