uploading matlab files

AF_trapezoid_integration_v2.m

@@ -0,0 +1,39 @@
+%initializing
+
+d_area = 0;
+
+% d = your data
+% enter your values here:
+d_start = 150;
+d_end = 600;
+
+%num_intervals = 50;
+
+%interval = ceil( (d_end-d_start)/num_intervals );
+
+interval = 1;
+
+d_b = d - d(d_start); %subtracting baseline
+
+x = (d_start : interval : d_end)';
+y = d_b (x);
+
+%plotting
+figure; hold on
+plot (d);
+
+%AF trapezoid integration
+for i = 1 : size(x)-1
+    d_area (i) = (x(i+1) - x(i)) * ((y(i) + y(i+1))/2);
+
+    %plotting trapezoid
+    t_x = [x(i), x(i), x(i+1), x(i+1)];
+    t_y = [0, y(i), y(i+1), 0];
+
+    fill (t_x, t_y, 'r');
+end
+
+AF_area = sum(d_area)
+
+%trapz integration
+trapz_area = trapz(x,y)

IclTrace.m

@@ -0,0 +1,12 @@
+classdef IclTrace
+    %ICLTRACE Summary of this class goes here
+    %   Detailed explanation goes here
+
+    properties
+    end
+
+    methods
+    end
+
+end
+

anal_events.m

@@ -1,3 +1,5 @@
+
+
 %% get the data, init constants
 
 clear all;
@@ -22,6 +24,7 @@
 % find peak at min (stimulus start : peak_end)
 
 figure; hold on;
+xlim([data_start, data_finish])
 grey=[.8,.8,.8];
 
 for i = 1 : size (fig_y)
@@ -33,77 +36,52 @@
 
     %     data_peaks_x(i) = find (data_sweeps{i} == data_peaks_y(i));
     data_peaks_x(i) = data_peaks_x(i) + stim_start;
+
     plot (data_sweeps{i}, 'Color', grey);
     plot (data_peaks_x(i), data_peaks_y(i), 'ob');
 
 
     %% finding the onset
 
-    data_pos = pos * data_sweeps{i};
-
-    d_smooth = smooth (data_pos, smooth_deg, 'loess');
-    diff_s = diff (d_smooth);
-
-    min_diff = std (diff_s(data_start:stim_start));
-
-
-    diff_pts = find (diff_s (stim_start:peak_end) > min_diff);
-
-    if diff_pts
-        event_onset_x (i) = diff_pts(1,1) + stim_start;
-        event_onset_y (i) = data_sweeps{i}(event_onset_x(i));
-
-        plot (event_onset_x (i), event_onset_y (i), 'ko');
-    else
-        event_onset_x (i) = nan;
-        event_onset_y (i) = nan;
-    end
+    [event_onset_x(i), event_onset_y(i)] = find_onset (data_sweeps{i}, pos);
+    plot (event_onset_x(i), event_onset_y(i), 'ob');
 end
 
+%% average and plot data
+
+% figure(); hold on;
+
 avg_peak_x = mean (data_peaks_x);
 avg_peak_y = mean (data_peaks_y);
 
 plot (avg_peak_x, avg_peak_y, 'bs');
 
+
 avg_onset_x = nanmean (event_onset_x);
 avg_onset_y = nanmean (event_onset_y);
 
 plot (avg_onset_x, avg_onset_y, 'ks');
 
 
-%% plotting the data and the average trace
-
 dim = ndims(data_sweeps{2});         %# Get the number of dimensions for your arrays
 M = cat(dim+1,data_sweeps{:});            %# Convert to a (dim+1)-dimensional matrix
 y_mean = mean(M,dim+1);      %# Get the mean across arrays
 
 plot (y_mean, 'r');
 
-[y_mean_peak_y y_mean_peak_x] = min (y_mean(stim_start:peak_end));
+[y_mean_peak_y, y_mean_peak_x] = min (y_mean(stim_start:peak_end));
 y_mean_peak_x = y_mean_peak_x + stim_start;
 
