ca_decodeshitPos.m

function f = ca_decodeshitPos(pos, clusters, tdecode, dim)
%decodes position and outputs decoded x, y, confidence(in percents), and time.
%dim is bin sie in cm
%tdecode is decoding in seconds
%auto detects of REM based on time input


velthreshold = 2;

%pos(:,3) = 0;


if isa(clusters,'double')==1
  for k = 1:size(clusters,1)
    name = num2words(k);
    for z = 1:length(name)
      if strfind(name(z),'-')
        name(z) = ' ';
      end
    end
    name = name(find(~isspace(name)));
    curclus = clusters(k,:);
    curclus = curclus(find(~isnan(curclus)));
    cluststruct.(name) = curclus;
  end
  clusters = cluststruct;
end

tic


posData = pos;
%posData = fixpos(posData);


timee = pos(:,1);


[cc indexmin] = min(abs(posData(1,1)-timee));
[cc indexmax] = min(abs(posData(end,1)-timee));
decodetimevector = timee(indexmin:indexmax);
if length(decodetimevector)<10
  timevector = timee;
else
  timevector = decodetimevector;
end




vel = ca_velocity(posData);
vel(1,:) = smoothdata(vel(1,:), 'gaussian', 15); %originally had this at 30, trying with 15 now
goodvel = find(vel>=velthreshold);

tdecodesec = tdecode;
t = round(30*tdecode);


%find number of clusters
clustname = (fieldnames(clusters));
numclust = length(clustname);

%BIN
psize = 1.000 * dim;

xvals = posData(:,2);
yvals = posData(:,3);
xmin = min(posData(:,2));
ymin = min(posData(:,3));
xmax = max(posData(:,2));
ymax = max(posData(:,3));
xbins = ceil((xmax-xmin)/psize); %number of x
ybins = ceil((ymax-ymin)/psize); %number of y
if ybins ==0
  ybins = 1;
end


xinc = xmin +(0:xbins)*psize; %makes a vectors of all the x values at each increment
yinc = ymin +(0:ybins)*psize; %makes a vector of all the y values at each increment



% for each cluster,find the firing rate at esch velocity range
fxmatrix = ca_firingPerPos(posData, clusters, dim, tdecodesec, 7.5, velthreshold);
names = (fieldnames(fxmatrix));
for k=1:length(names)
  curname = char(names(k));
  now = fxmatrix.(curname);
  if size(fxmatrix.(curname),2)>1 & max(now)>0
  fxmatrix.(curname) = chartinterp(fxmatrix.(curname));
  fxmatrix.(curname) = ndnanfilter(fxmatrix.(curname), 'gausswin', [dim*2/dim, dim*2/dim], 2, {}, {'replicate'}, 1);

  end

  current = fxmatrix.(curname);
  current(isnan(current)) = eps;
  fxmatrix.(curname) = current;


end


%fx = chartinterp(fx);
%fx = ndnanfilter(fx, 'gausswin', [10/dim, 10/dim], 2, {}, {'symmetric'}, 1);

%outputs a structure of rates

maxprob = [];
spikenum = 1;
times = [];
percents = [];
numcel = [];
maxx = [];
maxy = [];
same = 0;

occ = zeros(xbins, ybins);
testing = 0;
for x = (1:xbins)
  for y = (1:ybins)
    if x<xbins & y<ybins
      occx = find(xvals>=xinc(x) & xvals<xinc(x+1));
      occy = find(yvals>=yinc(y) & yvals<yinc(y+1));
    elseif x==xbins & y<ybins
      occx = find(xvals>=xinc(x));
      occy = find(yvals>=yinc(y) & yvals<yinc(y+1));
    elseif x<xbins & y==ybins
      occx = find(xvals>=xinc(x) & xvals<xinc(x+1));
      occy = find(yvals>=yinc(y));
    elseif x==xbins & y==ybins
      occx = find(xvals>=xinc(x));
      occy = find(yvals>=yinc(y));
    end
    if length(intersect(occx, occy)) ==0
      occ(x,y) = 0;
    else
      occ(x,y) = 1;
    end
end
end



n =0;
nivector = zeros((numclust),1);
tm = 1;
while tm < (length(timevector)-t)


  goodvel = find(vel(2,:)>=timevector(tm) & vel(2,:)<timevector(tm+t));

%  if nanmean((vel(1,goodvel)))>velthreshold & nanmedian((vel(1,goodvel)))>velthreshold
  if length(find(vel(1,goodvel)>velthreshold)) >= length(goodvel)*.75
   %find spikes in each cluster for time
   nivector = zeros((numclust),1);
   for c=1:numclust   %permute through cluster
     name = char(clustname(c));
     %find number of cells that fire during each period
     nivector(c) = length(find(clusters.(name)>=timevector(tm) & clusters.(name)<timevector(tm+t)));
   end

      %for the cluster, permute through the different positions
      endprob = zeros(xbins, ybins);
        for x = (1:xbins) %WANT TO PERMUTE THROUGH EACH SQUARE OF SPACE SKIPPING NON OCCUPIED SQUARES. SO EACH BIN SHOULD HAVE TWO COORDINATES
          for y = (1:ybins)
          productme =0;
          expme = 0;
          c = 1;

          if occ(x,y) == 0 %means never went there, dont consider
            endprob(x,y) = NaN;
          %  break
          end

          for c=1:numclust  %permute through cluster
              ni = nivector(c);
              name = char(clustname(c));
              fx = fxmatrix.(name);
              if length(fx)<2
                continue
              end


              fx = (fx(x, y));
              productme = productme + (ni)*log(fx);  %IN
              expme = (expme) + (fx);
               % goes to next cell, same location

          end
          numcel(end+1) = (ni);
          % now have all cells at that location
          tmm = tdecodesec;

        endprob(x, y) = (productme) + (-tmm.*expme); %IN
        end
        end



        [maxvalx, maxvaly] = find(endprob == max(endprob(:)));

        mp = max(endprob(:))-12;

      endprob = exp(endprob-mp);


         %finds indices
        conv = 1./sum(endprob(~isnan(endprob)), 'all');
        endprob = endprob.*conv; %matrix of percents
        %percents = vertcat(percents, endprob);

        percents(end+1) = max(endprob(:)); %finds confidence
        if length(maxvalx) > 1 %if probs are the sample, randomly pick one and print warning
            same = same+1;
            maxvalx = datasample(maxvalx, 1);
            maxvaly = datasample(maxvaly, 1);

        end

            if length(maxvalx)<1 | length(maxvaly) <1
              maxx(end+1) = NaN;
              maxy(end+1) = NaN;
            else
              maxx(end+1) = (xinc(maxvalx)); %translates to x and y coordinates
              maxy(end+1) = (yinc(maxvaly));
            end


    else
      %means vel is too low
      maxx(end+1) = NaN;
      maxy(end+1) = NaN;
      percents(end+1) = NaN;
    end

        times(end+1) = timevector(tm);

    %if want overlap
    if tdecodesec>.5
      tm = tm+(t/2);
    else
      tm = tm+t;
    end

    n = n+1;
    if rem(n,500)==0
      n;
    end
end

warning('your probabilities were the same')
same = same
maxx = maxx+psize/2;
maxy = maxy+psize/2;
notnan = ~isnan(maxy);
maxy(notnan) = 0;
values = [maxx; maxy; percents; times];


toc
f = values;

error = ca_decodederror(f, posData, tdecode);
error_av = nanmean(error(1,:))
error_med = nanmedian(error(1,:))