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import numpy as np
import matplotlib.pyplot as plt
from functools import reduce
from database_pre2 import connection
import matplotlib.pyplot as plt
import re
import folium

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]


def testNan (x):
    test = x != x
    return test


def mapReduce_kmeans(data,targetNB):
    results = dict()
    for row in data.result():
        data_target = row[targetNB]
        if testNan(data_target):
            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


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]

#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(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]]




def MapnewCentre(x):
    return x[1]/x[0]



def kmeans (data,targetNB):
    #3centre with [point count, temprature centre]
    centre = {0:[1,0],1:[1,0],2:[1,0]}
    #cluster est pour stocler lat, lon de chaque point de chaque cluster
    cluster = [[],[],[]]

    result = mapReduce_kmeans(data,targetNB)

    #mettre le premier 3 point comme le centres init
    init_point_values = [result[i] for i in result.keys()][:3]
    init_point_keys = [i for i in result.keys()][:3]

    for key in centre.keys():
        centre[key] = [1,init_point_values[key]]
        cluster[key].append(init_point_keys[key])
    

    #init the centre new and result new for mapreduce
    centre_new = {0:[0,0],1:[0,0],2:[0,0]}
    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
            print ('jasdlkjalsdkjalskd         ',cluster_nb_diff(centre_new,centre))
            for eachculster in centre_new:
                centre_new[eachculster][1] = MapnewCentre(centre_new[eachculster])
            centre = centre_new
            centre_new = {0:[0,0],1:[0,0],2:[0,0]}
            result_new = dict()
            cluster = [[],[],[]]
    createMap(cluster)


def createMap(data):
    mean_lat = 0
    mean_lon = 0
    count = 0
    for each in data.result():
        #print(each)
        mean_lat += each[1]
        mean_lon += each[2]
        count += 1
    if count == 0:
        print('No data available at this timestamp !')
        return
    mean_lat = mean_lat/count
    mean_lon = mean_lon/count


    m = folium.Map(location=[mean_lon,mean_lat],zoom_start=6)

    color = {0:'blue',1:'red',2:'green'}
    attributes = ["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"]
    for each in data.result():
        # print(each)
        l = [attributes[i] + ":" + str(each[i + 4]) for i in range(len(attributes)) if str(each[i + 4]) != 'nan'
             and attributes[i] != "metar"]
        string='\n'.join(l)
        folium.Marker([each[2],each[1]],
                    popup=string,
                    icon=folium.Icon(color='red')).add_to(m)
    m.save("Projet-NF26/map.html")



if __name__ == "__main__":
    session = connection()
    timestamp = '2017-12-02 00:30:00'
    data = session.execute_async("select * from meurouth_cql.database_time where date = '%s' ALLOW FILTERING"%(timestamp))
    createMap(data)