-% y_mean_peak_x = find (y_mean==y_mean_peak_y);
+plot (y_mean_peak_x, y_mean_peak_y, 'or');
 
 
+[y_mean_onset_x y_mean_onset_y] = find_onset(y_mean, pos);
+plot (y_mean_onset_x, y_mean_onset_y, 'or');
 
-plot (y_mean_peak_x, y_mean_peak_y, 'or');
-
-%     data_pos = pos * y_mean(stim_start:data_finish);
-
-    data_pos = pos * y_mean();
-
-    d_smooth = smooth (data_pos, smooth_deg, 'loess');
-    diff_s = diff (d_smooth);
-
-    min_diff = std (diff_s(data_start:stim_start));
-
-    diff_pts = find (diff_s > min_diff);
-
-    event_onset_x = diff_pts(1,1) + stim_start;
-    event_onset_y = y_mean(event_onset_x);
-
-    plot (event_onset_x, event_onset_y, 'rs');
 
-%% set axis limits
+%% results
 
-xlim([data_start, data_finish])
+avg_onset_x
+avg_onset_y
+avg_peak_x
+avg_peak_y

avg_fig_traces.m

@@ -0,0 +1,30 @@
+%% Averages traces on a selected figure
+
+data_b = {};
+
+
+h = findobj(gca,'Type','line');
+fig_x=get(h,'Xdata');
+fig_y=get(h,'Ydata');
+
+data_start = 1900;
+stim_start = 2000;
+data_finish = 2600;
+
+
+%subtract baseline
+figure; hold on;
+grey=[.8,.8,.8];
+
+for i = 1 : size (fig_y)
+    base = mean (fig_y{i}(data_start:stim_start));
+    data_b{i} = fig_y{i} - base;
+
+    plot (data_b{i}(data_start:data_finish), 'Color', grey);
+end
+
+dim = ndims(data_b{2});         %# Get the number of dimensions for your arrays
+M = cat(dim+1,data_b{:});            %# Convert to a (dim+1)-dimensional matrix
+y_mean = mean(M,dim+1);      %# Get the mean across arrays
+
+plot (y_mean (data_start:data_finish), 'r');

