netForMovie_boutOnset.m

function netForMovie_boutOnset(w, xdyn, trainingNeurons, PlottingParams)
% Makes network diagram and raster plots, called by RunHVC_boutOnset_net
% w: weight matrix
% xdyn: activity of network
% m: duration of one syllable, in timesteps
% trainingNeurons: cell array of structures containing neuron and time indices for each syllable type
% PlottingParams: sets linewidth, etc.  See RunHVC_split
%
% Emily Mackevicius 12/10/2014, heavily copied from Hannah Payne's code
% which builds off Ila Fiete's model, with help from Michale Fee and Tatsuo
% Okubo.

%% plotting parameters

msize = PlottingParams.msize;
linewidth = PlottingParams.linewidth;
Syl1Color = PlottingParams.Syl1Color;
Syl2Color = PlottingParams.Syl2Color;
numFontSize = PlottingParams.numFontSize;
labelFontSize = PlottingParams.labelFontSize;
nplots = PlottingParams.totalPanels;
ploti = PlottingParams.thisPanel;

%% network diagram
%subplot('position', [ploti/nplots-.9/nplots, .7, .9/nplots, .2])
cla; hold on

% calculate latency of each neuron
Latency = findLatency(xdyn, trainingNeurons);

% first double plot all neurons that don't fire at a consistent phase
nsteps = size(xdyn,2);
n = size(xdyn,1);
ntot = n;
x = zeros(1,n);
y = zeros(1,n);
trainingset1 = trainingNeurons{1}.nIDs;
trainingset2 = trainingNeurons{2}.nIDs;
indDoubled = [];
cDoub = n+1;
for ni = 1:n
    if length(intersect(trainingset1,ni))>0 % if it's a training neuron
        x(ni) = trainingNeurons{1}.candLat(1);
    elseif length(intersect(trainingset2,ni))>0 % if it's a training neuron
        x(ni) = trainingNeurons{2}.candLat(1);
    else
        if Latency{1}.FireDur(ni)|Latency{2}.FireDur(ni) % if it fired during either syll
            if (Latency{1}.FireDur(ni)&Latency{2}.FireDur(ni)) % if it fired during both sylls
                if (Latency{1}.mode(ni)==Latency{2}.mode(ni)) % if fired during both sylls at same phase
                    x(ni) = Latency{1}.mode(ni);
                else % double plot if different phases for both sylls
                    x(ni) = Latency{1}.mode(ni);
                    x(cDoub) = Latency{2}.mode(ni);
                    indDoubled = [indDoubled ni];
                    cDoub = cDoub+1;
                end
            elseif Latency{1}.FireDur(ni) % if it fired during syll 1 only
                x(ni) = Latency{1}.mode(ni);
            else % fired during syll 2 only
                x(ni) = Latency{2}.mode(ni);
            end
        else % if it fired during neither syll
            x(ni) = NaN;
        end
    end
end
indkeep = [find(~isnan(x(1:n))) indDoubled];
y = y(indkeep);
w = w(indkeep,indkeep);
xdyn = xdyn(indkeep,:);
x = x(indkeep);
ux = unique(x);
indDoubled

% keep track of which neurons participated in each syllable
FireDur1 = Latency{1}.FireDur(indkeep);
FireDur2= Latency{2}.FireDur(indkeep);

% classify neurons as specific or shared
Specific1 = FireDur1&~FireDur2;
Specific2 = FireDur2&~FireDur1;
Shared = (FireDur1&FireDur2);
indshared = find(Shared);

% calculate the incoming weights from specific neurons of each type, to
% determine sorting in y axis and color
c1 = zeros(1,length(x));
c2 = zeros(1,length(x));
for ni = 1:size(w,1)
    tmp = find(xdyn(ni,:));
    if sum(w(ni,:))>0
        c1(ni) = sum(w(ni,Specific1))/sum(w(ni,:));
        c2(ni) = sum(w(ni,Specific2))/sum(w(ni,:));
    end
    y(ni) = c1(ni)-c2(ni);
end

% for each latency (x), sort along y, with small gap between shared and
% specific neurons
y1 = zeros(1,size(w,1));
for ui = 1:length(ux)
    indshared = (x==ux(ui))&Shared;
    ind1 = (x==ux(ui))&Specific1;
    ind2 = (x==ux(ui))&Specific2;
    [~,y1(indshared)] = sort(y(indshared));
    tocentershared = 1+(numel(find(indshared))-1)/2;
    y1(indshared) = y1(indshared)-tocentershared;
    [~,y1(ind1)] = sort(y(ind1));
    y1(ind1) = y1(ind1) + (numel(find(indshared)))/2;
    [~,y1(ind2)] = sort(y(ind2));
    y1(ind2) = y1(ind2) -numel(find(ind2))-1-(numel(find(indshared)))/2;
end

