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stefiosif
2022-04-02 20:16:12 +03:00
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commit 9a8dfdb2c1
9 changed files with 2 additions and 496616 deletions
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NNqueries.txt
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README.md Normal file
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Rqueries.txt
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bulkloading.py
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from os import write
import sys
import csv
_DIVISORS = [180.0 / 2 ** n for n in range(32)]
class MBR:
def __init__(self, id, xlow, xhigh, ylow, yhigh):
self.xlow = xlow
self.xhigh = xhigh
self.ylow = ylow
self.yhigh = yhigh
self.id = id
self.zcurve = self.findZcurve()
def findZcurve(self):
x_median = (self.xlow + self.xhigh) / 2
y_median = (self.ylow + self.yhigh) / 2
return interleave_latlng(y_median, x_median)
def interleave_latlng(lat, lng):
if lng > 180:
x = (lng % 180) + 180.0
elif lng < -180:
x = (-((-lng) % 180)) + 180.0
else:
x = lng + 180.0
if lat > 90:
y = (lat % 90) + 90.0
elif lat < -90:
y = (-((-lat) % 90)) + 90.0
else:
y = lat + 90.0
morton_code = ""
for dx in _DIVISORS:
digit = 0
if y >= dx:
digit |= 2
y -= dx
if x >= dx:
digit |= 1
x -= dx
morton_code += str(digit)
return morton_code
# Given a set of coordinates find the MBR
def findMBR(objId, points):
x_min = min(points, key=lambda p: p[0])[0]
x_max = max(points, key=lambda p: p[0])[0]
y_min = min(points, key=lambda p: p[1])[1]
y_max = max(points, key=lambda p: p[1])[1]
return MBR(objId, x_min, x_max, y_min, y_max)
# Given a set of MBRs find the MBR
def createMBR(nodeId, mbrs):
x_min = min(mbr.xlow for mbr in mbrs)
x_max = max(mbr.xhigh for mbr in mbrs)
y_min = min(mbr.ylow for mbr in mbrs)
y_max = max(mbr.yhigh for mbr in mbrs)
return MBR(nodeId, x_min, x_max, y_min, y_max)
# Read data from input files
def inputReader(filename1, filename2):
set_of_points = []
with open(filename1, 'r') as offsets, open(filename2, 'r') as coords:
reader = csv.reader(offsets, delimiter=',', quoting=csv.QUOTE_NONE)
for row in reader:
points = []
for i in range(int(row[2]) - int(row[1]) + 1):
points.append([float(x) for x in coords.readline().split(',')])
set_of_points.append([row[0], points])
return set_of_points
# Write data into output file
def outputWriter(filename, rTree):
with open(filename, 'w') as rtree:
numNode = 0
leaves = 0
for level in rTree:
for node in level:
rtree.write("[{}, {}, [[".format(leaves, numNode))
for i in range(len(node) - 1):
rtree.write("{}, [{}, {}, {}, {}]],[".format(
node[i].id, node[i].xlow, node[i].xhigh, node[i].ylow, node[i].yhigh))
rtree.write("{}, [{}, {}, {}, {}]]]]".format(
node[-1].id, node[-1].xlow, node[-1].xhigh, node[-1].ylow, node[-1].yhigh))
rtree.write("\n")
numNode += 1
leaves = 1
#
def makeRtree(collection):
level = [] # list of levels(of nodes) of nodes(of mbrs) of mbrs(of points)
nodeId = 0
rank = 0
# Until we reach the root node
while len(collection) > 1:
# Split the mbr collection into nodes of 20
nodes = [collection[x:x+20] for x in range(0, len(collection), 20)]
# If the last node has less than 8 mbrs, fill with mbrs of the previous
balance = 8 - len(nodes[-1])
if balance > 0 and len(nodes) > 1:
migrate = len(nodes[-2])
nodes[-1] = nodes[-2][migrate-balance:] + nodes[-1]
nodes[-2] = nodes[-2][:migrate-balance]
# Make the new MBRs based on the corners of each 20-piece
collection = []
for node in nodes: #for every 20 MBRs in 500 MBRs
collection.append(createMBR(nodeId, node))
nodeId += 1
level.append(nodes)
print("{} nodes at level {}".format(len(nodes), rank))
rank += 1
return level
if __name__ == '__main__':
# Read the coordinates of the polygons points
set_of_points = inputReader(filename1=sys.argv[1], filename2=sys.argv[2])
# Create the mbrs of the polygons
mbrs = []
for sp in set_of_points:
mbrs.append(findMBR(sp[0], sp[1]))
# Sort the mbrs based on the Z curve
mbrs.sort(key=lambda ld: ld.zcurve)
# Make the Rtree
rTree = makeRtree(mbrs)
outputWriter("Rtree.txt", rTree)

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coords.txt
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knnqueries.py
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import sys
import ast
import heapq as hq
from math import sqrt