avgcurves.m

@@ -0,0 +1,133 @@
+function avgCurve = avgcurves( inputX, inputY )
+%avgcurves Takes (x,y) curve, averages y for any repeating
+% x values.
+
+
+
+    %% sort inputX and inputY into a cell%
+    tempCell = sortvectors( inputX, inputY );
+
+    srtCurve.x = tempCell{1};
+    srtCurve.y = tempCell{2};
+
+
+%% initializing newCurve and temp
+    % pre-allocating empty values for newCurve
+    newCurve.x = zeros(length(inputX),1);
+    newCurve.y = zeros(length(inputY),1);
+    newCurve.std = zeros(length(inputY),1);
+    newCurve.stdErr = zeros(length(inputY),1);
+
+    % initializing the first new X and Y    
+    newCurve.x(1) = srtCurve.x(1);
+    newCurve.y(1) = srtCurve.y(1);
+
+    %temp is a buffer vector holding all y-val's that have the same x-val
+    temp = newCurve.y(1);
+
+    uniqI = 1; %the index of the current unique x,y
+
+%% create averaged curve
+    for i = 2 : length( srtCurve.x ) %from the second value : last
+
+        if srtCurve.x(i) == srtCurve.x(i-1) %if two pts in a row are the same
+
+%            if temp ~= inf
+               temp = [temp; srtCurve.y(i)]; %add new point to temp
+%            else
+%                temp = srtCurve.y(i);
+%            end
+
+           %calculating y (avg), std and stdErr for the current x
+           newCurve.y(uniqI) = mean(temp(~isnan(temp))); %new y-value is mean(temp)
+%           newCurve.calcStd (uniqI, temp);
+
+           newCurve.std(uniqI) = std(temp(~isnan(temp)));
+           newCurve.stdErr(uniqI) = std(temp(~isnan(temp))) / sqrt( length(temp(~isnan(temp))) );
+
+        else
+           uniqI = uniqI + 1; %update uniqI
+
+           %update newCurve
+           newCurve.x(uniqI) = srtCurve.x(i);
+           newCurve.y(uniqI) = srtCurve.y(i);
+%          newCurve = newCurve.calcStd (uniqI, temp);
+
+           newCurve.std(uniqI) = std(temp(~isnan(temp)));
+           newCurve.stdErr(uniqI) = std(temp(~isnan(temp))) / sqrt( length(temp(~isnan(temp))) );
+
+           temp = newCurve.y(uniqI); %update temp to the next value
+
+        end %if
+
+    end %for
+
+    %delete zero values by making newCurve.x and .y go only until uniqI
+
+%     curve = {newCurve.x newCurve.y newCurve.std newCurve.stdErr};
+%     
+%     curve = curve(1:uniqI,1);
+%     
+%     [newCurve.x newCurve.y newCurve.std newCurve.stdErr] = curve{1,:};
+%     
+
+    % a little cell magic
+
+    % structure names
+    curveHeadings = {'x' 'y' 'std' 'stdErr'};
+
+    %conversting to a matrix
+    curve_mat = cell2mat( struct2cell(newCurve)' );
+
+    %assigining non-zero values to a cell
+    shortCurve_cell = num2cell( curve_mat(1:uniqI,:), 1)';
+
+    %converting back to a structure
+    newCurve = cell2struct (shortCurve_cell, curveHeadings, 1);
+
+
+
+
+
+%     newCurve.x = newCurve.x(1:uniqI,1);
+%     newCurve.y = newCurve.y(1:uniqI,1);
+%     newCurve.std = newCurve.std(1:uniqI,1);
+%     newCurve.stdErr = newCurve.stdErr(1:uniqI,1);    
+%% plotting results
+
+    % Create axes
+%     axes('Position',[0.13 0.1126 0.775 0.815]);
+%     box('on');
+%     hold('all');
+
+
+    %plot raw data and averaged data%
+    %plot (srtCurve.x, srtCurve.y, 'k.', newCurve.x,newCurve.y, '-ko');
+
+    plot (newCurve.x,newCurve.y, '-ko'); hold on;
+    %plot error bars%
+%     
+%     stdErr( length(srtCurve) ) = [];
+%     
+%     for i=1: length(strCurve.x)
+%         stdErr = 
+%     
+%     end
+
+
+  % stdErr = 10 * rand (1, length(newCurve.x) ); %generate random stdErr
+
+
+    errorbar(newCurve.x, newCurve.y, newCurve.stdErr,'LineStyle','none','Color',[0 0 1]);
+
+
+%% output
+
+    avgCurve = newCurve;  
+
+
+end
+
+%     function newCurve = calcStd(uniqI, temp)
+%         
+%     end

compare_v_th.m

@@ -0,0 +1,29 @@
+function  output = compare_v_th (current_cell)
+
+%     current_cell = cell_05;
+
+    for i = 1:min (size(current_cell))
+
+        sample = current_cell(:,i);
+
+        findAPs(sample);
+        APdata = find_dv_peaks (sample);
+        AP_th = find_v_th (APdata.APs);
+
+        total_th = [];
+        for j = 1:length (AP_th)
+            total_th = [total_th; AP_th(j).v];
+        end
+
+        mean_th(i,1) = mean (total_th);
+        f = find_v_th ({APdata.mean_AP});
+        mean_th_mAP(i,1) = f.v;
+    end
+
+    output.mean_th = mean_th;
+    output.mean_th_mAP = mean_th_mAP;
+
+    figure; hold on;
+
+    plot (output.mean_th, '.'); plot (output.mean_th_mAP, 'r.');
+end