Ways to shade points-delimited area or smooth the edge

Hi,

I have a plot created from a bunch of points, that looks like this:

The code looks like this:

for k = 1:size(Points,1)
    if (certain condition regarding Points' third column)
        plot(Points(k,1),surfacePoints(k,2),'.k');
    end
end

I am looking for a way to make the whole things smoother. Ideally, it would have no gaps and the outer edge would look smooth. However, creating that many points to achieve that takes too much time, so I wonder if you know of any other plotting tool that would help me obtain the same results.

Thanks!

[Adam Danz]

Not sure how your data are organized but by judging your for-loop, it seems that you could create a matrix of y-values where each column corresponds to an x-coordinate (so, plotting column 'n' would results in a vertical stack of points). You can use NaN values as fillers for columns with less data. Then, all you have to do is find the minimum y-value within each column (which is easy like sunday morning). You'll end up with a vector of y values and a vector of the associated x values which you can then plot as a line or interpolate to finer resolution.

[darova]

+1

[Mitsu]

Thank you Adam, doing this I obtain the figure below. Now that I have the points (either max or min in blue and red), what would you use to make it a smoother line, without adding more points?

[Adam Danz]

You could either smooth it out using smooth() or another method.

https://www.mathworks.com/help/curvefit/smoothing-data.html

Also check out the spline() function.

Or you could fit the blue data points to a sin function (or a sum of sines) if you expect that form.

[Mitsu]

Thank you, Adam, that did it!

[Adam Danz]

Great! I copied the solution to the answers section so your question appears as answered.

Answer 1

[Adam Danz]

You could create a matrix of y-values where each column corresponds to an x-coordinate (so, plotting column 'n' would results in a vertical stack of points). You can use NaN values as fillers for columns with less data. Then, all you have to do is find the minimum y-value within each column (which is easy like sunday morning). You'll end up with a vector of y values and a vector of the associated x values which you can then plot as a line or interpolate to finer resolution.

You could either smooth it out using smooth() or another method.

https://www.mathworks.com/help/curvefit/smoothing-data.html

Also check out the spline() function.

Or you could fit the blue data points to a sin function (or a sum of sines) if you expect that form.