Nothing

```
`FRWDft` <-
function(g,n,tstart,dt)
{
#####% FRWDft Fourier Transform with time shift and sample interval scaling
#####% USAGE: [G,f,t]=FRWDft(g,n,tstart,dt);
#####% Compute the Fourier Transform of g(t) correcting for time offsets and sample
###% interval. Output is scaled using conventions of continuous transforms in
###% Aki and Richards and in J.H. Karl.
###%
###% INPUT: g is a column vector time series evaluated at times specified by
###% tstart and dt. if tstart is a vector, it is the time vector and
###% dt is not necessary. if tstart is a scalar, it is the start time
###% and the sample interval is dt. n is the number of points in the FFT.
###% g is truncated or zero-padded to n points.
###%
###% OUTPUT: G is the Fourier Transform of g. it is scaled by dt to be
###% consistent with the continuous transform. the time shift
###% theorem has been used to account for time not starting at t=0.
###% the length of G is n.
###% f is the frequency vector for G.
###% t is the time vector for g.
###%
###% See also INVRFT and MAKEFREQ.
###% adapted and translated from K. Creager [email protected] 12/30/93
######## modified by J. M. Lees 10/20/2007
N=length(g) #% length of input time-domain vector
if(length(tstart)>1 )
{
#% define time vector
t=tstart;
tstart=t(1);
dt=t(2)-t(1);
}
else
{
t=tstart+seq(from=0,by=dt,to=(N-1)*dt) #% time vector
}
if(length(t)!=N)
{
### error('size of time and data vectors must be the same in FRWDFT')
}
f=makefreq(N,dt); #% construct the frequency vector
i = complex(real=0, imaginary=1)
G=dt*fft(g)*exp(-2*pi*i*tstart*f)
#% forward fourier transform with time shift
return(list(G=G, f=f, t=t))
}
```

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