I have access to NumPy and SciPy and want to create a simple FFT of a data set. I have two lists, one that is y values and the other is timestamps for those y values.

What is the simplest way to feed these lists into a SciPy or NumPy method and plot the resulting FFT?

I have looked up examples, but they all rely on creating a set of fake data with some certain number of data points, and frequency, etc. and don't really show how to do it with just a set of data and the corresponding timestamps.

I have tried the following example:

from scipy.fftpack import fft

# Number of samplepoints
N = 600

# Sample spacing
T = 1.0 / 800.0
x = np.linspace(0.0, N*T, N)
y = np.sin(50.0 * 2.0*np.pi*x) + 0.5*np.sin(80.0 * 2.0*np.pi*x)
yf = fft(y)
xf = np.linspace(0.0, 1.0/(2.0*T), N/2)
import matplotlib.pyplot as plt
plt.plot(xf, 2.0/N * np.abs(yf[0:N/2]))
plt.grid()
plt.show()

But when I change the argument of fft to my data set and plot it, I get extremely odd results, and it appears the scaling for the frequency may be off. I am unsure.

Here is a pastebin of the data I am attempting to FFT

http://pastebin.com/0WhjjMkb http://pastebin.com/ksM4FvZS

When I use fft() on the whole thing it just has a huge spike at zero and nothing else.

Here is my code:

## Perform FFT with SciPy
signalFFT = fft(yInterp)

## Get power spectral density
signalPSD = np.abs(signalFFT) ** 2

## Get frequencies corresponding to signal PSD
fftFreq = fftfreq(len(signalPSD), spacing)

## Get positive half of frequencies
i = fftfreq>0

##
plt.figurefigsize = (8, 4));
plt.plot(fftFreq[i], 10*np.log10(signalPSD[i]));
#plt.xlim(0, 100);
plt.xlabel('Frequency [Hz]');
plt.ylabel('PSD [dB]')

Spacing is just equal to xInterp[1]-xInterp[0].