I am currently doing an FFT on a very large amount of vibration data, which I am going to sample with no pattern which I want to PSD eventually
My question is in essence can I go from my data
[1, 3, 5, 10, 2, 4, 3 , 23, 24]
then sample it to
A = [0, 0, 5, 10, 0, 0, 0, 23, 24]
fft(A), will it give me the same answer as if I
B = [5, 10, 23, 24]
I think this is fine because if
A[n] = 0 it provides no contribution to the sum in the DFT but this feels very wrong and I don't know anywhere near enough about this stuff to feel confident.
I can't access data set
A because the way my data is sampled can only give me
I have very large time series data for vibrations caused by air flow over an object, I am trying to parse that data to identify what happens to the vibrations at specific wind speeds and wind directions (this is the sampling I am talking about, and why its sporadic) .
However the data comes in a strange file format (
.gcf) which I can only deal with using
obspy, and the only way I can find to sample the data (because there is so much and my computer is rubbish) is using obspy's
trim() function, which essentially deletes the rest of the not useful data. (I know this is stupid but I cant find a better way) And so this is what is reducing the dimension of the data .
And the end goal is to create a PSD plot but the problem is the fft really.