% Color weights white to black between wplotmin and wplotmax
wplot = w;
wplot = w-PlottingParams.wplotmin;
wplot(wplot<0) = 0;
wplot = wplot/(PlottingParams.wplotmax-PlottingParams.wplotmin);
wplot(wplot<prctile(wplot(:), PlottingParams.wprctile)) = 0;

% jitter a little in x and y, so it doesn't look like a grid
jitter = .1;
% fixed seed for plotting jitter, so it doesn't interact with seed for
% running network (Seed0 = randn(1,500);)
Seed0 = 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indJitter = setdiff(1:length(x), union(trainingset1, trainingset2)); % don't jitter seed neurons
x(indJitter)= x(indJitter)+jitter*Seed0(1:length(x(indJitter)));
y1(indJitter) = y1(indJitter)+jitter*Seed0((length(x(indJitter))+1):(2*length(x(indJitter))));

% plot w in order from weakest to strongest, so darker lines are on top
n = size(wplot,1);
js = repmat((1:n)',1,n);
is = repmat((1:n),n,1);
isVec = is(:);
jsVec = js(:);
wVec = wplot(:);
[wSort,indSort] = sort(wVec, 'ascend');
for k = 1:length(wSort)
    i = isVec(indSort(k));
    j = jsVec(indSort(k));
    if wplot(j,i)>0
        ff = x(i)<=x(j);
        longrange = abs(x(i)-x(j))>2;
        loopback = (round(x(i))==round(max(x)))&(round(x(j))==round(min(x)));
        if ff & ~longrange%|loopback
            C = ones(1,3)-wplot(j,i)*ones(1,3);
            plot([x(i), x(j)], [y1(i),y1(j)], 'color', C, 'linewidth', linewidth)
        end
    end
end

% color each neuron based on its relative input from each syllable type
for pli = 1:length(x)
    tmpC = c1(pli)'/(max(c1)+eps)*Syl1Color+c2(pli)'/(max(c2)+eps)*Syl2Color;
    tmpC = tmpC/(max(tmpC)+eps); % normalize so colors are bright
    if Shared(pli)
        tmpC = zeros(1,3);
    end
    if Specific1(pli)
        tmpC = Syl1Color;
    end
    if Specific2(pli)
        tmpC = Syl2Color;
    end
    plot(x(pli),y1(pli), 'marker', '.', 'color', tmpC, 'markersize', msize)
end

% plot rectangle for syl1 seed neurons
rx = mean(x(trainingset1))-.5;
ry = min(y1(trainingset1))-.5;
rw = 1;
rh = max(y1(trainingset1)) - min(y1(trainingset1))+1;
rectangle('Position', [rx ry rw rh], ...
    'FaceColor',  'none', ...
    'LineStyle', '-', 'LineWidth', .5, 'EdgeColor', PlottingParams.SeedColor,...
    'curvature', [.98 .1])

% plot rectangle for syl2 seed neurons
rx = mean(x(trainingset2))-.5;
ry = min(y1(trainingset2))-.5;
rw = 1;
rh = max(y1(trainingset2)) - min(y1(trainingset2))+1;
rectangle('Position', [rx ry rw rh], ...
    'FaceColor',  'none', ...
    'LineStyle', '-', 'LineWidth', .5, 'EdgeColor', PlottingParams.SeedColor,...
    'curvature', [.98 .1])

xlim([trainingNeurons{1}.candLat(1)-1 trainingNeurons{1}.candLat(end)+.5])
ylim([-9 9])%[-max(abs(y1)+1) max(abs(y1)+1)]); %[min(y1)-.5 max(y1)+.5])
axis off;
set(gca, 'color', 'none')

