Commit 66bb71c2 authored by Tianyang's avatar Tianyang
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add compare month

parent ec24abd2
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import numpy as np
import matplotlib.pyplot as plt
from functools import reduce
from database_pre3 import connection
import matplotlib.pyplot as plt
import re
import folium
import random
import datetime
table_variable = [
def add (x,y):
return x+y
def abs_diff(x,y):
longth = len(x)
result = []
for i in range(longth):
total = 0
for each in result:
total += each
return total
def diff(x,y):
return x-y
#caculate mean reduce
#input [count,mean]
def reduceFonction (x,y):
result = []
for i in range(2):
return result
#input [valeur] -> [count,mean]
def mapFonction1 (x):
return [1,x]
#input [count,mean] -> [mean]
def mapFonction2 (x):
return x[1]/x[0]
#Map reduce to caculate the means of each month of each station
def mapReduce_kmeans(targetNB,start,end):
results = dict()
for row in session.execute("select * from caitiany.database_kmeans where date >= '%s' and date <= '%s' ALLOW FILTERING"%(start,end)):
data_target = row[targetNB]
if data_target == None:
#We want to take out the month correspondant and add month into keys
dateparser = re.compile("(\d+)-(?P<month>\d+)-(\d+) (?P<time>\d+:\d+)")
match_month = dateparser.match(str(row[0]))
time = match_month.groupdict()
month = time["month"]
data_espace = (row[1],row[2],row[3],month)
if results.get(data_espace) is None:
results[data_espace] = mapFonction1(data_target)
mapresult = mapFonction1(data_target)
results[data_espace] = reduceFonction(mapresult,results[data_espace])
for eachEspace in results:
results[eachEspace] = mapFonction2(results[eachEspace])
#Now we need to put the data of each station all together
newResult = dict()
for each in results:
newKey = each[0:3]
if newResult.get(newKey) is None:
newResult[newKey] = [results[each]]
newResult[newKey] = newResult[newKey]+[results[each]]
max = 0
for each in newResult:
if len(newResult[each]) >max:
max = len(newResult[each])
deletList = []
#We want to check if there is a missing data so we check the number of month max
#Then we delete all the station who have the missing data
for each in newResult:
if len(newResult[each]) < max:
for delete in deletList:
return newResult
#Caculate the difference of the number of the clusters
def cluster_nb_diff(centre_new,centre):
sum = 0
for i in range(3):
sum += abs(centre_new[i][0]-centre[i][0])
return sum/3
#input [tmpt] -> [tmpt,tmpt,tmpt,mean]
def map1_kmeans(x):
return [x,x,x,x]
#input [tmpt,tmpt,tmpt,tmpt] and [c1,c2,c3,0] -> [|tmpt - c1|,|tmpt - c2|,|tmpt - c3|,tmpt]
def reduceKmeans (x,y):
result = []
for i in range(3):
return result
#input [|tmpt - c1|,|tmpt - c2|,|tmpt - c3|,tmpt] -> [cluster number, min(|tmpt - c|), tmpt]
def map2_kmeans(x):
min_value = 10000000000000
index = 0
for each in range(3):
if min_value > x[each]:
min_value = x[each]
index = each
return [index,min_value,x[3]]
#Update the new center by means
def MapnewCentre(x):
result = []
longth = len(x[1])
for i in range(longth):
if x[0] != 0:
return result
#caculate moyen
def caculateMoyen(x):
total = 0
count = 0
for i in x:
count += 1
total += i
return total/count
#caculate sum of two list
def caculateSumofList(x,y):
if x == []:
return y
longth = len(x)
result = []
for i in range(longth):
result.append(x[i] + y[i])
return result
#The main algorithm of Kmeans
def kmeans (targetNB,target,start,end):
#cluster est pour stocler lat, lon de chaque point de chaque cluster
cluster = [[],[],[]]
result = mapReduce_kmeans(targetNB,start,end)
if len(result) < 3:
raise Exception ("We\'ve just searched less than 3 station!!")
