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    <p>You've got a couple of options, depending on your circumstances. You can either sort the data to prevent the discontinuities, or you can insert a few <code>NaN</code>s into the X and Y vectors.</p> <ol> <li><p>To sort:</p> <pre><code>[xSorted, ixsSort] = sort(x); ySorted = y(ixSort); plot(xSorted, ySorted); </code></pre></li> <li><p>To add nans, you need to do some additional work to determine where the breaks should be, and then insert NaNs. For example, to break the line after the 10th entry</p> <pre><code>xBroken = [x(1:10) nan x(11:end)]; yBroken = [y(1:10) nan y(11:end)]; plot(xBroken, yBroken); </code></pre> <p>Edit: see sample code below for a more comprehensive example.</p></li> <li><p>Of course, as an easy backup, just do a scatter plot instead of a line plot:</p> <pre><code>plot(x, y, '.'); </code></pre></li> </ol> <hr> <p>Some sample code for method 2 below:</p> <pre><code>%Some sample data x = [1:10 2.1:11 3.2:12]; y = randn(size(x)); %Define where breaks are needed (and associated boundaries) ixsBreaksNeeded = find(diff(x)&lt;0); ixsSegmentBoundaries = [0 ixsBreaksNeeded length(x)]; %This makes the iterations a lot easier %Predefine some nan vectors to move data into xBroken = nan(1, length(x) + length(ixsBreaksNeeded)); yBroken = nan(1, length(x) + length(ixsBreaksNeeded)); %Move data segments into nan vectors, leaving gaps between segments ixOffset = 0; for ix = 2:length(ixsSegmentBoundaries) ixsOriginal = (ixsSegmentBoundaries(ix-1)+1):ixsSegmentBoundaries(ix); xBroken(ixsOriginal + (ixOffset)) = x(ixsOriginal); yBroken(ixsOriginal + (ixOffset)) = y(ixsOriginal); ixOffset = ixOffset+1; end %Plot to demonstrate subplot(211) plot(x,y); subplot(212) plot(xBroken, yBroken) </code></pre>
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    1. POIs there a way to fix this end effect of plotting in Matlab?
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    1. COThese are very good suggestions, the 1st and 3rd of which I have tried and do not work for my data due to it being a time series and also because it is sampled so that it is not clear when scattered. I also dont understand what you mean by your 2nd suggestion.
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    2. COFor approach 2, to find the points in your time series where the time jumps backwards, use `find(diff(t) < 0)' and instert NaNs at these locations
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