Transform iddata object to frequency domain
data
Datf = fft(Data)
Datf = fft(Data,N)
Datf = fft(Data,N,'complex')
Datf = fft(Data) transforms time-domain data to frequency domain
data. If Data is a time-domain iddata object with
real-valued signals and with constant sample time Ts,
Datf is returned as a frequency-domain iddata
object with the frequency values equally distributed from frequency 0 to the Nyquist
frequency. Whether the Nyquist frequency actually is included or not depends on the
signal length (even or odd). Note that the FFTs are normalized by dividing each
transform by the square root of the signal length. That is in order to preserve the
signal power and noise level.
Datf = fft(Data,N) specifies the transformation length. In the
default case, the length of the transformation is determined by the signal length. A
second argument N will force FFT transformations of length
N, padding with zeros if the signals in Data
are shorter and truncating otherwise. Thus the number of frequencies in the real signal
case will be (N/2)+1 or (N+1)/2.
If Data
contains several experiments, N can be a row vector of corresponding
length.
Datf = fft(Data,N,'complex') specifies to include negative
frequencies. For real signals, the default is that Datf only contains
nonnegative frequencies. For complex-valued signals, negative frequencies are also
included. To enforce negative frequencies in the real case, add a last argument,
'Complex'.