library(knitr) opts_chunk$set(out.extra='style="display:block; margin: auto"' #, fig.align="center" , fig.width=4.3, fig.height=3.2, dev.args=list(pointsize=10) , message=FALSE , results='hold' ) knit_hooks$set(spar = function(before, options, envir) { if (before){ par( las=1 ) #also y axis labels horizontal par(mar=c(2.0,3.3,0,0)+0.3 ) #margins par(tck=0.02 ) #axe-tick length inside plots par(mgp=c(1.1,0.2,0) ) #positioning of axis title, axis labels, axis } }) # genVigs("severalCycles")
isDevelopMode <- TRUE library(twDev) setwd('..');loadPkg()
if(!exists("isDevelopMode")) library(RespChamberProc) set.seed(0815) # for reproducible results
First, the data is loaded. Here, directly from zipped logger-output.
fName <- system.file("genData/SMANIE_Chamber1_26032015.zip", package = "RespChamberProc") if( nzchar(fName) ){ ds <- ds0 <- readDat(unz(fName, file=unzip(fName, list=TRUE)[1,"Name"] ),tz="UTC") } head(ds) plot( CO2_LI840 ~ TIMESTAMP, ds, ylab="CO2 (ppm)", xlab="Time")
The dataset contains several measurment cycles of light and dark chambers with increasing or decreasing concentations respectively.
First, we correct the pressure to standard units and correct the CO2 concentrations for water vapour.
ds$Pa <- ds0$AirPres * 100 # convert hPa to Pa ds$CO2_dry <- corrConcDilution(ds, colConc = "CO2_LI840", colVapour = "H2O_LI840") ds$H2O_dry <- corrConcDilution(ds, colConc = "H2O_LI840", colVapour = "H2O_LI840") ds$VPD <- calcVPD( ds$SurTemp, ds$Pa, ds$H2O_LI840)
In order to process each measurement cycle independently, we first determine parts of the time series that are contiguous, i.e. without gaps and without change of an index variable, here variable collar
.
dsChunk <- subsetContiguous(ds, colTime="TIMESTAMP", colIndex="Collar") head(dsChunk)
The new modified contains a new variable, iChunk
, holding a factor that
changes with different measurment cycles.
This factor can be used to select subset of single measurement cycles.
dss <- subset(dsChunk, iChunk==15) plot( CO2_dry ~ TIMESTAMP, dss, ylab="CO2 (ppm)", xlab="time (Minute:Second)")
Function calcClosedChamberFluxForChunks
helps you with subsetting the data and applying function calcClosedChamberFlux
to each subset.
# for demonstration use only the first 20 cycles dsChunk20 <- subset(dsChunk, as.integer(iChunk) <= 20) chamberVol=0.6*0.6*0.6 # chamber was a cube of 0.6m length surfaceArea=0.6*0.6 resChunks1 <- calcClosedChamberFluxForChunks(dsChunk20, colTemp="T_LI840" ,fRegress = c(lin = regressFluxLinear, tanh = regressFluxTanh) # linear and saturating shape ,debugInfo=list(omitEstimateLeverage=TRUE) # faster ,volume=chamberVol ,area=surfaceArea ) head(resChunks1)
The results are similar as for calcClosedChamberFlux
, unless there are several rows identified by additional key column iChunk.
library(twDev) reloadPkg("RespChamberProc")
Plot the results to dectect problems.
library(ggplot2) plots <- plotCampaignConcSeries( dsChunk20, resChunks1, plotsPerPage=64L) print(plots$plot[[1]]) # print the first page
If argument fileName
is provided to plotCampaignConcSeries
. All plots are
written to a pdf. If there are more cycles, i.e. plots, than argument plotsPerPage
(default 64) there will be several pages in the pdf.
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