% checkcorr - Identify and remove points with bad correlation
%
% [xclean,Qa]=checkcorr(x,corr,settings);
%
% Inputs
% x = the dirty data set
% settings = the fine controls
%   settings.locorr = 65; % low correlation cutoff
%   settings.rvalue = NaN; % replacement value to use, default is NaN
%
% xclean = the fixed data set
% Qa = the feedback on what happened
%   Qa.nbad = the number of bad points
%   Qa.delmean = the ratio of the means before and after points were replaced
%   Qa.delvar = the ratio of the variance before and after points were replaced
%   Qa.stdr = standard deviation of the residuals
% 

% written by Rachel Horwitz
% rewritten for a single vector by Marinna Martini

function [xclean,Qa] = checkcorr(x,corr,settings);

% check inputs
if exist('settings', 'var'),
    if isfield(settings,'locorr'), locorr = settings.locorr; end
    if isfield(settings, 'rvalue'), rvalue = settings.rvalue; end
end
    
if exist('locorr')~=1, locorr = 65; end
if exist('rvalue')~=1, rvalue = NaN; end

Qa = [];

% make sure the shape is consistent
[r, c] = size(x);
if r == 1, x = x'; end

xclean = x;
spikes = find(corr < locorr);
if ~ischar(rvalue),
    xclean(spikes) = ones(size(spikes)).*rvalue;
elseif strcmp(rvalue, 'median'),
    xclean(spikes) = ones(size(spikes)).*gmedian(x);
else
    xclean(spikes) = ones(size(spikes)).*gmean(x);
end
Qa.nbad = length(spikes);