library(ggplot2)
library(dplyr)
library(deSolve)
library(gridExtra)
library(cowplot)
library(lubridate)

#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("data2020.csv",col.names=c("DateTime", "DB", "RH","DP","P","WIND","WIND-DIR","SUN","RAIN","START"))

head(df)
df=df[complete.cases(df), ]
df$DateTime=as.POSIXct(df$DateTime,format="%d/%m/%Y %H:%M")
df$DB=(df$DB/10)

df <- df %>%
  filter(hour(DateTime) %in% c(0, 6, 12, 18),minute(DateTime) == 0)

head(df,n=20)
#stop()


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

model <- function(t, y, parms) {
  with(as.list(c(parms, y)), {
    Text <- input(t)   # <---- here
    SP=20
    HRS=ifelse(Text>y[1],
               max(0,min(0.7,(Text-y[1])*0.5)),
               max(0,min(0.7,(20-y[1])*1)))
#    HRS=ifelse(Text<16,0.7,0)
    dy1 <- -((qv * pa * Ca * (y[1] - ((HRS*y[1])-(Text*(HRS-1))))) / (V * pa * Ca))+((ifelse(Text>20,80,0)*(30*60))/(V*Ca*pa))
#+min(0,min(1,(y[1]-24)*1))*((q2 * pa * Ca * (9-y[1])) / (V * pa * Ca))
#    dy2 = 1
#    dy2 <- y[2] 
    list(c(dy1),
         Text = Text,
         ifelse(Text>y[1],
                max(0,min(1,(Text-y[1])*0.5)),
                max(0,(min(1,(20-y[1])*1)))),
         ifelse(Text>y[1],0.5,max(0,min(1,(y[1]-20)*1)))*((q2 * pa * Ca * (9-y[1]))),qv*pa*Ca*(20-Text))
  })
}
parms = c(qv = 0.005*(30*60), pa = 1.204, Ca = 1006, V = 3, q2=0.005)

out <- ode(y = c(Tr=20), times = seq(0,nrow(df),1), func = model, parms,rtol = 1e-1)

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=Tr)) + 
geom_line(color="red")+
geom_line(data=out, aes(x=time,y=Text),color="blue")+
#geom_hline(yintercept = 18)+
geom_hline(yintercept = 20)
plot2 = ggplot(out, aes(x=time,y=Sig))+geom_bar(stat = "identity", width = 0.1, color = "black") 
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=Total)) + geom_line()
#plot5 = ggplot(out, aes(x=time, y=Error))+geom_line()

# Open a PDF device to save the plots
pdf("tx5.pdf", width = 40, height = 20)

# Arrange and print the plots using gridExtra
grid.arrange(plot1,plot2,plot3,plot4,nrow = 4)

# 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)