function y = ft_preproc_slidingrange(dat, width, normalize, varargin)

% FT_PREPROC_SLIDINGRANGE computes the range of the data in a sliding time

% window of the width specified.

%

% Use as

% [dat] = ft_preproc_slidingrange(dat, width, normalize)

% where

% dat data matrix (Nchans x Ntime)

% width width of the smoothing kernel, this should be an odd number since the window needs to be centered on an individual sample

% normalize boolean, whether to normalize the range of the data with the square root of the window size (default = false)

%

% If the data contains NaNs, these are ignored for the computation, but retained in

% the output.

%

% See also PREPROC

% Copyright (C) 2012, Donders Centre for Cognitive Neuroimaging, Nijmegen, NL

%

% This file is part of FieldTrip, see http://www.fieldtriptoolbox.org

% for the documentation and details.

%

% FieldTrip 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.

%

% FieldTrip 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 FieldTrip. If not, see <http://www.gnu.org/licenses/>.

%

% $Id$

if nargin>3

% for backward compatibility, this function was first implemented as ft_preproc_slidingrange(dat, width, ...)

% with 'normalize' as an optional key-value pair, but all other PREPROC functions take fixed input arguments

normalize = varargin{1};

end

if nargin<3 || isempty(normalize)

normalize = false;

end

% preprocessing fails on channels that contain NaN

if any(isnan(dat(:)))

ft_warning('FieldTrip:dataContainsNaN', 'data contains NaN values');

end

if mod(width+1, 2)

ft_error('width should be an odd number');

end

% compute half width

h = (width-1)/2;

n = size(dat,2);

minval = zeros(size(dat));

maxval = zeros(size(dat));

for i=1:n

begsample = i-h;

endsample = i+h;

if begsample<1

begsample = 1;

end

if endsample>n

endsample=n;

end

minval(:,i) = min(dat(:,begsample:endsample),[],2);

maxval(:,i) = max(dat(:,begsample:endsample),[],2);

end

y = maxval - minval;

if istrue(normalize)

y = y ./ sqrt(width);

end