question3.py 7.35 KB
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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
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import random


table_variable = [
    'date',
    'lon',
    'lat',
    'station',
    'alti',
    'drct',
    'dwpf', 
    'feel',
    'gust',
    'ice_accretion_1hr',
    'ice_accretion_3hr',
    'ice_accretion_6hr',
    'metar',
    'mslp',
    'p01i',
    'peak_wind_drct',
    'peak_wind_gust',
    'peak_wind_time',
    'relh',
    'sknt',
    'skyc1',
    'skyc2',
    'skyc3',
    'skyc4',
    'skyl1',
    'skyl2',
    'skyl3',
    'skyl4',
    'tmpf',
    'vsby',
    'wxcodes']



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def add (x,y):
    return x+y


def abs_diff(x,y):
    return abs(x-y)

def diff(x,y):
    return x-y



#caculate mean reduce
#input [count,mean]
def reduceFonction (x,y):
    result = []
    for i in range(2):
        result.append(reduce(add,[x[i],y[i]]))
    return result

#input [valeur] -> [count,mean]
def mapFonction1 (x):
    return [1,x]

#input [count,mean] -> [mean]
def mapFonction2 (x):
    return x[1]/x[0]


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#Map reduce to caculate the means of each station
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def mapReduce_kmeans(targetNB,start,end):
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    results = dict()
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    for row in session.execute("select * from caitiany.database_kmeans where date >= '%s' and date <= '%s' ALLOW FILTERING"%(start,end)):
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        data_target = row[targetNB]
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        if data_target == None:
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            continue
        data_espace = (row[1],row[2],row[3])
        if results.get(data_espace) is None:
            results[data_espace] = mapFonction1(data_target)
        else:
            mapresult = mapFonction1(data_target)
            results[data_espace] = reduceFonction(mapresult,results[data_espace])
    for eachEspace in results:
        results[eachEspace] = mapFonction2(results[eachEspace])
    return results

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#Caculate the difference of the number of the clusters 
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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,tmpt]
def map1_kmeans(x):
    return [x,x,x,x]

def mapCentre(x):
    return [x[0],x[1],x[2],0]


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#input [tmpt,tmpt,tmpt,tmpt] and [c1,c2,c3,0] -> [|tmpt - c1|,|tmpt - c2|,|tmpt - c3|,tmpt]
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def reduceKmeans (x,y):
    result = []
    for i in range(4):
        result.append(reduce(abs_diff,[x[i],y[i]]))
    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]]



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#Update the new center by means
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def MapnewCentre(x):
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    if x[0] != 0:
        return x[1]/x[0]
    else:
        return 0
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#The main algorithm of Kmeans
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def kmeans (targetNB,target,start,end):
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    #cluster est pour stocler lat, lon de chaque point de chaque cluster
    cluster = [[],[],[]]

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    result = mapReduce_kmeans(targetNB,start,end)
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    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]}
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    for key in centre.keys():
        centre[key] = [1,init_point_values[key]]
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        centre_new[key] = [1,init_point_values[key]]
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        cluster[key].append(init_point_keys[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]:
                continue
            
            #caculate the distance between the data of this lingne and the centre

            #Map1_kemeans
            result_new[eachkey] = map1_kmeans(result[eachkey])
            centre_values = []
            for each in centre:
                centre_values.append(centre[each][1])
            centre_values = mapCentre(centre_values)

            #Reduce
            result_new[eachkey] = reduceKmeans(result_new[eachkey],centre_values)

            #Map2_kmeans
            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] += result_new[eachpoint][2]
            cluster[clusterNB].append(eachpoint)
        
        #compare centre_new and centre, if
        if not cluster_nb_diff(centre_new,centre) > 1:
            break
        else:
            #caculate the new centre
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            print ("Cluster number differ:  ",cluster_nb_diff(centre_new,centre))
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            for eachculster in centre_new:
                centre_new[eachculster][1] = MapnewCentre(centre_new[eachculster])
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            print("center new: ",centre_new)
            print("center old: ",centre)
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            centre = centre_new
            centre_new = {0:[0,0],1:[0,0],2:[0,0]}
            result_new = dict()
            cluster = [[],[],[]]
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    createMap(cluster,result)
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#Create the map of the cluster
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def createMap (cluster,result):
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    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:
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            label = str(each_pos[2])+"\n"+""+target+":"+str(round(result[(each_pos[0],each_pos[1],each_pos[2])],2))
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            folium.Marker([each_pos[1],each_pos[0]],
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                        popup=label,
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                        icon=folium.Icon(color=color[i])).add_to(m)
        i +=1
    m.save("Projet-NF26/map.html")
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    print("Generate successfully")


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#Check which number of the indicateur
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def checkNBvariable (x):
    i=0
    for each in table_variable:
        if x == each:
            return i
        i += 1
    print ('Doesn\'t exist!!')
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if __name__ == "__main__":
    session = connection()
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    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 = '2008-12-19'
    #end = '2012-12-14'
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    #data = session.execute_async("select * from caitiany.database_kmeans where date >= '%s' and date <= '%s' ALLOW FILTERING"%(start,end))
    kmeans(targetNB,target,start,end)