KDT Closest Verts

Functionality

For every vertex in Verts, it checks the list of Vertices in Check Verts. What it does exactly depends on the Search Mode. Search modes are mere examples of what is possible with Blender’s KDTree module. The documentation for kdtree is found at the latest version of mathutils.kdtree.html.

Inputs

Mode	Input Name	type
All	Verts	vertices
	Check Verts	vertices
N	N	int, or list of ints
Radius	Radius	float, or list of floats

Verts and Check Verts do not need to be the same pool of verts, they don’t even need to be the same length.

Parameters

Search Mode	Description
1	for each vertex in Verts return the vertex in Check Verts which is closest.
N	for each vertex in Verts return the list of N closest vertices found in Check Verts
Radius	for each vertex in Verts return the vertices of Check Verts that are found within radius-distance of that vertex.

Outputs

The meaning of each output differs between Modes, but essentially they are:

• Vertices coordinates

• Vertex Indices of related vertex in Check Verts

• Vertex Distance between Vertex in Verts and Check Verts

The output lists will be nested if the Mode allows multiple outputs, as is the case in N and Radius Mode.

Examples

All kinds of crazy things are possible, see some examples here in the development thread

---

• Generator-> Plane

• Generator-> Circle

• Transform-> Rotate

• Transform-> Noise Displace

• Number-> A Number

• Viz-> Viewer Draw

• Text-> Stethoscope

---

• Generator-> Plane

• Generator-> Circle

• Transform-> Rotate

• Number-> A Number

• Viz-> Viewer Draw

• Text-> Stethoscope

---

Notes

Design specs

'''
find(co)
    : internal function
    : < Find nearest point to co
    : > returns co, index, dist

    : inputs:
        1) Main Verts for kdtree to hold
        2) [cVert(s)] to check against
    : outputs:
        1) [Verts.co] from Main Verts that were closest
        2) [Verts.idx] from Main Verts that were closest


find_n(co, n)
    : internal function
    : > Find nearest n points to co
    : < returns iterable of (co, index, dist)

    : inputs:
        1) Main Verts for kdtree to hold
        2) [cVert(s)] to check against (size don't have to match)
        3) n, max n nearest
        optional?
        1) mask, [0, 0, 1, 0, 1]  (return 3rd and 5th closest)
        2) range clamp, [2:] (don't return first 2 closest)
    : outputs:
        for v in cVerts:
        1) ([Verts.co],..) from Main Verts closest to v.co
        2) ([Verts.idx],..) from Main Verts closest to v.co
        optional!
        1) could generate edges directly (Saves node noodle)


find_range(co, radius)
    : > Find all points within radius of co
    : < returns iterable of (co, index, dist)

    : inputs:
        1) Main Verts for kdtree to hold
        2) [cVert(s)] to check against (size don't have to match)
        3) [distance(s)] ,

    : outputs:
        options:
        1) grouped [.co for points in Main Verts in radius of v in cVert]
        2) grouped [.idx for points in Main Verts in radius of v in cVert]
        3) grouped [.dist for points in Main Verts in radius of v in cVert]

'''

If you need large kdtree searches and memoization or specific functionality you shall want to write your own Node to utilize the kdtree module. Part of the problem of making a general use node is that it becomes sub-optimal for certain tasks. On the up-side, having this node allows you to rip out the specifics and implement your own more specialized kdtree node. Recommend using a different Node name and sharing it with team Sverchok :)