# Load CSV file
options(width = 150)
df <- read.csv("data.csv",col.names=c("DateTime", "DB", "RH","DP","P","WIND","WIND-DIR","SUN","RAIN","START"))

#df=remove_empty(df, which = c("rows"), cutoff = 1, quiet = TRUE)
sum(is.na(df))
df=df[complete.cases(df), ]
nrow(df)
#print(df[54646:54650,])
#df=df[df$DB != FALSE, ] 
#df=na.omit(df)
# Print the first few rows of the data
library(psychrolib)
SetUnitSystem("SI")
df$DB=(df$DB/10)
df$RH=(df$RH/100)
df$P=(df$P*100)

#head((is.na(df)),n=100)
df=df[50000:nrow(df),]
sum(is.na(df))
#head(df)
df$HR=GetHumRatioFromRelHum(df$DB, df$RH,df$P)

df$H=GetMoistAirEnthalpy(df$DB, df$HR)

#df$a = quantile(df$H, probs = 0.9999)
#df$b = quantile(df$DB, probs =0.9999)

#top=which(df$a & df$b)

maxRH=max(df$RH)
df$pRHdiff=abs((maxRH-df$RH)/((maxRH+df$RH)/2))

maxDB=max(df$DB)
df$pDBdiff=abs((maxDB-df$DB)/((maxDB+df$DB)/2))

designH=quantile(df$H,probs=0.9999)
df$pHdiff=abs((designH-df$H)/((designH+df$H)/2))

df$score=sqrt(((df$pHdiff)^2)+((df$pRHdiff)^2)+((df$pDBdiff)^2))

print(designH)
print(maxRH)
print(maxDB)
#top = df[df$H > quantile(df$H, probs =0.99999),]
tail(df[order(df$score, decreasing=TRUE),],n=25)
#df <- df[order(df$H), ]
#tail(df,n=(411367/400))