% %% rasters...
% 
% Syl1Color = PlottingParams.Syl1Color;
% Syl2Color = PlottingParams.Syl2Color;
% ProtoSylColor = PlottingParams.ProtoSylColor;
% numFontSize = PlottingParams.numFontSize;
% labelFontSize = PlottingParams.labelFontSize;
% 
% bottom = .1;
% height = .55;
% scale = .005;
% 
% spacing = .75/(2*nplots);
% 
% % cmap = flipud(gray);
% % cmap = cmap(1:64,:);
% % cn = size(cmap,1);
% Red = trainingNeurons{1}.nIDs;
% Green = trainingNeurons{2}.nIDs;
% IsTrain1 = zeros(1,length(xdyn)); IsTrain1(Red) = 1;
% IsTrain2 = zeros(1,length(xdyn)); IsTrain2(Green) = 1;
% 
% % cmap(cn+1,:) = [0 0 0];
% % cmap(cn+2,:) = Syl1Color; % some red training neurons
% % cmap(cn+3,:) = Syl2Color; % some green training neurons
% % cmap(cn+4,:) = ProtoSylColor; % sometimes magenta ... never in this case.
% %
% % xdyn(Red,:) = xdyn(Red,:)*(1+1/cn);
% % xdyn(Green,:) = xdyn(Green,:)*(1+2/cn);
% 
% %%
% %collecting what I'll plot for the raster
% sylIDtoplot = 7; %(don't choose a protosyllable that's at the beginning of a bout)
% k = length(union(trainingset1, trainingset2));
% tindplot1 = trainingNeurons{1}.tind(sylIDtoplot) + trainingNeurons{1}.candLat-1; % time of example syl 1
% tindplot2 = trainingNeurons{2}.tind(sylIDtoplot) + trainingNeurons{2}.candLat-1; % time of example syl 2
% [~,indsort] = (sortrows(xdyn(:,[tindplot1 tindplot2]))); % sort by which fired first
% tmp = xdyn(flipud(indsort), [tindplot1 tindplot2]); % pull out the example data from xdyn
% IsTrain1 = IsTrain1(flipud(indsort));
% IsTrain2 = IsTrain2(flipud(indsort));
% indShared = (sum(tmp(:,1:length(tindplot1)),2)>0) & (sum(tmp(:,(length(tindplot1)+1):end),2)>0);
% indBO = (sum(tmp(:,1:length(tindplot1)),2)>0) & (sum(tmp(:,(length(tindplot1)+1):end),2)==0);
% rest = ~indShared;
% tmp = tmp([find(indShared); find(rest)],:); % everything that will be plotted in the rasters
% IsTrain1 = IsTrain1([find(indShared); find(rest)]);
% IsTrain2 = IsTrain2([find(indShared); find(rest)]);
% 
% 
% %%
% % plot Bout Onset syllable
% subplot('position', [ploti/nplots-2*spacing, bottom, length(tindplot1)*scale, height])%subplot(3,nHorPlot,(nHorPlot+PlottingParams.thisPanel*4-2)+[0 nHorPlot]+.75)
% tmp1 = tmp(:,1:length(tindplot1)); % just bout onset syllable
% %tmp1(end+1,end+1) = 1+4/cn; % to normalize cmap for plotting
% PlottingParams.axesPosition = [ploti/nplots-2*spacing, bottom, length(tindplot1)*scale, height];
% 
% tOffset = trainingNeurons{1}.candLat(1)-1;
% for j=1:size(tmp1,2) % for all the time steps
%     Idx = find(tmp1(1:end-1,j)>0); % find the indices of active neurons
%     if ~isempty(Idx)
%         for k=1:length(Idx) % for all the active neurons
%             Color = IsTrain1(Idx(k))*PlottingParams.Syl1Color + ...
%                 IsTrain2(Idx(k))*PlottingParams.Syl2Color;
%             h = patch(10*([j-1,j,j,j-1]+tOffset),[Idx(k)-1,Idx(k)-1,Idx(k),Idx(k)],Color,'edgecolor','none');
%         end
%     end
% end
% 
% hold on; box off
% set(gca, 'fontsize', numFontSize)
% set(gca, 'color', 'none', 'xtick', [0 50 100], 'xticklabel', {'0', '50', '100'},'ydir', 'reverse', 'fontsize', numFontSize)
% set(gca, 'ydir', 'reverse','tickdir','out','ticklength',[0.015 0.015], 'color', 'none', 'fontsize', numFontSize,'tickdir','out');
% 
% if PlottingParams.thisPanel==1
%     ylabel('Neuron','fontsize', labelFontSize)
%     set(gca,'ytick',0:20:100,'fontsize', numFontSize)
% else
%     set(gca,'ytick',0:20:100,'yticklabel',{});
% end
% 
% if PlottingParams.Hor
%     if sum(indShared)>0 % if shared neurons
%         plot([-10+trainingNeurons{1}.candLat(1)*10 trainingNeurons{1}.candLat(end)*10], (sum(indShared))*ones(1,2), 'k', 'linewidth', PlottingParams.linewidth);
%     end
%     plot([-10+trainingNeurons{1}.candLat(1)*10 trainingNeurons{1}.candLat(end)*10], ...
