[G]allopingTopiary

import Rhino
import rhinoscriptsyntax as rs
import scriptcontext
from System.Drawing import Color
import random as rnd
import time
"""--------------------------------------------------------
PERFORMANCE TEST - RTREE CLASS RHINOCOMMON
Repeated Search To Find Closest Neighbor
Search Sample = 1 Million Points
Search Radius = 500 Units
Search Space = Cube (5000,5000,5000)
--------------------------------------------------------"""
class SearchData:
    def __init__(self, pts, point):
        #Search Data class set up for instancing repeaded searching and tracking
        self.HitCount = 0 #amount of times we searched starts with 0
        self.arrPts = pts
        self.Point = point
        self.Index = -1 #insuring that we reduce the search radius after the first pass, see the conditional in searchcallback
        self.Distance = 0
        self.count = 0
def SearchCallback(sender, e):
    data = e.Tag
    data.HitCount += 1 #incrementing the hitcount
    vertex = data.arrPts[e.Id] #the current closest neighbor
    distance = data.Point.DistanceTo(vertex) #distance to the current closest neighbor from the query loc to define new search radius
    if data.Index == -1 or data.Distance > distance: #condition to resize the search sphere to optimize the searching
        e.SearchSphere = Rhino.Geometry.Sphere(data.Point, distance) #resize the search sphere
        data.Index = e.Id #update the data.index to current point ID
        data.Distance = distance #update the data.distance to the current distance
def RunSearch():
    pttimeStart = time.time() #Start Timer
    point = Rhino.Geometry.Point3d(5000/2,5000/2,5000/2)
    intNumPts = 1000000 #specify the number of sample points to search 
    pts = []
    tree = Rhino.Geometry.RTree() #create a new instance of the rtree class
    for x in range(0,intNumPts):
        randomPt = Rhino.Geometry.Point3d(rnd.random()*50000,rnd.random()*50000,rnd.random()*50000) #randomly place each point inside the 5k x 5k x 5k cube
        pts.append(randomPt) #add the current point to the end of the pts list
        tree.Insert(randomPt,x) #insert the current random point in the RTree at the specified index (x)
    pttimeEnd = time.time() #end the timer
    print "time taken to create pts: {}".format(pttimeEnd - pttimeStart) #print amount of time it took for calculations
    print "TreeCount = {}".format(tree.Count) #count of the amount of items in the tree
    timeStart = time.time() #Start Timer
    data = SearchData(pts, point) #Create an instance of the Search Data Class passing sample list, query loc
    sphere = Rhino.Geometry.Sphere(point, 500) #specify the inital search sphere size
    if tree.Search(sphere, SearchCallback, data): #call the search method from the Rtree Class 
        print "Found point in {0} tests".format(data.HitCount) #print the amount of search iterations we did
        print "ClosestPt = {}".format(data.arrPts[data.Index]) #print the closest neighbor
    timeEnd = time.time() #end the timer
    print "time taken: {}".format(timeEnd - timeStart) #print amount of time it took for calculations
if __name__=="__main__":
    RunSearch()
Update

import Rhino
import rhinoscriptsyntax as rs
import scriptcontext
from System.Drawing import Color
import random as rnd
import time
"""--------------------------------------------------------
RHINOCOMMON METHODS
One Time Search to find all points within specified radius
The points found inside the specified radius get sampled for closest neighbor
No Repeat Search
Search Sample = 1 Million Points
Search Radius = 500 Units
Search Space = Cube (5000,5000,5000)
--------------------------------------------------------"""
def closestSearch(pts, point):
    #function to sample point based on a search radius first then search for the closest inside that list mimic the rtree pass basically but with brute force
    ptsinRange = [] #dynamic list to store points found inside initial search
    for i, pt in enumerate(pts):
        dist = point.DistanceTo(pt) #find the distance from each point to the query point
        if dist < 500: 
            ptsinRange.append(pt) #if the distance is less than 500 append that point to the ptsinRange List
    cPoint = Rhino.Collections.Point3dList.ClosestPointInList(ptsinRange,point) #search for the closest point from that list of points in range
    if cPoint[0] == point[0] and cPoint[1] == point[1] and cPoint[2] == point[1]: #test against the closest neighbor being you
        print "IS THE SAME" 
    return cPoint,ptsinRange #return the closest neighbor and the list of points in range
def RunSearch():
    point = Rhino.Geometry.Point3d(5000/2,5000/2,5000/2)
    intNumPts = 1000000 #specify number of sample points
    pts = [] #store the random points in an empty list
    for x in range(0,intNumPts):
        randomPt = Rhino.Geometry.Point3d(rnd.random()*5000,rnd.random()*5000,rnd.random()*5000) #randomly place each point inside the 5k x 5k x 5k cube
        pts.append(randomPt) #append each random point to the pts list
    timeStart = time.time() #Start timer
    data = closestSearch(pts, point) #call closestsearch function Pass query pt and list of points to search
    print "ClosestPoint = {}".format(data[0]) #print the closest neighbor
    print "Number of Pts in Range = {}".format(len(data[1])) #print how many points were captures inside the search radius
    timeEnd = time.time() #stop the timer
    print "time taken: {}".format(timeEnd - timeStart) #print the amount of time the calculations took
if __name__=="__main__":
    RunSearch()
Update

import Rhino
import rhinoscriptsyntax as rs
import scriptcontext
from System.Drawing import Color
import random as rnd
import time
"""--------------------------------------------------------
RHINOSCRIPT METHODS USING PYTHON
One Time Search to find all points within specified radius
The points found inside the specified radius get sampled for closest neighbor
No Repeat Search
Search Sample = 1 Million Points
Search Radius = 500 Units
Search Space = Cube (5000,5000,5000)
--------------------------------------------------------"""
def closestSearch(pts, point):
    #function to sample point based on a search radius first then search for the closest inside that list mimic the rtree pass basically but with brute force
    ptsinRange = []#dynamic list to store points found inside initial search
    for i in range(len(pts)):
        dist = rs.Distance(pts[i],point)#find the distance from each point to the query point
        if dist < 500:
            ptsinRange.append(pts[i])#if the distance is less than 500 append that point to the ptsinRange List
    cPointIndex = rs.PointArrayClosestPoint(ptsinRange,point)#search for the closest index from that list of points in range
    cPoint = ptsinRange[cPointIndex] #find the closest point in the in range list from the index found above
    if cPoint[0] == point[0] and cPoint[1] == point[1] and cPoint[2] == point[1]:#test against the closest neighbor being you
        print "IS THE SAME"
    return cPoint,ptsinRange #return the closest neighbor and the list of points in range
def main():
    point = [5000/2,5000/2,5000/2]
    intNumPts = 1000000#specify number of sample points
    pts = []#store the random points in an empty list
    for x in range(0,intNumPts):
        randomPt = [rnd.random()*5000,rnd.random()*5000,rnd.random()*5000] #randomly place each point inside the 5k x 5k x 5k cube
        pts.append(randomPt)#append each random point to the pts list
    timeStart = time.time()#Start timer
    data = closestSearch(pts, point)#call closestsearch function Pass query pt and list of points to search
    print "ClosestPoint = {}".format(data[0])#print the closest neighbor
    print "Number of Pts in Range = {}".format(len(data[1]))#print how many points were captures inside the search radius
    timeEnd = time.time()#stop the timer
    print "time taken: {}".format(timeEnd - timeStart)#print the amount of time the calculations took
main()
Update