How to sort 3D data into bins?
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wkm42
on 4 Jul 2016
Commented: Ramesh Venkatasubramanian
on 27 Oct 2020
Hey,
I'm looking for some function like 'gridfunc' in the example beneath.
Datapoints:
x: torque = [12,13,20,30]
y: speed = [512,800,1300,1506]
z: z = [2,2,1,4]
Intervalls of my desired grid:
torque_intervall = [10,20,30]
speed_intervall = [500,1000,1500,2000]
The function im looking for:
output = gridfunc(torque,speed,z,torque_intervall,speed_intervall)
output(torque=10..20,speed=500..1000)= [z(1),z(2)]
Thanks for any suggestions!
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Accepted Answer
Walter Roberson
on 5 Jul 2016
For each variable, histc() and take the second output, or histcounts() and take the third output. This will be the bin number along that dimension. Now you can use
output = accumarray([firstbinnum(:), secondbinnum(:), thirdbinnum(:)], z(:), [], @(vals) {vals}, {});
The result will be a 3D cell array where the first dimension reflects the bin numbers along the first variable, the second dimension reflects the bin numbers along the second variable, the third dimension reflects the bin number along the third variable. Each element of the cell array will be a cell array of z values that fell into the combination of the three bins; this might be empty if nothing happened to fall into that voxel.
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More Answers (1)
Thorsten
on 4 Jul 2016
Try hist3.
3 Comments
Star Strider
on 4 Jul 2016
It’s going to be very difficult to create any sort of meaningful interpolation surface from your data:
torque = [12,13,20,30];
speed = [512,800,1300,1506];
z = [2,2,1,4];
figure(1)
stem3(torque, speed, z)
grid on
axis square
view([-60, 30])
xlabel('Torque')
ylabel('Speed')
zlabel('Z')
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