library(ggplot2)
library(grid)
library(gridExtra)
library(scales)
epw = "~/weather-datasets/GBR_London-Heathrow.AP.1989_2080High90pct_CIBSETM49.epw"
system(paste("cat ",epw, "| tail -8760 > test.csv"))
EPW <- read.csv("test.csv", header=FALSE)
EPW$OAT = EPW[,7]
EPW$RH = EPW[,9]
EPW$MDH <- paste(EPW$V2,EPW$V3,EPW$V4)
EPW$M = as.POSIXlt(EPW$MDH,format="%m")
EPW$DT = as.POSIXlt(EPW$MDH,format="%m %d %H") 
EPW$HR = as.numeric(format(EPW$DT, "%H"))
EPW$hum_ratio=((2.16679*((610.78*exp((EPW$OAT/(EPW$OAT+238.3))*17.2694))*(EPW$RH/100)/(273.15+EPW$OAT)))/1275.4)


temps <- data.frame()
for (i in 0:23) {temps <- rbind(temps,c(
                  i,
                  mean(subset(subset(EPW, HR==i), format(DT,'%m')=='12')$OAT),
                  mean(subset(subset(EPW, HR==i), format(DT,'%m')=='03')$OAT),
                  mean(subset(subset(EPW, HR==i), format(DT,'%m')=='06')$OAT),
                  mean(subset(subset(EPW, HR==i), format(DT,'%m')=='09')$OAT)))
}

colnames(temps) <- c("hr", "temp_dec", "temp_mar", "temp_jun", "temp_sep")


pdf("plots.pdf")
#ggplot(diamonds, aes(x=carat, y=price)) + geom_point()
p1 = ggplot(temps, aes(x=hr, y=temp_dec)) + geom_line() + scale_y_continuous(limits = c(5, 30))
p2 = ggplot(temps, aes(x=hr, y=temp_mar)) + geom_line() + scale_y_continuous(limits = c(5, 30))
p3 = ggplot(temps, aes(x=hr, y=temp_jun)) + geom_line() + scale_y_continuous(limits = c(5, 30))
p4 = ggplot(temps, aes(x=hr, y=temp_sep)) + geom_line() + scale_y_continuous(limits = c(5, 30))
grid.arrange(p1, p2, p3, p4, ncol = 2)
dev.off()