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JeremyR

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Reply with quote  #1 
question since I cannot get this working from scratch, what makes the weight components grow over time using KDTree? I thought that this was just something the KDTree did as it checked for nearest neighbour stuff and therefore got larger and larger over time?

Also it looks like the weighted verts in your scene are buffered like an AttributeDataContainer would do, is this inbuilt into KDTree?
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pshipkov

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Reply with quote  #2 
Are you saying that you cannot make it work using the provided GUI, or building the underlying nodal networks by yourself ?

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JeremyR

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Reply with quote  #3 
Im good with the UI toolset, it works really well. Its just when I try to recreate the setup myself I cant get it to accumulate the point set over time. I want it to grow.
I dont understand how the KDtree works under the hood I guess.
At the moment I just have one KDtree node but once I get that working Ill work in the second to mimic more of what you have done.

Screen Shot 2017-07-09 at 8.39.47 PM.png 

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pshipkov

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Reply with quote  #4 
I see.
Ok, well, that's not going to work like that.
You will need the entire network reconstructed before it works.
individual parts of it won't work on their own.
Once you have the network in place you need to run on it the "evaluate" (not in front of Maya right now, so the exact Python call escapes me) to make it work.
Because there is a cycle loop that we need to work around - basically read the output of the network and feed it to the input.
In the example scenes there is a script node that is set to a frame change. You need to run the exact command it contains, etc.

As for the kdTree - it is just one component of the framework that provides fast lookup of neighbour points, nothing more than that.
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JeremyR

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Reply with quote  #5 
Okay got it, I totally missed the script node in there. Is there any relevance for the user to be able to see this python script at all? Awesome tool BTW, works so well out of the box.
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pshipkov

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Reply with quote  #6 
Thanks. Glad to hear it works well over there.
I don't think there is any value for the user to see the invoked python code, because it is 100% tied to the underlying nodal network.
With other words it is not applicable to anything else.
What it ]does is to read the nodal graph's output (and this this forcing it to evaluate) and then write the result to the input.
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