def buildRtree(dataSetName, *B):
global root
global Bvalue
Bvalue = 25
if len(B) == 1 and B[0] != None:
Bvalue = B[0]
f = open(dataSetName, 'rt')
nextLine = f.readline()
# size of date set
size = int(nextLine.strip('\n'))
# read the first point and build a root
nextLine = f.readline()
point = Rtree.Point(scanRange.getPoint(nextLine))
root = Rtree.Leaf(Bvalue, 1, point)
root.addChild(point)
# add the remained points
nextLine = f.readline()
while nextLine != '':
point = Rtree.Point(scanRange.getPoint(nextLine))
insert(root, point)
nextLine = f.readline()
f.close()
print('R-tree has been built. B is:', Bvalue, 'Highest level is:',
root.level)
return root
def buildRtree(dataSetName, *B):
global root
global Bvalue
Bvalue = 25 # Upper limit of the node, initial 25.
if len(B) == 1 and B[0] != None:
Bvalue = B[0]
f = open(dataSetName, 'rt')
nextLine = f.readline()
# size of date set
size = int(nextLine.strip('\n'))
# read the first point and build a root
nextLine = f.readline()
point = Rtree.Point(getPoint(nextLine))
root = Rtree.Leaf(Bvalue, 1, point)
root.addChild(point)
# add the remained points
nextLine = f.readline()
while nextLine == '\n':
nextLine = f.readline()
while nextLine != '':
point = Rtree.Point(getPoint(nextLine))
insert(root, point)
nextLine = f.readline()
while nextLine == '\n':
nextLine = f.readline()
f.close()
maintain(root)
print('R-tree has been built. B is:', Bvalue,'. Highest level is:',root.level)
'''print('第1层： ',root.attribute,' ',root.bitmap)
for child in root.childList:
print('第2层： ',child.attribute,' ',child.bitmap)
for cc in child.childList:
print('第3层： ', cc.attribute, ' ', cc.bitmap)
for ccc in child.childList:
print('第4层： ', ccc.attribute, ' ', ccc.bitmap)
for aaa in ccc.childList:
print(aaa.x,' ',aaa.y,' ',aaa.attribute,aaa.bitmap)'''
return root
def handleOverFlow(node):
global root
global Bvalue
# split node into two new nodes
nodes = node.split()
# if root node is overflow, new root need to build
if node.paren == None:
root = Rtree.Branch(Bvalue, node.level + 1, nodes[0])
root.addChild(nodes[0])
root.addChild(nodes[1])
root.childList[0].paren = root
root.childList[1].paren = root
else:
# update the parent node
parent = node.paren
parent.childList.remove(node)
parent.childList += nodes
# check whether parent node is overflow
if parent.isOverFlow():
handleOverFlow(parent)
def basicTest():
gen = MbrGenerator()
mTree = Rtree(d=4, M=25, maxE=10 ** 6, reset=True, initOffset=0, partitionType=0)
resFileName = "../results/Resultados Test.txt"
f = open(resFileName, 'a+')
f.write(datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))
f.write(" d=%d M=%d partitionType=%d\n" % (4, 25, 0))
f.write("meanPartitionsPerNode meanInsertTime meanTotalNodes meanInternalNodes\n")
# Construyo el Rtree con los elementos
for s in range (10 ** 3):
mTree.insert(gen.next(d=4))
f.write("%f %f %f %f\n" % (mTree.getMeanNodePartitions(), mTree.meanInsertionTime, mTree.meanTotalNodes, mTree.meanInternalNodes))
f.write("meanVisited meanQueryTime\n")
f.close()
fileName = "../results/busqueda test " + str(mTree.treeType) + " M" + str(mTree.M()) + " d" + str(mTree.d()) + ".bin"
mTree = Rtree(d=4, M=25, maxE=10 ** 6, reset=False, initOffset=0, partitionType=0)
# Construyo los elementos de consulta
f = open(resFileName, 'a+')
fb = open(fileName, 'a+')
for s in range (100):
mTree.search(gen.nextRadial(d=4, r=0.25 * 2), fb,False, True)
f.write("%f %f\n" % (mTree.getMeanVisitedNodes(), mTree.meanSearchTime))
f.close()
fb.close()
from Rtree import *
from random import uniform
from time import time
import csv
root = Rtree(m=3, M=7)
stations = []
i = 0
with open("2013NRSC.csv", "rU") as csvfile:
radioCsv = csv.reader(csvfile, delimiter=",", quotechar='"')
for station in radioCsv:
y = float(station[7])
x = float(station[8])
stations.append(
node(
MBR={
"xmin": x - 0.01,
"xmax": x + 0.01,
"ymin": y - 0.01,
"ymax": y + 0.01,
},
index=i,
))
i = i + 1
for j in range(i):
root = Insert(root, stations[j])
print(type(root))
print(root.leaves)
#!/usr/bin/python
# -*- coding: utf-8 -*-
from Rtree import *
from random import uniform
from time import time
numRuns = 1000
data = {}
# Initialize 10000 coordinates (-1000, 1000), an area of a rectangle 0.01.
for i in range(numRuns):
x = uniform(-1000, 1000)
y = uniform (-1000, 1000)
data [i] = {'xmin': x, 'xmax': x + 0.01, 'ymin': y, 'ymax': y + 0.01}
# Set a root node, m = 3, M = 7
root = Rtree(m = 3, M = 7)
n = []
for i in range(numRuns):
n.append(node(MBR = data[i], index = i))
t0 = time()
# Insert
for i in range(numRuns):
root = Insert(root, n[i])
print root
t1 = time()
print 'Inserting ...'
print t1 - t0
#search for
x = root.Search(merge(n[0].MBR, n[1].MBR))
print x
mTree.search(gen.nextRadial(d=4, r=0.25 * 2), fb,False, True)
f.write("%f %f\n" % (mTree.getMeanVisitedNodes(), mTree.meanSearchTime))
f.close()
fb.close()
if __name__ == '__main__':
# basicTest()
resFileName = "../results/Resultados.txt"
for j in range(1, 11):
k = 1
for partitionType in [0, 1]:
# for d in [2, 4, 8, 16]:
for d in [4, 8, 16]:
for M in [50, 100]:
gen = MbrGenerator()
mTree = Rtree(d=d, M=M, maxE=10 ** 6, reset=True, initOffset=0, partitionType=partitionType)
f = open(resFileName, 'a+')
f.write(datetime.datetime.now().strftime("%Y-%m-%d %H:%M"))
f.write(" d=%d M=%d partitionType=%d\n" % (d, M, partitionType))
f.write("meanPartitionsPerNode meanInsertTime meanTotalNodes meanInternalNodes\n")
# Construyo el Rtree con los elementos
for s in range (10 ** 5):
mTree.insert(gen.next(d=d))
f.write("%f %f %f %f\n" % (mTree.getMeanNodePartitions(), mTree.meanInsertionTime, mTree.meanTotalNodes, mTree.meanInternalNodes))
f.write("meanVisited meanQueryTime\n")
print("Insercion n:%d" % k)
k += 1
f.close()
fileName = "../results/busqueda " + str(mTree.treeType) + " M" + str(mTree.M()) + " d" + str(mTree.d()) + ".bin"
# Construyo los elementos de consulta
for radio in [0.25, 0.50, 0.75]: