setwd("/Users/bp/Desktop/CONTRATS_AirNormand/2016/RapportFinalBruno")
rm(list=ls())

# Lecture des données
pm  =  read.table("DATA/mesures_horaires_hloc_pm10_a_filer.csv",sep=";",dec=".",header=T) #,row.names=1)
n     = dim(pm)[1]
datedebut = "10/12/2008"

# Chargement des données météo et indicateurs
VarExp <- read.table("DATA/meteo_extra_jourMois.csv",sep=";",dec=".",header=T)
VarExp <- VarExp[-1,]

# Lecture des dates
dates =  read.table("DATA/Dates_jours.csv",header=F,as.is=T)
dates = dates[,1]

# Contruction des matrices de données
pm.h   <- matrix(pm[,2],ncol=24,byrow=TRUE)

Nlignes = nrow(pm.h)
Data = cbind(pm.h[1:(Nlignes -1), ], pm.h[2:Nlignes, ])
dates2 = dates[2:Nlignes]
rownames(Data) = dates2
# df contient l'ensemble des données.
#df <- cbind(Data,varexp[,-1])
df <- Data
PMjour <- apply(df[,25:48],1,mean,na.rm=T)
dfexp <- cbind(VarExp,PMjour)

Dates = c(
"16/03/2015",
"19/01/2015",
"27/04/2015")

Categorie = c("Epandage", "Chauffage", "Non Polluée")

RepFig = "FIGURES_Etude"

ResDates = NULL

nbvois=10
j=1 # numéro de semaine
ij=6 # numéro du jour (0 = lundi)

Err24 = NULL
ErrPrev = NULL
Kvois = NULL

for (Hc in 5:24) {

H=24+Hc
fen=H
#fen=12 ou 24 ou H
L = (H-fen+1):H

# Premier conditionnement : mois
indcond <- dfexp[,"Mois"] == 2 | dfexp[,"Mois"] == 3 | dfexp[,"Mois"] == 4 | dfexp[,"Mois"] == 9 | dfexp[,"Mois"] == 10
data = df[indcond,]
varexp = dfexp[indcond,]

nl = (1:nrow(data))[rownames(data)==Dates[j]]
dateJPrev = rownames(data)[nl+ij]
dataj = as.numeric(data[nl+ij, 1:48])
data = data[-(nl + ij), ]
varexp = varexp[-(nl + ij), ]
indNA = attr(na.omit(data[, 1:48]),"na.action")
data = data[-indNA,]
varexp = varexp[-indNA,]

# Conditionnement : les jours avec PMjour +/- large
large = 1
bornes = mean(dataj[25:48])+c(-large,large)
indcond = varexp[,"PMjour"]>=bornes[1] & varexp[,"PMjour"]<=bornes[2]
data = data[indcond,]
varexp = varexp[indcond,]

D = rep(0,nrow(data))
for (k in 1:nrow(data)) {
       #D[k] = sqrt(sum((1:H)*(dataj[L] -data[k,L])^2))
       D[k] = sqrt(sum((dataj[L] -data[k,L])^2))
}
ind = order(D)[1:nbvois]
w = 1/(D[ind]^2)
w = w/sum(w)
W = w%o%rep(1,48)
JourMoy = apply(data[ind, 1:48], 2, mean)
#JourMoy = apply(W*data[ind, 1:48], 2, sum)
NomFile = paste("Voisins_Epandage_PMjour_Hc_",Hc,".png",sep="")
Titre = paste("Jour à prévoir : ",dateJPrev," - ", length(ind)," voisins",sep="")
#erreur = sqrt(sum((dataj[25:48] - JourMoy[25:48])^2))
if(Hc==24){erreurPrev=NA}else{erreurPrev = mean(abs(dataj[(H+1):48] - JourMoy[(H+1):48]))}
erreur24 = mean(abs(dataj[25:48] - JourMoy[25:48]))
png(NomFile)
matplot(t(data[ind, 1:48]), type = "l", lwd=1.4, lty=1, col=1:length(ind), 
	cex.axis=1.4, cex.main = 1.7, cex.lab=1.5, 
	xlab="Heures locales", ylab=paste0("PM10 - Erreurs = ",round(erreur24,1)," / ",round(erreurPrev,1)), main=Titre)
legend("top",rownames(data)[ind],ncol=2,lwd=1.4, lty=1, col=1:length(ind))
lines(1:48, dataj, lwd=2.5)
lines(JourMoy, lty = 2, lwd=2)
abline(v=c(24.5,H+0.5), lty = 2, lwd=1.2)
xx=dev.off()



  Err24 = c(Err24, erreur24)
  ErrPrev = c(ErrPrev, erreurPrev)
ResDates = cbind(ResDates, rownames(data)[ind])
}


rownames(ResDates) = 1:10

Kvois = NULL
for (Col in ncol(ResDates):1) {
K = 0
for (I in 1:10){
    for (J in 1:10){
       if (ResDates[I,1] == ResDates[J,Col]) K = K +1
     }
}
Kvois = c(Kvois, K)
}

# pdf("Erreur_Epandage_PMjour.pdf")
ymin = min(na.omit(ErrPrev), Err24)
ymin = min(ymin, Kvois)
ymax = max(na.omit(ErrPrev), Err24)
ymax = max(ymax, Kvois)
plot(5:24,Err24, type = "l", lwd = 2, cex.axis=1.4, cex.lab = 1.5,
  ylab = "MAE",xlab = "Heures de prévision", ylim= c(ymin,ymax))
lines(5:24,ErrPrev, lwd=2, lty = 2)
legend("topright", legend=c("Erreur 24h", "Erreur prévision"), lty = c(1,2), lwd=2, cex=1.5)
points(5:24, Kvois, cex=1.8, pch = 19)
# xx = dev.off()

length(D)