#mettre ramdom 3 point comme le centres init
center1 = random.choice(list(result))
center2 = random.choice(list(result))
center3 = random.choice(list(result))
init_point_values = [result[center1],result[center2],result[center3]]
#init_point_values = [result[i] for i in result.keys()][:3]
init_point_keys = [center1,center2,center3]
#3centre with [point count, temprature centre]
centre = {0:[0,0],1:[0,0],2:[0,0]}
centre_new = {0:[0,0],1:[0,0],2:[0,0]}
for key in centre.keys():
centre[key] = [1,init_point_values[key]]
centre_new[key] = [1,init_point_values[key]]
#init the centre new and result new for mapreduce
result_new = dict()
#When the number of point of cluster don't change,stop
while True:
for eachkey in result:
if eachkey in cluster[0] or eachkey in cluster[1] or eachkey in cluster[2]:
#caculate the distance between the data of this lingne and the centre
result_new[eachkey] = map1_kmeans(result[eachkey])
centre_values = [centre[0][1],centre[1][1],centre[2][1]]
result_new[eachkey] = reduceKmeans(result_new[eachkey],centre_values)
result_new[eachkey] = map2_kmeans(result_new[eachkey])
#Put all the distance and points into the clusters
#Result format [cluster number, min(|tmpt - c|),tmpt - c]
for eachpoint in result_new:
clusterNB = result_new[eachpoint][0]
centre_new[clusterNB][0] += 1
centre_new[clusterNB][1] = caculateSumofList(centre_new[clusterNB][1],result_new[eachpoint][2])
#compare centre_new and centre, if
if not cluster_nb_diff(centre_new,centre) > 1:
#caculate the new centre
print ("Cluster number differ: ",cluster_nb_diff(centre_new,centre))
for eachculster in centre_new:
centre_new[eachculster][1] = MapnewCentre(centre_new[eachculster])
print("center new: ",centre_new)
print("center old: ",centre)
centre = centre_new
centre_new = {0:[0,[]],1:[0,[]],2:[0,[]]}
result_new = dict()
cluster = [[],[],[]]
#Create the map of the cluster
def createMap (cluster,result):
mean_lat = 0
count = 0
for each in [cluster[0],cluster[1],cluster[2]]:
for each_pos in each:
mean_lat += each_pos[0]
count += 1
mean_lat = mean_lat/count
mean_lon = 0
count = 0
for each in [cluster[0],cluster[1],cluster[2]]:
for each_pos in each:
mean_lon += each_pos[1]
count += 1
mean_lon = mean_lon/count
m = folium.Map(location=[mean_lon,mean_lat],zoom_start=6)
color = {0:'blue',1:'red',2:'green'}
i = 0
for each in [cluster[0],cluster[1],cluster[2]]:
for each_pos in each:
label = str(each_pos[2])+"\n"+""+target+":"+str(round(caculateMoyen(result[(each_pos[0],each_pos[1],each_pos[2])]),2))
i +=1"Projet-NF26/map.html")
print("Generate successfully")
#Check which number of the indicateur
def checkNBvariable (x):
for each in table_variable:
if x == each:
return i
i += 1
print ('Doesn\'t exist!!')
if __name__ == "__main__":
session = connection()
start = input("Please enter the start time [form: AAAA-MM-DD (From 2008-1-1 to 2017-12-30)]: ")
end = input("Please enter the end time [form: AAAA-MM-DD (From 2008-1-1 to 2017-12-30)]: ")
target = input("Which indicator do you want to check [tmpf,dwpf,etc]: ")
targetNB = checkNBvariable(target)
#start = '2008-01-01'
#end = '2013-12-12'
#data = session.execute_async("select * from caitiany.database_kmeans where date >= '%s' and date <= '%s' ALLOW FILTERING"%(start,end))
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