Deformed surf using griddata ?
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I have a set of data as attached. but when I surf them after using griddata. the result is deformed surf AS SHOWN IN THE PICTURE with red squares. Z values on the surf were supposed to follow the edges of the shape. I think its because there is something wrong with (z_surf) matrix in my code. But I don't know how to solve it.
Hope you can help me to solve this problem.
Thank you.
x_limit=xyz_board_ordered_dvd(:,1);
y_limit=xyz_board_ordered_dvd(:,2);
z_limit=xyz_board_ordered_dvd(:,3);
dx=1;
dy=1;
x_edge=floor(min(x_limit)):dx:ceil(max(x_limit));
y_edge=floor(min(y_limit)):dy:ceil(max(y_limit));
[x_surf,y_surf]=meshgrid(x_edge,y_edge);
z_surf=griddata(x_limit,y_limit,z_limit,x_surf,y_surf);
surf(x_surf,y_surf,z_surf)
xlabel('X')
ylabel('Y')
xlim([0 120])
ylim([0 120])
1 个评论
Walter Roberson
2017-8-24
I notice that only 554 of the 604 points are unique.
采纳的回答
John D'Errico
2017-8-24
编辑：John D'Errico
2017-8-24
Easy, peasy.
Griddata will fail here. In fact, my own gridfit will produce some artifacts.
But essentially, you have a simple ribbon of the form z(y), which seems to be independent of x.
x = xyzSTL_board_ordered_dvd(:,1);
y = xyzSTL_board_ordered_dvd(:,2);
z = xyzSTL_board_ordered_dvd(:,3);
plot(y,z,'o')
There is NO x in that relationship. So all you need to do is fit the curve z(y). Use a spline. Start out by removing any replicates. Using my consolidator tool, as found on the file exchange :
[yhat,zhat] = consolidator(y,z,@mean);
plot(yhat,zhat,'-')
spl = pchip(yhat,zhat);
Use pchip here. This is fairly important.
[xgrid,ygrid] = ndgrid(linspace(min(x),max(x),10),linspace(min(y),max(y),100));
zgrid = repmat(ppval(spl,linspace(min(y),max(y),100)),10,1);
surf(xgrid,ygrid,zgrid)
8 个评论
Walter Roberson
2017-8-24
I am getting a bit bogged down in figuring out good ways to automatically determine the proper pseudo-plane for the case where the values do not happen to sit nicely on the axes. The data looks like a scan of something and I am thinking about the possibility of scans where the object was not nicely aligned.
Faez Alkadi
2017-8-24
编辑：Faez Alkadi
2017-8-24
John D'Errico
2017-8-25
If I plot the data you gave us, there is NO dependency on x. NONE AT ALL. So are you asking what to do if you have a completely different problem from the one you asked us about, based on the data you gave us? You solve it in a different way.
So what if you get points in the middle that do not lie as simply as these do? This very much depends on how complex the surface is.
Why you could not use ndgrid, I have no clue, because you do not show what you tried. ndgrid is appropriate for some problems. In other cases, it is ndgrid that is correct. The two are only subtly different. I could have used ndgrid here instead.
John D'Errico
2017-8-25
@Walter - that is an interesting question. If, for example, this set had simply been rotated arbitrarily. It could be worse though, with the points randomly scattered along the length of the curve, as well as random amounts of x, then rotating the whole ribbon in three dimensions. The solution is not obvious at the moment.
Faez Alkadi
2017-8-25
编辑：Faez Alkadi
2017-8-25
Hi John, @Walter I really appreciate your help
here i'm attaching a more general data that has points in the middle making a dome shape. what i'm hoping to find here is to meshgrid in x and y in (dx and dy) incremental values, then surf it with respect to z.
but unfortunately i got the one in the picture which has so many defects. Thank you.
John D'Errico
2017-8-25
The "defects" are there because you have no data in that area. The griddata algorithm breaks the problem into a triangulation in (x,y). If you have no data in an area, then any triangle will be quite large. In other areas, your data was quite dense. Dense data means small triangles, so no problems in those other areas.
So if you want to get rid of the "defects" then you need to fill in additional data points in those large holes. Just because your brain can visualize something does not mean a computer can easily produce the same.
Stephen23
2017-8-25
Another option would be to fill "missing data" areas with NaNs, which will not be plotted.
Faez Alkadi
2017-8-25
编辑：Faez Alkadi
2017-8-25
更多回答（1 个）
Walter Roberson
2017-8-24
0 个投票
You can get closer to what you want if you add the 'nearest' option to the griddata() call.
You will get an odd artifact, which I figure is due to peculiarities about which is the "closest" point in 3-space.
There is no reason for the surface to follow the edge using griddata(). Gridding takes place in 3-space, so when you get sufficiently far from the edges, the closest points might be from a different z than you were picturing.
If you have a surface that you know should be a ribbon, then there are ways to decompose that.
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