Here's a jupyter notebook demonstrating how to read in and plot an ROI (Region of Interest) summary using R. In this case I'm using the 3-day summary file from the alligatorriver site. The summary files are in CSV format and can be read directly from the site using a URL.
library(ggplot2)
library(lubridate)
baseurl = 'http://klima.sr.unh.edu/data/archive'
sitename = 'alligatorriver'
roiname1 = 'DB_0001'
csvfile = sprintf("%s_%s_3day.csv",sitename,roiname1)
csvurl = sprintf("%s/%s/ROI/%s",baseurl,sitename,csvfile)
df = read.csv(url(csvurl),comment.char="#",header=TRUE)
df$date = as.Date(df$date)
df$year = year(df$date)
ystart = min(df$year)
yend = max(df$year)
df$year = factor(df$year,levels=seq(ystart,yend))
head(df)
Attaching package: ‘lubridate’ The following object is masked from ‘package:base’: date
date | year | doy | image_count | midday_filename | midday_r | midday_g | midday_b | midday_gcc | midday_rcc | ⋯ | rcc_std | rcc_50 | rcc_75 | rcc_90 | max_solar_elev | snow_flag | outlierflag_gcc_mean | outlierflag_gcc_50 | outlierflag_gcc_75 | outlierflag_gcc_90 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2012-05-04 | 2012 | 125 | 2 | alligatorriver_2012_05_03_120110.jpg | 106.30031 | 115.7373 | 55.34694 | 0.41724 | 0.38322 | ⋯ | 0.00038 | 0.38285 | 0.38304 | 0.38315 | 70.15241 | NA | NA | NA | NA | NA |
2012-05-07 | 2012 | 128 | 56 | alligatorriver_2012_05_07_120109.jpg | 104.66830 | 114.4270 | 57.99294 | 0.41296 | 0.37774 | ⋯ | 0.00839 | 0.37729 | 0.38064 | 0.38655 | 71.54443 | NA | NA | NA | NA | NA |
2012-05-10 | 2012 | 131 | 62 | alligatorriver_2012_05_10_113109.jpg | 94.53853 | 114.4593 | 68.72398 | 0.41214 | 0.34041 | ⋯ | 0.01312 | 0.38170 | 0.38501 | 0.38677 | 72.32268 | NA | NA | NA | NA | NA |
2012-05-13 | 2012 | 134 | 62 | alligatorriver_2012_05_13_120110.jpg | 104.59880 | 113.6020 | 57.22023 | 0.41247 | 0.37978 | ⋯ | 0.00911 | 0.37968 | 0.38395 | 0.38772 | 73.05576 | NA | NA | NA | NA | NA |
2012-05-16 | 2012 | 137 | 57 | alligatorriver_2012_05_16_113128.jpg | 102.15546 | 111.6633 | 53.31193 | 0.41801 | 0.38242 | ⋯ | 0.00946 | 0.36699 | 0.37010 | 0.37619 | 73.74132 | NA | NA | NA | NA | NA |
2012-05-19 | 2012 | 140 | 56 | alligatorriver_2012_05_19_120110.jpg | 104.56388 | 114.9894 | 58.09885 | 0.41415 | 0.37660 | ⋯ | 0.00739 | 0.37182 | 0.37666 | 0.38113 | 74.37753 | NA | NA | NA | NA | NA |
options(repr.plot.width = 8)
options(repr.plot.height = 2.5)
p = ggplot(df,aes(x=date,y=gcc_90)) + geom_line(col='green') + geom_point(size=.5, na.rm=TRUE)
p
## plot gcc_90 vs doy for each year
options(repr.plot.width = 8)
options(repr.plot.height = 3.0)
df$year = factor(df$year,levels=seq(ystart,yend))
p = ggplot(df,aes(x=doy,y=gcc_90,col=year)) + geom_line(alpha=.5, na.rm=TRUE)
p = p + geom_point(alpha=.5, na.rm=TRUE)
p