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CCidx2_JP.m

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+% Cluster Connectivity index 2
+% Get cluster connectivity index of neuronal groups of peaks.
+% Mean of the groups' connectivity index
+% (This is an idea of Jesús E. Pérez-Ortega, José Bargas, et al.)
+%
+% [CCI] = CCidx2_JP(g,Xp,idx)
+%
+% Inputs
+% g = number of groups
+% Xp = binary data as matrix PxC (P = #peaks, C = #cells)
+% idx = indexes of group to which each data point belongs
+% 
+% Outputs
+% CCI = Connectivity index
+%
+% ..:: by Jesús E. Pérez-Ortega ::.. Mar-2012
+
+function [CCI] = CCidx2_JP(g,Xp,idx)
+
+P=size(Xp,1);
+C_intra=zeros(g,1);   % connectivity inter-group
+single=0;             % single neuronal vector
+for i=1:g
+    clust = find(idx==i);
+    PM=Xp(clust,:); 
+    idxPM=find(sum(PM,1));
+    PM=PM(:,idxPM);
+    if size(PM,1)==1    % if single not be taken into account
+        C_intra(i,1)=0; % unlike with the first version: 1 instead of 0
+        single=single+1;
+    else
+        C_intra(i,1)=CI_JP(PM);
+    end
+end
+% disp(C_intra)
+CCI=sum(C_intra)/g; % unlike with the first version: g instead of (g-single)
+
+
+

Change_Names_MEA_Channels.m

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+% Exclusivo para MEAs de 128 canales
+
+function new_channels = Change_Names_MEA_Channels(channels)
+
+    ch=channels(:,1);
+    ends=[find(diff(ch)~=0); length(ch)];
+    inis=[1; ends(1:end-1)+1];
+
+    for i=1:length(inis)
+       new(inis(i):ends(i))=i;
+    end
+
+    channels(:,1)=new;
+    new_channels=channels;
+end

Circle_Mask.m

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+function mask = Circle_Mask(image_size,xy_center,radius)
+% Create a circle mask
+%
+% Jesus Perez-Ortega, [email protected]
+% March, 2019
+
+% Get size of image
+h = image_size(1);
+w = image_size(2);
+[x,y] = meshgrid(1:w,1:h);
+
+% Get mask
+mask = sqrt((x-xy_center(1)).^2 + (y-xy_center(2)).^2) < radius;

ClustIdx_JP.m

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+% Clustering indexes
+% Get indexes for evaluating clustering from hierarchical cluster tree
+%
+% [ClustIdx g] = ClustIdx_JP(Tree, SimOrXpeaks, Metric, numFig)
+%
+% Inputs
+% Tree = hierarchical cluster tree
+% SimOrXpeaks = Sim, similarity as matrix PxP (P=#peaks) for metrics Dunn &
+%               Contrast; Xpeaks, peaks vectors as matrix PxC for metrics
+%               Connectivity & Davies
+%               (P = #peaks; C=#cells)
+% Metric = index to compute ('Dunn','Connectivity','Davies','Contrast')
+% numFig = number of the figure to plot
+%
+% Output
+% ClustIdx = clustering indexes of 'Metric' from 2 to 10 groups
+% g = best number of groups according to the index selected
+%
+% ..:: by Jesús E. Pérez-Ortega ::.. Jun-2012
+% modified March-2018
+
+function [ClustIdx g] = ClustIdx_JP(tree_or_data,similitud,method,num_fig, clust_method,groups)
+
+if(nargin==5)
+    groups=2:30;
+end
+
+dist=1-similitud;
+j=1;
+for i=groups
+    switch(clust_method)
+        case 'hierarchical'
+            T = cluster(tree_or_data,'maxclust',i);
+        case 'kmeans'
+            T = kmeans(tree_or_data,i);
+    end
+    g=max(T);
+
+    switch(method)
+        case 'Dunn'
+            ClustIdx(j)=DunnIdx_JP(g,dist,T);
+        case 'Connectivity'
+            ClustIdx(j)=CCidx2_JP(g,similitud,T);
+        case 'Davies'
+            ClustIdx(j)=DaviesIdx_JP(g,similitud,T);
+        case 'Contrast'
+            ClustIdx(j)=ContrastIdx_JP(g,similitud,T);
+        otherwise
+            method='Dunn';
+            ClustIdx(j)=DunnIdx_JP(g,dist,T);
+    end
+    j=j+1;
+end
+figure(num_fig)
+plot(groups,ClustIdx)
+hold on
+
+[gIdx,g]=max(ClustIdx);
+
+plot(groups(g),gIdx,'*r')
+hold off
+title([method '''s index (' num2str(groups(g)) ' groups recommended)'])

