% BDT Filters
%   This file contains all of the filters for BDT.  Filters have a
%   predefined format which must be followed in order to properly function
%   in BDT.  
%
%   1) Filters are separated by empty lines.
%
%   2) The first line of a filter contains what will be shown in the
%   filters list.  The first two characters of the name line must be %- so
%   that they will be recognized.
%
%   3) The remaining lines are the commands to be executed. Lines with %% 
%   as the first two characters will be executed when the filter is added
%   while lines which begin with '% ' minus the quotes will be executed
%   when the graphs are plotted.  Try to keep anything with user input
%   immediately after adding the filter.
%
%   4) Any data you want input immediately after adding the filter and then
%   used during plotting must be stored in the Filt.Filters structure.  It
%   is important to note that Filt.Filters.String and Filt.Filters.Eval
%   must not be used as they are already used by BDT.
%
%   5) XData and YData are both numeric arrays. The XData holds the data
%   corresponding to the x-axis on each plot 
%
%   6) Functions for graphical user input include:
%       inputdlg, logindlg, listdlg, questdlg, and other which can be found
%       in Matlab help.  logindlg has been made by the creator of bdt2 to
%       hide passwords behind astrixes so it will not have a document entry
%       in Matlab help.  For logindlg help type 'help logindlg' in the
%       command window.
%
%   7) Data available when the filter is added includes everything in the
%   Filt structure.  XData, YData, and Filt are available when the graphs
%   are plotted.  The data is un-normalized before filters are applied.
%
%   8)  Filt field definitions (not all of these fields are guaranteed to be there):
%           Filt.DB - Database the data came from
%           Filt.Table - Table the data came from
%           Filt.XAxis - Column the x data came from
%           Filt.YAxis - Column the y data came from
%           Filt.Norm - Normalization factor for the data
%           Filt.Indices - Indices of currently selected points
%           Filt.SHandle - Handle of the selected points plot
%           Filt.Windows - Contains more fields with the handles of Data windows
%           Filt.Filters - Storage area for fields containing filter information
%
% Last Edit:  7/30/2007

%-X Lower Range
%%xl = inputdlg({'X'},'Lower Range',1);
%%Filt.Filters.XLRange = str2num(xl{1});
% XData(XData < Filt.Filters.XLRange) = NaN;

%-X Upper Range
%%xu = inputdlg({'X'},'Upper Range',1);
%%Filt.Filters.XURange = str2num(xu{1});
% XData(XData > Filt.Filters.XURange) = NaN;

%-Y Lower Range
%%yl = inputdlg({'Y'},'Lower Range',1);
%%Filt.Filters.YLRange = str2num(yl{1});
% YData(YData < Filt.Filters.YLRange) = NaN;

%-Y Upper Range
%%yu = inputdlg({'Y'},'Upper Range',1);
%%Filt.Filters.YURange = str2num(yu{1});
% YData(YData > Filt.Filters.YURange) = NaN;

%-Bias
%%Filt.Filters.Bias = inputdlg('Value to Subtract','Bias',1);
% YData = YData - str2num(Filt.Filters.Bias{1});

%-Curve Fit
%%[Filt.Filters.fr_function_fit.Options Filt.Filters.fr_function_fit.Input Filt.Filters.fr_function_fit.Obs Filt.Filters.fr_function_fit.Func Filt.Filters.fr_function_fit.Init] = curve_fit();
% curve_fit_exec(Filt,XData,YData);

%-Least Squares Fit, Geometric
% XData(isnan(YData)) = [];
% YData(isnan(YData)) = [];
% YData(isnan(XData)) = [];
% XData(isnan(XData)) = [];
% [m b c sdevm sdevb] = lsqfitgm(XData,YData);
% xtemp = XData;
% ytemp = YData;
% xtemp(isnan(ytemp)) = [];
% ytemp(isnan(ytemp)) = [];
% ytemp(isnan(xtemp)) = [];
% xtemp(isnan(xtemp)) = [];
% PData = get(p,'UserData');
% Color = get(p,'Color');
% Color = abs(Color-.3);
% if ~~isfield(PData,'XAxis');PData.XAxis = [PData.XAxis '_fit'];else;PData.XAxis = '';end
% if ~~isfield(PData,'YAxis');PData.YAxis = [PData.YAxis '_fit'];else;PData.YAxis = '';end
% PData.Norm = 1;
% PData.Indices = [];
% PData.SHandle = [];
% PData.Login = '';
% PData.Pass = '';
% PData.Comments = {['Fit Type = Least Squares, Geometric'];['Slope = ' num2str(m)];['Slope Standard Deviation = ' num2str(sdevm)];['Slope Intercept = ' num2str(b)];['Slope Intercept Standard Deviation = ' num2str(sdevb)];['Correlation Coefficient = ' num2str(c)];'Note:  NaN''s have been ignored without respect to the NaN''s in the corresponding data set.'};
% NextP = get(Ax,'NextPlot');
% hold(Ax,'on')
% p2 = plot(Ax,[min(xtemp) max(xtemp)],[min(xtemp)*m+b max(xtemp)*m+b],'Color',Color);
% set(Ax,'NextPlot',NextP)
% PData.PastHandles = p2;
% PData.Filters.String = [];
% PData.Filters.Eval = [];
% set(p2,'UserData',PData);
% cm_PlotInfo(get(Ax,'Parent'),[],p2)

%-Polynomial
%%temp = inputdlg({'N'},'Polynomial Degree',1);
%%if ~isempty(temp);Filt.Filters.PolyDegree = str2num(temp{1});else;Filt.Filters.PolyDegree = [];end
% NextP = get(Ax,'NextPlot');
% hold(Ax,'on')
% Color = get(p,'Color');
% Color = abs(Color-.3);
% PData = get(p,'UserData');
% XData2 = XData;
% YData2 = YData;
% XData2(isnan(YData2)) = [];
% YData2(isnan(YData2)) = [];
% YData2(isnan(XData2)) = [];
% XData2(isnan(XData2)) = [];
% P = polyfit(XData2,YData2,Filt.Filters.PolyDegree);
% YData2 = polyval(P,XData);
% p2 = plot(Ax,XData,YData2,'Color',Color,'LineWidth',2);
% if ~~isfield(PData,'Comments');PData.Comments = [PData.Comments;{' ';'Polyfit Coefficients:'}];else;PData.Comments = {' ';'Polyfit Coefficients:'};end
% PData.YAxis = [PData.YAxis ' Polyfit'];
% for i=1:1:length(P);PData.Comments = [PData.Comments;num2str(P(i))];end
% set(p2,'UserData',PData)
% set(Ax,'NextPlot',NextP)
% legend_update(Ax)