library(lubridate)
library(ggplot2)
library(StreamMetabolism)
library(xts)
library(reshape)
library(scales)
Ch_1004 <- sunrise.set(46.53595650395596,6.613597869873047, "2023/01/01", timezone="MET", num.days=370)
sunrise <- Ch_1004$sunrise
sunset <- Ch_1004$sunset
sunrise <- strftime(sunrise, format="%R", tz="MET")
sunset <- strftime(sunset, format="%R", tz="MET")
Ch_1004["sr"] <- as.POSIXct(sunrise, format = "%H:%M")
Ch_1004["ss"] <- as.POSIXct(sunset, format = "%H:%M")
Ch_1004["timestamp"] <- align.time(Ch_1004$sunrise, 60*10)
Ch_1004 <- Ch_1004[c("timestamp", "sr", "ss")]
locsrss <- ggplot(Ch_1004, aes(x=Ch_1004$timestamp)) + geom_line(aes(y=Ch_1004$sr)) + geom_line(aes(y=Ch_1004$ss)) + labs(title = " Sonnenauf-/Sonnenuntergang - Ch_1004 2023", x = "Datum", y = "Zeit")
pdf("Ch_1004_SA_SU.pdf", paper="a4r", width=11)
locsrss
dev.off()
png(filename="Ch_1004_SA_SU.png", width = 1400, height = 800, units = "px")
locsrss
dev.off()
Ch_1004["Sonnenaufgang"] <- strftime(Ch_1004$sr, format="%H:%M")
Ch_1004["Sonnenuntergang"] <- strftime(Ch_1004$ss, format="%H:%M")
write.table(Ch_1004, file="Ch_1004_SaSu.csv", dec=',', sep=';', row.names=FALSE)