Compare_Times.m

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+% Times comparison  Manual VS Auto
+
+function [coincidence,diffs] = Compare_Times(times_1,times_2,coactivity)
+    if(nargin==2)
+        coactivity=[];
+    end
+
+    n_1=size(times_1,1);
+    n_2=size(times_2,1);
+    Times_1=times_1(:);
+    Times_2=times_2(:);
+
+    Y_manual=1.5*ones(length(Times_1),1);
+    Y_auto=1.5*ones(length(Times_2),1);
+
+    % Comparison
+    n_coincidence=0;
+    auto_extra=0;
+    manual_extra=0;
+    auto_actual=1;
+    coincidence=[];
+    for i=1:n_1
+        i_m=times_1(i,1);
+        f_m=times_1(i,2);
+
+        for j=auto_actual:n_2
+            i_a=times_2(j,1);
+            f_a=times_2(j,2);
+
+            % If data is in the same place
+            %if(i_m>=i_a && i_m<=f_a || f_m>=i_a && f_m<=f_a || i_m<=i_a && f_m>=f_a)
+            if abs(i_m-i_a)<100
+                diff_i=i_a-i_m;
+                diff_f=f_a-f_m;
+                n_coincidence=n_coincidence+1;
+                coincidence(n_coincidence,:)=[i_m i_a f_m f_a];
+                diffs(n_coincidence,:)=[diff_i diff_f];
+                auto_actual=j+1;
+                break;
+            % If are differents
+            elseif(i_m>f_a)
+                auto_extra=auto_extra+1;
+            elseif(f_m<i_a)
+                manual_extra=manual_extra+1;
+                auto_actual=j;
+                break;
+            end
+        end
+    end
+
+%     Set_Figure('Errors in initial and final times',[0 0 1000 600]);
+%     subplot(2,1,1)
+%     hist(diffs(:,1))
+%     xlabel('time (ms)')
+%     title(['Error distribution in initial times (mean ' num2str(mean(diffs(:,1)))...
+%         ' std ' num2str(std(diffs(:,1))) ')'])
+%     subplot(2,1,2)
+%     hist(diffs(:,2))
+%     xlabel('time (ms)')
+%     title(['Error distribution in final times (mean ' num2str(mean(diffs(:,2)))...
+%         ' std ' num2str(std(diffs(:,2))) ')'])
+
+%     s=hgexport('readstyle','A_default');
+%     s.Format = 'png';
+%     hgexport(gcf,'Histograms.png',s);
+
+    if (coactivity)
+        % Plot
+        Set_Figure('Coactivity with initial and final times',[0 0 1000 300]);
+        plot(coactivity,'k'); hold on
+        plot(Times_1,Y_manual,'or')
+        plot(Times_2,Y_auto,'xb')
+        legend({'Coactivity','manual','auto'})
+
+        Y_coincidence=2.5*ones(n_coincidence,1);
+        % Plot manual
+        plot(coincidence(:,1),Y_coincidence,'og')
+        plot(coincidence(:,3),Y_coincidence,'og')
+        % Plot auto
+        plot(coincidence(:,2),Y_coincidence,'xg')
+        plot(coincidence(:,4),Y_coincidence,'xg')
+
+        title(['manual: ' num2str(length(times_1)) ' ( ' num2str(manual_extra) ' extras) - auto: ' num2str(length(times_2)) ' ( ' num2str(auto_extra) ' extras)'])
+
+%         s=hgexport('readstyle','A_default');
+%         s.Format = 'png';
+%         hgexport(gcf,'Coactivity with times.png',s);
+    end
+end

