library(ggplot2)
library(deSolve)
library(gridExtra)
library(cowplot)

#signal <- data.frame(times = times, import = rep(0, length(times)))

Text=read.csv("input.csv")

#head(signal)
#signal$import <- ifelse((trunc(signal$times) %% 2 == 0), 0, 1)
#signal[8:12,]

#head(signal)

df <- read.csv("dataJUL.csv",col.names=c("DateTime", "DB", "RH","DP","P","WIND","WIND-DIR","SUN","RAIN","START"))
df=df[complete.cases(df), ]
df$DateTime=as.Date(df$DateTime)
df$DB=(df$DB/10)


input <- approxfun(df$DB, rule = 2)

model <- function(t, y, parms) {
  with(as.list(c(parms, y)), {
    TE <- input(t)   # <---- here
    dT1 = (-H1*(T1-T2)- (Hce+Hri)*(T1-TE))/K1
    dT2 = (H2*(T2-T3)+ H1*(T1-T2))/K2
    dT3 = (H3*(T3-T4)+ H2*(T2-T3))/K3
    dT4 = (H4*(T4-T5)+ H3*(T3-T4))/K4
    dT5 = ((Hci+Hri)*(T5-TA)+ H4*(T4-T5))/K5
    dTA = -(A*(Hci+Hri)*(TA-T5))/KA
#    dy1 <- -((qv * pa * Ca * (y[1] - Text)) / (V * pa * Ca))+max(0,min(1,(20-y[1])*1))*((q2 * pa * Ca * (40-y[1])) / (V * pa * Ca))
#    dy2 = 1
#    dy2 <- y[2] 
    list(c(dT1,dT2,dT3,dT4,dT5,dTA),TE=TE)
#    list(c(dy1),Text = Text,max(0,(min(1,(20-y[1])*1))),max(0,min(1,(20-y[1])*1))*((q2 * pa * Ca * (40-y[1]))),qv*pa*Ca*(20-Text))
  })
}

U=20
K=150
parms = c(H1=U/6,H2=U/3,H3=U/3,H4=U/6,K1=K/8,K2=K/4,K3=K/4,K4=K/4,K5=K/8,KA=5,Hce=9,Hre=9,Hci=9,Hri=9,A=10)

out <- ode(y = c(T1=20,T2=20,T3=20,T4=20,T5=20,TA=20), times = seq(0,nrow(df),1), func = model, parms)

out=as.data.frame(out)
head(out,n=50)

#parms = c(qv = 54, pa = 1.204, Ca = 1006, V = 3000)

#model <- function(time, Tr,Text, parms) {
#df <- read.csv("dataWEEK.csv",col.names=c("DateTime", "DB", "RH","DP","P","WIND","WIND-DIR","SUN","RAIN","START"))
#df=df[complete.cases(df), ]
#df$DateTime=as.Date(df$DateTime)
#df$DB=(df$DB/10)
#

#Text=parms[5]
#dTr <- -(parms[1] * parms[2] * parms[3] * (Tr - Text)) / (parms[4] * parms[2] * parms[3])
#dText = df$DB
#list(dTr,Text)
#}#

#solution <- ode(y = 20, times =as.numeric(rownames(df)), func = model, parms = parms)
#print(solution)

#solution=as.data.frame(solution)

#names(out) <- c("time", "Tr","Text","Sig","Heating","Loss")

#out$Total <- cumsum((out$Heating/2))
#head(out)
##head(solution)
#
# Sample data for plot1

# Create ggplot objects
plot1 <- ggplot(out, aes(x=time, y=TA)) + 
geom_line(color="red")+
geom_line(data=out, aes(x=time,y=TE),color="blue")+
#geom_hline(yintercept = 18)+
#geom_hline(yintercept = 20)
#plot2 = ggplot(out, aes(x=time,y=Sig))+geom_line()
#plot3 = ggplot(out, aes(x=time,y=Heating))+geom_line()+
#geom_line(data=out,aes(x=time,y=Loss),color="red")
#plot4 <- ggplot(out, aes(x=time, y=Loss)) + geom_line()
#plot5 = ggplot(out, aes(x=time, y=Error))+geom_line()
# Open a PDF device to save the plots
pdf("tx.pdf", width = 40, height = 20)

# Arrange and print the plots using gridExtra
grid.arrange(plot1,nrow = 1)

# Or arrange and print the plots using cowplot
# plot_grid(plot1, plot2, nrow = 1)

# Close the PDF device to save the plots to the PDF file
dev.off()



#gg_plot <- ggplot(data = out, aes(x = time, y = Total)) +
#  geom_line() +
#  geom_line(aes(x = time, y = Text), color = "blue")+
#  geom_line(aes(x=time,y=Sig),color="red")+
#  labs(x = "Time Steps", y = "Room Temperature (Tr)") +
#  ggtitle("Room Temperature Over Time")
##
#ggsave(filename = "room_temperature_plot.pdf", plot = gg_plot)