%         (sum(indBO)+sum(indShared))*ones(1,2), 'k', 'linewidth', PlottingParams.linewidth);
% end
% if isfield(PlottingParams,'boutOnsetElement')
%     patch([-10+trainingNeurons{1}.candLat(1)*10 -10 -10 -10+trainingNeurons{1}.candLat(1)*10],[-4 -4 -2 -2],Syl1Color);
%     patch([-10+trainingNeurons{2}.candLat(1)*10 trainingNeurons{2}.candLat(end)*10 trainingNeurons{2}.candLat(end)*10 -10+trainingNeurons{2}.candLat(1)*10],[-4 -4 -2 -2],Syl2Color);
%     if PlottingParams.thisPanel>1 % TO
%         text((-10+trainingNeurons{1}.candLat(1)*10-10)/2,-7,'\epsilon','fontsize',7)
%         text(((-10+trainingNeurons{2}.candLat(1)*10)+(trainingNeurons{2}.candLat(end)*10))/2,-7,'\alpha','fontsize',7)
%     end
% else
%     patch([-10+trainingNeurons{1}.candLat(1)*10 trainingNeurons{1}.candLat(end)*10 trainingNeurons{1}.candLat(end)*10 -10+trainingNeurons{1}.candLat(1)*10],[-4 -4 -2 -2],Syl1Color);
%     if PlottingParams.thisPanel>2
%         text(((-10+trainingNeurons{1}.candLat(1)*10)+(trainingNeurons{1}.candLat(end)*10))/2,-7,'\beta','fontsize',7)
%     end
% end
% ylim([-5 ntot])
% xlim([-10+trainingNeurons{1}.candLat(1)*10 trainingNeurons{1}.candLat(end)*10+10])
% %%
% 
% % Plot protosyllable.
% subplot('position', [ploti/nplots-spacing, bottom, length(tindplot2)*scale, height])%subplot(3,nHorPlot,(nHorPlot+PlottingParams.thisPanel*4)+[0 nHorPlot])
% tmp1 = tmp(:,(length(tindplot1)+1):end); % just for protosyllable
% %tmp1(end+1,end+1) = 1+4/cn; % to normalize cmap for plotting
% PlottingParams.axesPosition = [ploti/nplots-spacing, bottom, length(tindplot2)*scale, height];
% 
% tOffset = trainingNeurons{2}.candLat(1)-1;
% for j=1:size(tmp1,2) % for all the time steps
%     Idx = find(tmp1(1:end-1,j)>0); % find the indices of active neurons
%     if ~isempty(Idx)
%         for k=1:length(Idx) % for all the active neurons
%             Color = IsTrain1(Idx(k))*PlottingParams.Syl1Color + ...
%                 IsTrain2(Idx(k))*PlottingParams.Syl2Color;
%             h = patch(10*([j-1,j,j,j-1]+tOffset),[Idx(k)-1,Idx(k)-1,Idx(k),Idx(k)],Color,'edgecolor','none');
%         end
%     end
% end
% 
% % PlottingParams.tOffset = trainingNeurons{2}.candLat(1)-1;
% % plotRaster(tmp1,PlottingParams,0,2)
% %imagesc(tmp1, 'xdata', trainingNeurons{2}.candLat*10); colormap(cmap); axis tight
% hold on; box off
% set(gca, 'fontsize', numFontSize)
% set(gca, 'color', 'none', 'xtick', [0 50 100], 'xticklabel', {'0', '50', '100'}, 'ydir', 'reverse', 'fontsize', numFontSize)
% set(gca, 'ydir', 'reverse','tickdir','out','ticklength',[0.015 0.015], 'color', 'none', 'fontsize', numFontSize,'tickdir','out');
% set(gca, 'ytick',0:20:100,'yticklabel',{})
% if PlottingParams.Hor
%     if sum(indShared)>0 % if shared neurons
%         plot([-10+trainingNeurons{2}.candLat(1)*10 trainingNeurons{2}.candLat(end)*10], (sum(indShared))*ones(1,2), 'k', 'linewidth', PlottingParams.linewidth);
%     end
%     plot([-10+trainingNeurons{2}.candLat(1)*10 trainingNeurons{2}.candLat(end)*10], ...
%         (sum(indBO)+sum(indShared))*ones(1,2), 'k', 'linewidth', PlottingParams.linewidth);
% end
% 
% patch([-10+trainingNeurons{2}.candLat(1)*10 trainingNeurons{2}.candLat(end)*10 trainingNeurons{2}.candLat(end)*10 -10+trainingNeurons{2}.candLat(1)*10],[-4 -4 -2 -2],Syl2Color);
% if isfield(PlottingParams,'boutOnsetElement')
%     if PlottingParams.thisPanel>1
%         text(((-10+trainingNeurons{2}.candLat(1)*10)+(trainingNeurons{2}.candLat(end)*10))/2,-7,'\alpha','fontsize',7)
%     end
% else
%     if PlottingParams.thisPanel>2
%         text(((-10+trainingNeurons{2}.candLat(1)*10)+(trainingNeurons{2}.candLat(end)*10))/2,-7,'\alpha','fontsize',7)
%     end
% end
% ylim([-5 ntot])
% xlim([-10+trainingNeurons{2}.candLat(1)*10 trainingNeurons{2}.candLat(end)*10+10])