## Copyright (C) 2001 Paul Kienzle <pkienzle@users.sf.net>
##
## This program is free software; you can redistribute it and/or modify it under
## the terms of the GNU General Public License as published by the Free Software
## Foundation; either version 3 of the License, or (at your option) any later
## version.
##
## This program is distributed in the hope that it will be useful, but WITHOUT
## ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or
## FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
## details.
##
## You should have received a copy of the GNU General Public License along with
## this program; if not, see <http://www.gnu.org/licenses/>.

## -*- texinfo -*-
## @deftypefn {Function File} {@var{v} =} nanstd (@var{X})
## @deftypefnx{Function File} {@var{v} =} nanstd (@var{X}, @var{opt})
## @deftypefnx{Function File} {@var{v} =} nanstd (@var{X}, @var{opt}, @var{dim})
## Compute the standard deviation while ignoring NaN values.
##
## @code{nanstd} is identical to the @code{std} function except that NaN values are
## ignored.  If all values are NaN, the standard deviation is returned as NaN.
## If there is only a single non-NaN value, the deviation is returned as 0. 
##
## The argument @var{opt} determines the type of normalization to use. Valid values
## are
##
## @table @asis 
## @item 0:
##   normalizes with @math{N-1}, provides the square root of best unbiased estimator of 
##   the variance [default]
## @item 1:
##   normalizes with @math{N}, this provides the square root of the second moment around 
##   the mean
## @end table
##
## The third argument @var{dim} determines the dimension along which the standard
## deviation is calculated.
##
## @seealso{std, nanmin, nanmax, nansum, nanmedian, nanmean}
## @end deftypefn

function v = nanstd (X, opt, varargin)
  if nargin < 1
    print_usage;
  else
    if nargin < 3
      dim = min(find(size(X)>1));
      if isempty(dim), dim=1; endif;
    else
      dim = varargin{1};
    endif
    if ((nargin < 2) || isempty(opt))
      opt = 0;
    endif

    ## determine the number of non-missing points in each data set
    n = sum (!isnan(X), varargin{:});
    
    ## replace missing data with zero and compute the mean
    X(isnan(X)) = 0;
    meanX = sum (X, varargin{:}) ./ n;
    
    ## subtract the mean from the data and compute the sum squared
    sz = ones(1,length(size(X)));
    sz(dim) = size(X,dim);
    v = sumsq (X - repmat(meanX,sz), varargin{:});
    
    ## because the missing data was set to zero each missing data
    ## point will contribute (-meanX)^2 to sumsq, so remove these
    v = v - (meanX .^ 2) .* (size(X,dim) - n);
    
    if (opt == 0)
      ## compute the standard deviation from the corrected sumsq using
      ## max(n-1,1) in the denominator so that the std for a single point is 0
      v = sqrt ( v ./ max(n - 1, 1) );
    elseif (opt == 1)
      ## compute the standard deviation from the corrected sumsq
      v = sqrt ( v ./ n );
    else
      error ("std: unrecognized normalization type");
    endif
    
    ## make sure that we return a real number
    v = real (v);
  endif
endfunction
