+60k Lines on one plot - Too slow
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Ömer Yaman
el 25 de Jun. de 2022
Comentada: Ömer Yaman
el 27 de Jun. de 2022
Dear all,
I need to plot more than 60k lines. I'm doing it in a for loop. It takes too much time, about 170 seconds with the rest of the code. Is there any quicker way?
And also saveas(fig ... command takes too much time as well. About 60 seconds. I would like to save my plot as a png. file.
What are your suggestions to reduce time cost.
Best Regards,
4 comentarios
Adam Danz
el 25 de Jun. de 2022
It's hard to imagine how 60k lines would be useful in a visualization and we may be able to suggest alternative approaches, or, perhaps, it will make instance sense to us why 60k lines is useful.
If your plotting inputs are vectors, it looks like you could compute all of the y values (slopes+energy+slopes) and store the results in a matrix and the plot it all at once using the template line below. However, that will still produce 60k line objects while will be painfully slow.
h = plot(1:5, rand(10,5))
Respuesta aceptada
Adam Danz
el 25 de Jun. de 2022
Editada: Adam Danz
el 25 de Jun. de 2022
Pros:
- 1 graphics object, light weight plot
- simple to compute & simple to plot
- you can see the density patterns created by overlapping lines etc.
- If there is a strong median curve, you'll see it in the plot
Cons:
- Single curves won't appear in the plot (but you wouldn't see many single curves anyway with 60k lines)
- Outliers may become difficult or impossible to see
Example
Create 10k line series that follow a general pattern with varying amounts of noise. I'll use the same x-coordinates but this works just as well if the x-values vary too.
gaus = @(x,mu,sig,amp,vo)amp*exp(-(((x-mu).^2)/(2*sig.^2)))+vo;
x = 0:850;
n = 1e4;
y = zeros(n,numel(x));
rng('default') % to reproduce these results
for i = 1:n
mu = rand(1)*175+300;
sig = rand(1)*100+150;
amp = randn(1)*20+30;
vo = rand(1)*10;
y(i,:) = gaus(x,mu,sig,amp,vo);
end
Now, let's plot 50 random lines to see what they look like. Use randperm to do the random selection.
figure
m = 50;
selection = randperm(n,m);
plot(x, y(selection,:))
title(sprintf('%d random curves from %d',m,n))
axis tight
% Replicate the X-vector (skip this if your x values vary for each curve)
xRep = repmat(x, n, 1);
% plot the bivariate historgram
figure
h = histogram2(xRep(:),y(:),'DisplayStyle','tile','ShowEmptyBins','on');
title('Bivariate density of curves')
cb = colorbar();
ylabel(cb,'Number of curves')
Alternatively, you could turn off the empty bins to see the shape of the entire dataset
figure
h = histogram2(xRep(:),y(:),'DisplayStyle','tile');
title('...ignoring empty bins')
cb = colorbar();
ylabel(cb,'Number of curves')
2 comentarios
dpb
el 26 de Jun. de 2022
@Adam Danz, that's a very good idea for such large datasets, indeed. I've used such in the past as well but didn't think to suggest it here...
Más respuestas (2)
dpb
el 25 de Jun. de 2022
Editada: dpb
el 25 de Jun. de 2022
This is way too much data to plot on single figure realistically, of course it's going to take time.
But, the use of a loop and plot is the slowest way possible -- you're computing/drawing every single line individually instead of using the power of MATLAB in being vectorized...
y=[slopes(:,1)*energy+slopes(i,2)].'; % generate y(i,j) by column
hL=plot(energy,y); % plot the result
Here on a not terribly upscale system,
>> tic,y=m*e;toc
Elapsed time is 0.001305 seconds.
>> tic,hL=plot(e,y);toc
Elapsed time is 15.350194 seconds.
>> delete(hL)
>> close
although it took the renderer quite some additional time to actually draw and display the figure -- and at least as long to delete 60K graphics handles.
Since there are only some 1-2K pixels on a monitor anyway, 60K points is some 60X the possible density to be able to display; I would strongly suggest decimating the y array by at least a factor of 10 and likely you'll be unable to see any difference in the result at factors approaching 100X.
Image Analyst
el 25 de Jun. de 2022
Plot just a subsample of the curves. You'll never be able to see all 60k lines anyway, so just pick, say 1000 of them at random and plot those. You'll probably still get the general idea of what all the data would look like if you could have plotted them.
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