FFT on continuous fifo data - Is y axis magnitude sum of all samples? I'm running an fft once a second on a buffer of data 60 seconds long. The data is sampled at 558Hz and is placed into the fifo buffer once per second. FFT is numpy.rfft. The data is scaled to psia prior to fft. From the research I have done so far (found several previous questions on this) the unit of the fft magnitude will also be in psia. But I am unsure of how I should interpret this magnitude. Is it a sum of the entire sample? Something else? My intuition says it is a sum of the entire sample. Where if I had say a peak at 25Hz of 30psia on the 60 second buffer, if I wanted to know the peak over a 1 second sample I would divide by 60. My sensor data is only varying by about 2-3 psi when plotted in time domain so that got me wondering how the fft y-data could be orders of magnitude larger than the pk-pk difference of input signal. Or maybe I'm thinking about this totally wrong, I am pretty ingorant on this : )
In general the  output of a Fourier Transform is &quot;spectral density&quot;. For a continuous Fourier Transform that's implicit: if the signal in Volts, the unit of the FT are Volts/Hz. For the discrete case, the units are the same but the frequency interval is implicit: it's the sample rate divided by the FFT length.
If you want to extract &quot;physical&quot; meaning from the FFT, you need to watch your scaling. In your specific case it would probably be best to scale both the forward and backward FFT by $1/\sqrt{N}$ since that maintains Parseval's theorem, i.e.
$$\sum |x[n]|^2 = \sum |X[k]|^2$$
which means that total power in the time and frequency domain are the same.