class Point:
def __init__(self, x, y):
self.x = x
self.y = y
class MBR:
def __init__(self, id, xlow, xhigh, ylow, yhigh):
self.xlow = xlow
self.xhigh = xhigh
self.ylow = ylow
self.yhigh = yhigh
self.id = id
self.obj = False
def setDistance(self, qp):
self.distance = self.findDistance(qp)
def findDistance(self, qp):
dx = dy = 0
if qp.x < self.xlow:
dx = self.xlow - qp.x
elif qp.x > self.xhigh:
dx = qp.x - self.xhigh
else:
dx = 0
if qp.y < self.ylow:
dy = self.ylow - qp.y
elif qp.y > self.yhigh:
dy = qp.y - self.yhigh
else:
dy = 0
return sqrt(dx**2 + dy**2)
def makeObject(self):
self.obj = True
def __lt__(self, other):
if self.distance < other.distance:
return True
return False
class Node:
def __init__(self, leaf, id, list_of_mbrs):
self.leaf = leaf
self.id = id
self.list_of_mbrs = list_of_mbrs
def parseRtree(filename):
with open(filename, 'r') as f:
nodes = []
for line in f:
node = ast.literal_eval(line)
mbrs = []
for mbr in node[2]:
mbrs.append(MBR(int(mbr[0]), float(mbr[1][0]), float(mbr[1][1]),
float(mbr[1][2]), float(mbr[1][3])))
nodes.append(Node(node[0], node[1], mbrs))
return nodes
# Read and execute all the NN queries assigned to the r tree
def parseQuery(rtree, filename, k):
with open(filename, 'r') as f:
num = 0
for line in f:
[x, y] = line.split()
findKNN(rtree, Point(float(x), float(y)), k, num)
num += 1
# Search for the neareset k neighboors
def findKNN(rtree, qp, k, num):
# List to put our k nn objects
results = []
# Initialize a priority queue
pq = []
# Put the root nodes into the priority queue
for mbr in rtree[-1].list_of_mbrs:
mbr.setDistance(qp)
hq.heappush(pq, mbr)
while len(pq) > 0 and len(results) < k:
# Retrieve the next node and remove it from the queue
e = hq.heappop(pq)
if e.obj == True:
hq.heappush(results, e)
continue
# At a leaf node
if rtree[e.id].leaf == 0:
for mbr in rtree[e.id].list_of_mbrs:
mbr.setDistance(qp)
mbr.makeObject()
hq.heappush(pq, mbr)
# At an intermediate node
else:
for node in rtree[e.id].list_of_mbrs:
node.setDistance(qp)
hq.heappush(pq, node)
print("{}:".format(num), end=' ')
for r in range(k-1):
nn = hq.heappop(results)
print("{},".format(nn.id), end='')
nn = hq.heappop(results)
print(nn.id)
if __name__ == '__main__':
rtree = parseRtree(sys.argv[1])
parseQuery(rtree, sys.argv[2], int(sys.argv[3]))

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offsets.txt
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import sys
import ast
class MBR:
def __init__(self, id, xlow, xhigh, ylow, yhigh):
self.xlow = xlow
self.xhigh = xhigh
self.ylow = ylow
self.yhigh = yhigh
self.id = id
def intersects(self, other):
if self.xlow > other.xhigh or self.xhigh < other.xlow:
return False
if self.ylow > other.yhigh or self.yhigh < other.ylow:
return False
return True
class Node:
def __init__(self, leaf, id, list_of_mbrs):
self.leaf = leaf
self.id = id
self.list_of_mbrs = list_of_mbrs
def parseRtree(filename):
with open(filename, 'r') as f:
nodes = []
for line in f:
node = ast.literal_eval(line)
mbrs = []
for mbr in node[2]:
mbrs.append(MBR(int(mbr[0]), float(mbr[1][0]), float(mbr[1][1]),
float(mbr[1][2]), float(mbr[1][3])))
nodes.append(Node(node[0], node[1], mbrs))
return nodes
# Read and execute all the range queries assigned to the r tree
def parseQuery(rtree, filename):
with open(filename, 'r') as f:
i = 0
for line in f:
[xlow, ylow, xhigh, yhigh] = line.split()
results = []
rangeQuery(rtree, MBR(int(i), float(xlow), float(xhigh),
float(ylow), float(yhigh)), results)
print("{} ({}):".format(i, len(results)), end=' ')
for r in range(len(results) - 1):
print("{},".format(results[r].id), end='')
if len(results) != 0:
print(results[-1].id)
else:
print(' ')
i += 1
# Find the intersecting rectangles
def rangeQuery(rtree, window, results, node=None):
# At the root node
if node == None:
node = rtree[-1]
for n in node.list_of_mbrs:
if window.intersects(n) or n.intersects(window):
rangeQuery(rtree, window, results, rtree[n.id])
# At an intermediate node
elif node.leaf == 1:
for n in node.list_of_mbrs:
if window.intersects(n) or n.intersects(window):
rangeQuery(rtree, window, results, rtree[n.id])
# At a leaf node
else:
for mbr in node.list_of_mbrs:
if window.intersects(mbr) or mbr.intersects(window):
results.append(mbr)
if __name__ == '__main__':
rtree = parseRtree(sys.argv[1])
parseQuery(rtree, sys.argv[2])