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main2.py
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main2.py
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from netCDF4 import Dataset
from fastdtw import fastdtw
import numpy as np
from scipy.spatial.distance import euclidean
from scipy.sparse import csr_matrix
from igraph import *
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
#latitutdes index range: 0 to 93
#longitudes index range: 0 to 191
#126 to 157 USA longitude
#20 to 34 USA latitude
neighbourlong=[0,1,1,1,0,-1,-1,-1]
neighbourlat =[-1,-1,0,1,1,1,0,-1]
globalcorepoints=set()
globalnoisepoints=set(range(480))
globalborderpoints=set()
g = Graph(directed=True)
filename="ALDA_dataset_and_initial_code/air.2m.gauss."
f = Dataset(filename+str(1979)+".nc", "r+", format="NETCDF4")
distancedict={}
def isCorePoint(minPts, epsDist, currlong, currlat, minlong, minlat, rangelong, rangelat, timeindex, timewindow):
# for i in range(num_files):
# f = netcdf.netcdf_file('air.2m.gauss.' + str(i+1979) + '.nc', 'r')
# print(f.air)
countwithineps=1 #count self. hence 1
#currtimeslice = []
#for iter in range(-1*timewindow/2+1, timewindow/2+1):
## [-4, -3, -2, -1, 0, 1, 2, 3, 4]
#currtimeslice.append(f.variables['air'][timeindex + iter][0][currlat][currlong])
lowertimeindex=timeindex - timewindow/2
uppertimeindex=timeindex + timewindow/2 + 1
currtimeslice = f.variables['air'][lowertimeindex : uppertimeindex, 0, currlat, currlong]
currindex=(currlat - minlat)*rangelong + (currlong - minlong)
smallDTWDict={} #key is index of neighbourlong and neighbourlat
for iter in range(8):
neighbourindex=(currlat+neighbourlat[iter] - minlat)*rangelong + (currlong+neighbourlong[iter] - minlong)
#checking neighbours of current, using neighbourlong and neighbourlat
#neighbourtimeslice=[]
#for iter2 in range(-1*timewindow/2+1, timewindow/2+1):
# neighbourtimeslice.append(f.variables['air'][timeindex + iter2][0][currlat+iter][currlong+iter])
neighbourtimeslice = f.variables['air'][lowertimeindex : uppertimeindex, 0, currlat+neighbourlat[iter], currlong+neighbourlong[iter]]
#distance variable available outside if statement.
if str(neighbourindex)+"_"+str(currindex) in distancedict: #if previously inserted, from current vertex's perspective: "neighbourindex_currindex"
distance = distancedict[str(neighbourindex)+"_"+str(currindex)]
else:
distance, path = fastdtw(currtimeslice, neighbourtimeslice, dist=euclidean)
encode=str(currindex)+"_"+str(neighbourindex) #not previously inserted. so cache it. from current vertex's perspective: "currindex_neighbourindex"
distancedict[encode]=distance
del distancedict[encode] #don't need anymore. neighbour fetched the distance. keep memory util low.
#print ("iter: ", iter)
#print ("distance:", distance)
smallDTWDict[iter]=distance
if distance < epsDist:
countwithineps+=1
if countwithineps >= minPts:
globalcorepoints.add(currindex)
if currindex in globalnoisepoints:
globalnoisepoints.remove(currindex)
for iter in range(8):
#Add edge to points which are within epsDist. use smallDTWDict
neighbourindex=(currlat+neighbourlat[iter] - minlat)*rangelong + (currlong+neighbourlong[iter] - minlong)
if smallDTWDict[iter] < epsDist:
#Consider neighbors for spatially bordered point but don't add them to the graph
if currlat+neighbourlat[iter] >= minlat and currlat+neighbourlat[iter] <minlat+rangelat and currlong+neighbourlong[iter] >=minlong and currlong+neighbourlong[iter]<minlong+rangelong:
#print(currindex)
#print(neighbourindex)
#print(currlat+neighbourlat[iter])
#print(minlat)
#print(rangelat)
#print(currlong+neighbourlong[iter])
#print(minlong)
#print(rangelong)
g.add_edge(currindex, neighbourindex)
if (neighbourindex) in globalnoisepoints:
globalnoisepoints.remove(neighbourindex)
def DBSCAN(minPts, epsDist, timeindex, timewindow, minlong, minlat, rangelong, rangelat):
#in our case the points of USA are well within the index range of f. currently not writing a border handling case
for iterlong in range(minlong,minlong+rangelong):
for iterlat in range(minlat,minlat+rangelat):
isCorePoint(minPts, epsDist, iterlong, iterlat, minlong, minlat, rangelong, rangelat, timeindex, timewindow)
distancedict={} #trash the old dictionary.
def main():
#g= setupGraph()
#using global variable for now
parser = ArgumentParser("DBSCAN", formatter_class=ArgumentDefaultsHelpFormatter, conflict_handler='resolve')
parser.add_argument('--minPts', default=3, type=int, help='minPts for DBSCAN algorithm')
parser.add_argument('--epsDist', default=15, type=float, help='epsDist for DBSCAN. Enter float')
#parser.add_argument('--timeindex', type=int, help='Timeindex')
parser.add_argument('--timewindow', default=9, type=int, help='')
parser.add_argument('--country', default=1, type=int, help='1 for USA, 2 for World')
args = parser.parse_args()
if args.country == 1:
minlong=126
minlat=20
rangelong=32
rangelat=15
g.add_vertices(480)
elif args.country==2:
minlong=1
minlat=1
rangelong=190
rangelat=92
g.add_vertices(92*190)
timeindices= range(27, 40)
#timeindices=[4]
for timeindex in timeindices:
DBSCAN(args.minPts, args.epsDist, timeindex, args.timewindow,minlong, minlat, rangelong, rangelat)
print("completed DBSCAN for timeindex:",timeindex)
clusteredg = g.clusters(mode=WEAK)
print("clustered")
if args.country==1:
file = open("./second/usa."+str(timeindex)+".clusters", 'w')
else:
file = open("./second/world."+str(timeindex)+".clusters", 'w')
for cluster in clusteredg:
file.write(",".join(str(idx) for idx in cluster))
file.write("\n")
print("Wrote to file.")
file.close()
if __name__ == '__main__':
main()