Documentation of lin_remove_NaN

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Function Synopsis

y = lin_remove_NaN(xdat, xtim, tol, show);

Help text

  function Y = lin_remove_NaN ( Xdat , Xtim , tol , show ) ;

   Y = lin_remove(Xdat, Xtim) removes the best linear fit of Xtim to
   each column of Xdat.  If Xdat is N-dimensional, then it is
   assumed that the time series Xtim will be removed from the first
   dimension of Xdat.

   This function ignores elements of Xdat and Xtim that have NaNs.

   tol is the number of common points required between xdat(:,i) and
   _every_ column of xtim, in order for xtim to be removed from xdat.
   So, y will only have (max) as many rows as the least amount of data
   in any column of xtim.

   show = 'show', 'noshow', or 1, 0, respectively.

   This routine is quicker than lin_remove_NaN_defunct, and seems to do
   the same thing.

Listing of function lin_remove_NaN

function y = lin_remove_NaN(xdat, xtim, tol, show);

sz = size(xdat); ndim = length(sz);
if (ndim == 2) & (sz(1) == 1); xdat = xdat(:); end;
sz = size(xdat); ndim = length(sz);

if nargin < 3; tol = 2; end;

if nargin < 4; show = 0; end;
if isstr(show);
  show = strcmp(show, 'show');
end

if (size(xdat, 1) ~= size(xtim, 1));
  error('The number of columns of xdat and xtim must be equal');
end

[m1, n1] = size(xdat);
[m2, n2] = size(xtim);

%  Condense data to get rid of NaN's in Xtim
if n2 > 1;
  kp_time = find(~isnan(sum(xtim'))); 
else
  kp_time = find(~isnan(xtim));
end
nkp_time = length(kp_time);
xdat = xdat(kp_time,:); xtim = xtim(kp_time,:);

%  Start regressions and removals
if show; disp(['Number of iterations:  ' num2str(n1)]); end;

c = repmat(NaN, [nkp_time, n1]);
for i = 1:n1;
  if show; 
    disp(['Iteration ' num2str(i) ' of ' num2str(n1)]); 
  end;
  if n2 > 1;
    kp = find(sum( ~isnan((xdat(:,i)*ones(1,n2)) .* xtim)') == n2 );
  else
    kp = find(~isnan(xdat(:,i) .* xtim));
  end
  nkp = length(kp);
  if nkp > tol
    c(kp,i) = xdat(kp,i) - xtim(kp,:)*(xtim(kp,:)\xdat(kp,i));
  end
end

%  Reshape output so it is the same dimension as input

y = repmat(NaN, [m1, n1]);
y(kp_time,:) = c;

if ndim > 2;
  y = reshape(y, sz);
end