ContrastIdx_JP.m

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+% Contrast index
+% Get the Contrast index for g groups given a similarity matrix.
+% (Michelson contrast 1927, Plenz 2004)
+%
+% [CstIdx] = ContrastIdx_JP(g,sim,idx)
+%
+% Inputs
+% g = number of groups
+% sim = similarity as matrix PxP (P = #peaks)
+% idx = indexes of group to which each data point belongs
+% 
+% Outputs
+% CstIdx = Contrast index
+
+% ..:: by Jesús E. Pérez-Ortega ::.. April-2012
+
+function [CstIdx] = ContrastIdx_JP(g,sim,idx)
+
+simC=sim-diag(diag(sim));
+
+s_i=0;
+s_o=0;
+p_i=0;
+p_o=0;
+for i=1:g
+    g_i=find(idx==i);
+    g_o=find(idx~=i);
+
+    s_i=s_i+sum(sum(simC(g_i,g_i)));  % sum intra-group
+    s_o=s_o+sum(sum(simC(g_i,g_o))); % sum others
+
+    p_i=p_i+numel(g_i)^2;
+    p_o=p_o+numel(g_i)*numel(g_o);
+end
+Di=s_i/p_i;
+Do=s_o/p_o;
+
+CstIdx=(Di-Do)/(Di+Do);

DaviesIdx_JP.m

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+% Davies-Bouldin Index
+% Get the Davies-Bouldin Index for g groups given a distance matrix.
+%
+% [DBI] = DaviesIdx_JP(g,dist,idx)
+%
+% Inputs
+% g = number of groups
+% Xp = binary data as matrix PxC (P = #peaks, C = #cells)
+% dis = square distance matrix
+% idx = indexes of group to which each data point belongs
+% 
+% Outputs
+% DBI = Davies-Bouldin index
+%
+% ..:: by Jesús E. Pérez-Ortega ::.. Mar-2012
+
+function [DBI] = DaviesIdx_JP(g,Xp,idx)
+
+db=zeros(g);
+for i=1:g-1
+
+    g_i=find(idx==i);           % index of ith group
+    c_i=mean(Xp(g_i,:),1);        % centroid of ith group
+
+    c_i_rep=repmat(c_i,numel(g_i),1);
+    s_i=mean(sqrt(sum((c_i_rep-Xp(g_i,:)).^2,2))); % standard deviation of ith group
+
+    for j=i+1:g
+
+        g_j=find(idx==j);       % index of jth group
+        c_j=mean(Xp(g_j,:));    % centroid of ith group
+        c_j_rep=repmat(c_j,numel(g_j),1);
+        s_j=mean(sqrt(sum((c_j_rep-Xp(g_j,:)).^2,2))); % standard deviation of ith group
+
+        d_ij=sqrt(sum((c_i-c_j).^2)); % Distance Between centroids
+
+        db(i,j)=(s_i+s_j)/d_ij;
+        db(j,i)=(s_i+s_j)/d_ij;
+    end
+end
+
+DBI=sum(max(db))/g;
+
+
+

Delete_Consecutive_Coactivation.m

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+% Delete consecutive coactivation patterns from raster
+%
+% By Jesús E. Pérez-Ortega Aug-2018
+
+function [short_raster indices]= Delete_Consecutive_Coactivation(raster)
+    same=squareform(pdist(double(raster'),'hamming'),'tomatrix')==0;
+    d=diff(same);
+    j=1; indices=1;
+    while(~isempty(j))
+        idx=find(d(j:end,j)==-1,1,'first');
+        j=j+idx;
+        indices=[indices j];
+    end
+    short_raster=raster(:,indices);
+end

Delete_Raster_Activity_Between_Trials.m

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+% Get the raster without spurious activity (between trials)
+%
+% Pérez-Ortega Jesús - July 2018
+
+function raster_corrected = Delete_Raster_Activity_Between_Trials(raster,offset,time,trials)
+
+    trial_time=time+offset;
+    offset=offset+1;
+    idx=[];
+    for i=1:trials
+        idx=[idx (offset:trial_time)+trial_time*(i-1)];
+    end
+    raster_corrected=raster(:,idx);
+
+end

Divide_Peaks.m

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+% Divide peak indices in bin
+% 
+% By Jesús Pérez-Ortega april-2018
+
+function peak_indices_divided = Divide_Peaks(peak_indices, bin)
+    peak_indices_divided=peak_indices;
+    peaks=max(peak_indices);
+    peak_i=1;
+    for i=1:peaks
+        peak=find(peak_indices==i);
+        if(~isempty(peak))
+            n_divs=ceil(length(peak)/bin);
+            for j=1:n_divs
+                ini=peak((j-1)*bin+1);
+                if(j==n_divs)
+                    fin=peak(end);
+                else
+                    fin=peak(j*bin);
+                end
+                peak_indices_divided(ini:fin)=peak_i;
+                peak_i=peak_i+1;
+            end
+        end
+    end
+end