New answers tagged ifft
1
Removing the 2nd term -> array = np.asarray(array, dtype=float) should work
2
Without having the original data it is difficult to confirm, but the DC offset is due to bin 0 in the DFT. Bin 0 corresponds to the DC term or average offset value for the sequence. The plot in the DFT result shows a large value for bin 0 while the time domain waveform appears to be closer to 0 average. I would need to see the original data to determine how ...
4
Because your data is (I assume) composed of some interesting stuff times a teeny number, plus the -- presumably uninteresting -- $k_0 + N\,k_1 + N^2\,k_2$, where $N$ is your "epoch".
So the Fourier transform of the data as a whole is dominated by the Fourier transform of $k_0 + N\,k_1 + N^2\,k_2$.
7
[EDITED FROM DISCUSSION]
On the first order, your data looks like a decay with a positive origin on a small-valued range $[0.7 \; 0.49]\times 10^{-7}$, and very tiny fluctuations with respect to the area under the curve.
So from afar, your data is much closer to an almost constant function than to some putative oscillations. So the the zero, or DC-...
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