# fft

Fast Fourier transform (FFT) of `iddata`

object

## Description

uses the fast Fourier transform (FFT) algorithm to transform the time-domain `datf`

= fft(`data`

)`iddata`

object data to the frequency
domain `iddata`

object `datf`

.
`data`

contains real-valued signals and has constant sample
time `Ts`

, and `datf`

has frequency values equally
distributed from 0 to the Nyquist frequency. The Nyquist frequency is included if
the signal length is even and not included if the length is odd. To preserve the
signal power and noise level, FFTs are normalized by dividing each transform by the
square root of the signal length.

specifies the transformation length `datf`

= fft(`data`

,`N`

)`N`

. In the default case, the
length of the transformation is determined by the signal length. Including
`N`

forces the FFT transformations to pad with zeros if the
signals in `data`

are shorter than `N`

and
truncate the signals otherwise. Thus, the number of frequencies in the real signal
case is `(N/2)+1`

when `N`

is even or
`(N+1)/2`

when `N`

is odd.
If
`data`

contains several experiments, `N`

can be a row vector of corresponding length.

## Examples

## Input Arguments

## Output Arguments

## Tips

`fft`

does not produce the same answer as the base MATLAB^{®}
`fft`

function.

For real signals, by default,

`fft`

computes the FFT for only the positive portion of frequency range. MATLAB`fft`

computes the FFT for the entire frequency range.`fft`

scales the result by`1/sqrt(N)`

, where`N`

is the data length. MATLAB`fft`

does not apply scaling.

## Version History

**Introduced in R2007a**