Just as the sampled time data represents the value of a signal at discrete points in time, the result of a (forward) Fast Fourier Transform represents the spectrum of the signal at discrete frequencies. These discrete frequencies are a function of the frequency index (m), the number of samples collected (N), and the sampling interval (d):
f(m) = m / (N*delt)
The frequencies for which the FFT of a sampled signal are defined are sometimes called frequency bins, which refers to the histogram-like nature of a discrete spectrum. The width of each frequency bin is 1/(N * d).
Due to the complex exponential in the definition of the DFT, the spectrum has a cyclic dependence on the frequency index m. That is:
v(m+p*N) = v(m)
for p = any integer.
The frequency spectrum computed by IDL's FFT function for a one-dimensional time sequence is stored in a vector with indices running from 0 to N-1, which is also a valid range for the frequency index m. However, the frequencies associated with frequency indices greater than N/2 are above the Nyquist frequency and are not physically meaningful for sampled signals. Many textbooks choose to define the range of the frequency index m to be from - (N/2 - 1) to N/2 so that it is (nearly) centered around zero. From the cyclic relation above with p = -1:
v(- (N/2 - 1)) = v(N/2 + 1 - N) = v(N/2 + 1)
v(- (N/2 - 2)) = v(N/2 + 2 - N) = v(N/2 + 2)
v(-2) = v(N - 2 - N) = v(N - 2)
v(-1) = v(N - 1 - N) = v(N - 1)
This index shift is easily accomplished in IDL with the SHIFT function. See Real and Imaginary Components for an example.