library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
DE_Gera <- sunrise.set(50.8850706,12.0807203, "2024/01/01", timezone="MET", num.days=370)
sunrise <- DE_Gera$sunrise
sunset <- DE_Gera$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
DE_Gera["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
DE_Gera["ss"] <- as.POSIXct(sunset, format = "%H:%M")
DE_Gera["timestamp"] <- align.time(DE_Gera$sunrise, 60*10)
DE_Gera <- DE_Gera[c("timestamp", "sr", "ss")]
locsrss <- ggplot(DE_Gera, aes(x=DE_Gera$timestamp)) + geom_line(aes(y=DE_Gera$sr)) + geom_line(aes(y=DE_Gera$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - DE_Gera 2024", x = "Datum", y = "Zeit")
pdf("DE_Gera_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="DE_Gera_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
DE_Gera["Sonnenaufgang"] <- strftime(DE_Gera$sr, format="%H:%M")
DE_Gera["Sonnenuntergang"] <- strftime(DE_Gera$ss, format="%H:%M")
write.table(DE_Gera, file="DE_Gera_SaSu.csv", dec=',', sep=';', row.names=FALSE)