#' ---
#' title: "Demo: How to prepare daily data for PESCO"
#' author: "Giovanni Bonafe'"
#' date: "March 23rd, 2015"
#' ---
#' ## Demo: How to prepare daily data for PESCO
#' You can reproduce the following R code with
#' ```demo(daily.synthesis)``` after loading package ```pesco```.
## load package
require(pesco)
#' With ```data()``` you load some example datasets.
#' See ```example/prepare-datasets.R``` to prepare these datasets
#' from ASCII and NetCDF files.
## load hourly observation
data(NO2.obs)
#' With ```str(NO2.obs)``` you can compactly display the structure
#' of the object ```NO2.obs```.
## calculate daily maxima
NO2.obs.max <- dailyObs(NO2.obs,statistic="max",pollutant="NO2")
boxplot(data=NO2.obs.max, NO2~Time, range=0, border="orange",
col="orange", lty=1)
#' After calculating daily maxima and averages of ```NO2.obs```,
#' we can remove it. Obviously, ```boxplot()``` is not required to
#' prepare the data, it is just useful to have an idea of what you did.
## calculate daily averages
NO2.obs.ave <- dailyObs(NO2.obs,statistic="mean",pollutant="NO2")
rm(NO2.obs)
boxplot(data=NO2.obs.max, NO2~Time, range=0, border="orange",
col="orange", lty=1)
boxplot(data=NO2.obs.ave, NO2~Time, range=0, border="olivedrab",
col="olivedrab", lty=1, add=T, xaxt="n", yaxt="n")
## load hourly CTM concentrations
data(PM10.ctm)
#' After calculating daily averages of ```PM10.ctm```,
#' we can remove it. To plot ```PM10.ctm.ave``` we use
#' ```filled.contour```. Note that ```PM10.ctm.ave``` is a list,
#' and its elements ```PM10.ctm.ave$data``` is an array with 3 dimensions.
#' Therefore we use ```PM10.ctm.ave$data[,,1]``` to select the first day.
## calculate daily averages
PM10.ctm.ave <- dailyCtm(PM10.ctm, statistic="mean")
rm(PM10.ctm)
library(fields)
filled.contour(PM10.ctm.ave$data[,,1],color.palette=tim.colors)
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