library(ggplot2)
library(deSolve)
#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, Tr, parms) {
  with(as.list(c(parms, Tr)), {
    Text <- input(t)   # <---- here
    dTr <- -(qv * pa * Ca * (Tr - Text)) / (V * pa * Ca)
    list(Tr=dTr, Text = Text)
  })
}
parms = c(qv = 5.4, pa = 1.204, Ca = 1006, V = 300)

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

out=as.data.frame(out)
head(out)

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