how to vectorize this for loop

[mrows,ncols]  = size(spec);
if (mrows==1) mrows=ncols; ncols=1; spec = spec(:);   end

% ---- edit out NaNs and Infs ---------------------------------------

good = isfinite(spec); 
rmin = min(spec(good)) - 1; 
bad  = find(~isfinite(spec));
if (~isempty(bad)) 
   spec(bad) = ones(size(bad)) * rmin; 
end 
[mrows,ncols]  = size(spec);
if (mrows==1) mrows=ncols; ncols=1; spec = spec(:);   end

% ---- edit out NaNs and Infs ---------------------------------------

good = isfinite(spec); 
rmin = min(spec(good)) - 1; 
bad  = find(~isfinite(spec));
if (~isempty(bad)) 
   spec(bad) = ones(size(bad)) * rmin; 
end 
[mrows,ncols]  = size(spec);
if (mrows==1) mrows=ncols; ncols=1; spec = spec(:);   end

% ---- edit out NaNs and Infs ---------------------------------------

good = isfinite(spec); 
rmin = min(spec(good)) - 1; 
bad  = find(~isfinite(spec));
if (~isempty(bad)) 
   spec(bad) = ones(size(bad)) * rmin; 
end  

   val =  ones(npicks,ncols) * NaN ; 
    loc =  zeros(npicks,ncols) ; 

    for k=1:ncols
                                               % Find all local peaks: 
        dx = diff([rmin; spec(:,k); rmin]);    % for a local peak at either end 
        lp = find(dx(1:mrows)   >= 0 ... 
                & dx(2:mrows+1) <=0);          % peak locations 
        vp = spec(lp,k);                       % peak values

        for p=1:npicks
           [v,l] = max(vp);                   % find current maximum
           val(p,k) = v;  loc(p,k) = lp(l);   % save value and location 

           ind = find(abs(lp(l)-lp) > rdiff);  % find peaks which are far away

           if (isempty(ind)) 
               break                           % no more local peaks to pick
           end 
           vp  = vp(ind);          % shrink peak value array
           lp  = lp(ind);                      % shrink peak location array 
        end
    end

[Jan]

I assume, that you do not want to vectorize the code, but to accelerate it. Vectorized code is not necessarily faster. But what should be the purpose of suggesting slower code?!

Optimizing code without the possibility to run the code, is based on guessing. Please provide some relevant input data, e.g. produced by rand with a typical dimension. It matters, if the inputs are huge (some GB) and you run this function once only, or if you want to process billions of small arrays.