Making a contour plot from x and y data

I have a two column vector containing x and y values. The y values have a high accuracy (many decimal points).

x ranges from 0 to 50000 and y from 0 to 14.

I need to use contourf to make a 2d representation of this data.

How can this be done?

[jonas]

I need to use contourf to make a 2d representation of this data.

What do you mean by x and y - data? contourf is a 2d representation of three dimensional data. Perhaps you are missing an array?

[Adam Danz]

Contour plots are 2D representations of 3D data. x and y define the first 2 dimensions but what will define the 'height' or the 3rd dimension of your contour plot? Are you looking for a bivariate distribution of your (x,y) values (histogram2)?

Check out the first example of the contour function to get a sense what it does.

https://www.mathworks.com/help/matlab/ref/contour.html#mw_96c26d96-2a02-4e5f-a593-6d711e9e1083

[Adam Danz]

Ha! Nearly identical comments written simultaneously.

[Markus Similä]

I need to show the density of the data points. Sorry, should have been more specific.

[KSSV]

Share your data.

[Markus Similä]

The data looks something like this. There are a lot more data points (millions).

Oops, I missed a zero on the range of the x.

35847 1.547

6062 2.172

115733 2.282

33021 1.760

35769 1.772

35872 2.030

55739 2.298

55266 0.693

55348 0.693

1409 1.605

1685 2.059

33735 1.615

50968 2.043

60111 1.637

74971 2.217

55397 1.431

1810 1.321

54242 2.581

35771 0.944

2118 1.782

34322 2.127

1943 1.743

1758 1.721

168417 0.583

129397 1.737

76477 1.624

91810 1.150

76881 1.669

223104 1.834

4048 2.066

5170 1.524

Answer 1

[Adam Danz]

The bivariate density can be computed using histcounts2. You'll need to specify either the number of bins or the bin edges for the x and y variables using one of the syntaxes below.

• [N,Xedges,Yedges] = histcounts2(X,Y,nbins)
• [N,Xedges,Yedges] = histcounts2(X,Y,Xedges,Yedges)

Here's what the solution will look like. xy is your nx2 matrix of [x,y] values.

[N,Xedges,Yedges] = histcounts2(xy(:,1), xy(:,2), 5);
% or use [N,Xedges,Yedges] = histcounts2(X,Y,Xedges,Yedges);

% Compute bin centers
Xcnt = Xedges(2:end) - abs(diff(Xedges(1:2))/2); 
Ycnt = Yedges(2:end) - abs(diff(Yedges(1:2))/2); 

figure()
contour(Xcnt,Ycnt, N)

% show bins
xlim = [min(Xedges),max(Xedges)]; 
ylim = [min(Yedges),max(Yedges)]; 
arrayfun(@(x)xline(x,'Color',[.8 .8 .8]),Xedges)
arrayfun(@(y)yline(y,'Color',[.8 .8 .8]),Yedges)

% colorbar
cb = colorbar(); 
ylabel(cb,'density')

[Markus Similä]

Thank you!

This is what I was trying to do as well.

Answer 2

[KSSV]

I would follow like below:

1. You need to generate z values which are density values.
2. I would fix a distance r and calculate the number of points within r for each point using rangesearch.
3. Now you can use scatter to plot the density values.
4. If your data is structured arrange the data and use contourf.