Plotting many lines as a heatmap

Question

I have a large number (~1000) of files from a data logger that I am trying to process.

If I wanted to plot the trend from a single one of these log files I could do it using

plot(timevalues,datavalues)

I would like to be able to view all of these lines at same time in a similar way to how an oscilloscope has a "persistant" mode.

Oscilloscope Diplay

I can probably cobble together something that uses histograms but am hoping there is pre-existing or more elegant solution to this problem.

I want to use the data from 1000 lines to produce one plot, like in the oscilloscope plot, it shows many updates at the same time, by the use of the colours it shows where more of these wave-forms are "stacked" (i.e. the red parts) and where there is the occasional difference or glitch in waveforms (i.e. the blue parts) — Hugoagogo
– Hugoagogo, Commented Apr 29, 2014 at 5:49
will this be helpful? mathworks.com/help/comm/ref/commscope.eyediagram.html — bla
– bla, Commented Apr 29, 2014 at 6:08

Acorbe · Accepted Answer · 2014-04-29 06:55:54Z

You can do exactly what you are suggesting yourself, i.e. plotting the heatmap of the signals.

Consider the following: I'll build a test signals (out of sine waves of different amplitude), then I'll plot the heatmap via hist3 and imagesc.

The idea is to build an auxiliary signal which is just the juxtaposition of all your time histories (both in x and y), then extract basic bivariate statistics out of that.

 % # Test signals xx = 0 : .01 : 2* pi; center = 1; eps_ = .2; amps = linspace(center - eps_ , center + eps_ , 100 ); % # the auxiliary signal will be stored in the following variables yy = []; xx_f = []; for A = amps xx_f = [xx_f,xx]; yy = [yy A*sin(xx)]; end % # final heat map colormap(hot) [N,C] = hist3([xx_f' yy'],[100 100]); imagesc(C{1},C{2},N')

enter image description here

You can use also jet colormap instead of hot colormap for readability. In the following the amplitude is gaussian instead of homogeneus.

enter image description here

Thanks this was just what I was looking for, I had looked at hist3 briefly but wasnt sure how to put the pieces together. Before I accept the answer, is there any way besides interpolation that can be used to add more data to the heat map when there is a rapid change in the signal, resulting in the line dissapearing.
@Hugoagogo, I see your problem. I guess interpolation is a neat way to deal with the problem. Nonetheless, you must increase time resolution by interpolation at every time and not just where you have the problem, otherwise the bivariate stat will get ruined.
I found this code example mathworks.com.au/matlabcentral/answers/… that seems to work well enough for my purposes. Marking this answer as accepted
Another important note is appending all the points in each file as in the example lead to memory issues as well as large slowdowns when the array was reallocated. I was able to get a large speed by precalculating bin centers, calculating the histogram for each file with those centers and adding the results together, this prevented ever increasing memory consumption.

bla · Accepted Answer · 2014-04-29 08:34:39Z

here's a "primitive" solution that is just using hist:

%# generate some fake data x=-8:0.01:8; y=10*sinc(x); yy=bsxfun(@plus,y,0.1*randn(numel(x),1000)' ); yy(randi(1000,1,200),:)= 5-randi(10)+ circshift(yy(randi(1000,1,200),:),[1 randi(numel(x),1,200)]); %# get plot limit parameters plot(x,yy) yl=get(gca,'Ylim'); xl=get(gca,'Xlim'); close all; %# set 2-d histogram ranges ybins=100; xbins=numel(x); yrange=linspace(yl(1),yl(2),ybins); xrange=linspace(xl(1),xl(2),xbins); %# prealocate m=zeros(numel(yrange),numel(xrange)); % build 2d hist for n=1:numel(x) ind=hist(yy(:,n),yrange); m(:,n)=m(:,n)+ind(:); end imagesc(xrange,yrange,m) set(gca,'Ydir','normal')

enter image description here

This is the approach I was considering and works great, the solution @Acorbe provides is I think a nice more elegant solution so I have accepted that answer. Your sample data is better though.

Roman · Accepted Answer · 2014-04-29 11:18:38Z

Why don't you normalize the data and then add all the lines together? You could then plot the heatmap from the single datafile.

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