library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Danzi <- sunrise.set(54.3520252,18.6466384, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Danzi$sunrise
sunset <- Danzi$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Danzi["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Danzi["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Danzi["timestamp"] <- align.time(Danzi$sunrise, 60*10)
Danzi <- Danzi[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Danzi, aes(x=Danzi$timestamp)) + geom_line(aes(y=Danzi$sr)) + geom_line(aes(y=Danzi$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Danzi 2023", x = "Datum", y = "Zeit")
pdf("Danzi_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Danzi_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Danzi["Sonnenaufgang"] <- strftime(Danzi$sr, format="%H:%M")
Danzi["Sonnenuntergang"] <- strftime(Danzi$ss, format="%H:%M")
write.table(Danzi, file="Danzi_SaSu.csv", dec=',', sep=';', row.names=FALSE)