Many ways to do this depending...if have the Signal Processing TB, there's resample in it using a polyphase filter or there's a slightly less sophisticated version in current base product for the builtin time series object if you're using it.
Or, there's something as simple as interp1 for linear to spline interpolants...take your pick.
ADDENDUM Incorporate comments and sample code--dpb
OK, for your kind of problem I'd forgotten that resample is subject to serious end effects as it assumes signal is 0 on ends and yours is quite large numerically. There are ways around it, but with pretty smooth data as you have and to simply "shrink" or "stretch" it, I'd just use a spline (linear is probably almost as good as smooth as the data are, but it's really no harder with spline in Matlab) and recompute the positions needed to make a fit of 1:N points that you have for each trace match up to the M (say 46) points you want.
displ=PaulAs...; % get a shorter name for brevity
M=46; % the target number of points/trace
N=sum(isfinite(displ),2); % the number of data points in each trace
m=(N-1)./(M-1); % slope of line between 1,1 and N,M vs 1:M
b=1-m; % and intercept for the interpolant
nTr=size(displ,1); % number traces
d=zeros(nTr,M); % preallocate the output array
for i=1:nTr
d(i,:)=spline(1:N(i),displ(i,1:N(i)),polyval([b(i) m(i)],1:M));
end
The above simply does a linear transformation of the N points to M scaled to 1:M so the output of the spline fit will be the same at the two end points but there will be the same number of points (M) in each trace.
d will thus be array of nTr by M points with each trace having 46 points and having same values at end points as do the original traces at beginning and last finite point of each.
The above can be more fully vectorized although I'm not sure it would be any faster.
