Am I supposed to normalize FFT in Python?

1. How do you normalize FFT?
2. What are the limitations of FFT?
3. How accurate is FFT?

How do you normalize FFT?

Normalise the fft by dividing it by the length of the original signal in the time domain. Zero values within the signal are considered to be part of the signal, so 'non-zero samples' is inappropriate. The length to use to normalise the signal is the length before adding zero-padding.

What are the limitations of FFT?

A disadvantage associated with the FFT is the restricted range of waveform data that can be transformed and the need to apply a window weighting function (to be defined) to the waveform to compensate for spectral leakage (also to be defined). An alternative to the FFT is the discrete Fourier transform (DFT).

How accurate is FFT?

Discrete Fourier transforms computed through the FFT are far more accurate than slow transforms, and convolutions computed via FFT are far more accurate than the direct results. However, these results depend critically on the accuracy of the FFT software employed, which should generally be considered suspect.