Nothing
R/Example.R
In REddyProc: Data processing and plotting utilities of (half-)hourly eddy-covariance measurements
sEddyProc.example <- function( ) {
##title<<
## sEddyProc - Example code
##description<<
## Dummy function to show code example for sEddyProc provided below
##author<<
## AMM
# Empty function just to write attribute with example code for documenation
}
attr(sEddyProc.example,'ex') <- function( ){
#+++ Simple example code for using the sEddyProc reference class +++
if( FALSE ) { #Do not always execute example code (e.g. on package installation)
#library(REddyProc)
#+++ Load data with one header and one unit row from (tab-delimited) text file
Dir.s <- paste(system.file(package='REddyProc'), 'examples', sep='/')
EddyData.F <- fLoadTXTIntoDataframe('Example_DETha98.txt', Dir.s)
# note: use \code{fFilterAttr} to subset rows while keeping the units attributes
#+++ If not provided, calculate VPD from Tair and rH
EddyData.F <- cbind(EddyData.F,VPD=fCalcVPDfromRHandTair(EddyData.F$rH, EddyData.F$Tair))
#+++ Add time stamp in POSIX time format
EddyDataWithPosix.F <- fConvertTimeToPosix(EddyData.F, 'YDH', Year.s='Year', Day.s='DoY', Hour.s='Hour')
#+++ Initalize R5 reference class sEddyProc for processing of eddy data
#+++ with all variables needed for processing later
EddyProc.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg','Tair','VPD', 'Ustar'))
EddyProc.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
#+++ Generate plots of all data in directory \plots (of current R working dir)
EddyProc.C$sPlotHHFluxes('NEE')
EddyProc.C$sPlotFingerprint('Rg')
EddyProc.C$sPlotDiurnalCycle('Tair')
#+++ Plot individual months/years to screen (of current R graphics device)
EddyProc.C$sPlotHHFluxesY('NEE', Year.i=1998)
EddyProc.C$sPlotFingerprintY('NEE', Year.i=1998)
#+++ Fill gaps in variables with MDS gap filling algorithm (without prior ustar filtering)
EddyProc.C$sMDSGapFill('NEE', FillAll.b=TRUE) #Fill all values to estimate flux uncertainties
EddyProc.C$sMDSGapFill('Rg', FillAll.b=FALSE) #Fill only the gaps for the meteo condition, e.g. 'Rg'
#+++ Example plots of filled data to screen or to directory \plots
EddyProc.C$sPlotFingerprintY('NEE_f', Year.i=1998)
EddyProc.C$sPlotDailySumsY('NEE_f','NEE_fsd', Year.i=1998) #Plot of sums with uncertainties
EddyProc.C$sPlotDailySums('NEE_f','NEE_fsd')
#+++ Partition NEE into GPP and respiration
EddyProc.C$sMDSGapFill('Tair', FillAll.b=FALSE) # Gap-filled Tair (and NEE) needed for partitioning
EddyProc.C$sMDSGapFill('VPD', FillAll.b=FALSE) # Gap-filled Tair (and NEE) needed for partitioning
EddyProc.C$sMRFluxPartition() # night time partitioning -> Reco, GPP
EddyProc.C$sGLFluxPartition() # day time partitioning -> Reco_DT, GPP_DT
#EddyProc.C$sGLFluxPartition(controlGLPart.l=partGLControl(isBoundLowerNEEUncertainty=FALSE)) # day time partitioning -> Reco_DT, GPP_DT
#plot( EddyProc.C$sTEMP$GPP_DT ~ EddyProc.C$sTEMP$GPP_f); abline(0,1)
#plot( -EddyProc.C$sTEMP$GPP_DT + EddyProc.C$sTEMP$Reco_DT ~ EddyProc.C$sTEMP$NEE_f ); abline(0,1)
#names(EddyProc.C$sTEMP)
# there are some constraints, that might be too strict for some datasets
# e.g. in the tropics the required temperature range might be too large.
# Its possible to change these constraints
#EddyProc.C$sMRFluxPartition(parsE0Regression=list(TempRange.n=2.0, optimAlgorithm="LM") )
#+++ Example plots of calculated GPP and respiration
EddyProc.C$sPlotFingerprintY('GPP_f', Year.i=1998)
EddyProc.C$sPlotFingerprint('GPP_f')
EddyProc.C$sPlotHHFluxesY('Reco', Year.i=1998)
EddyProc.C$sPlotHHFluxes('Reco')
#+++ Processing with ustar filtering before
EddyProc.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg','Tair','VPD', 'Ustar'))
EddyProc.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
# estimating the thresholds based on the data
(uStarTh <- EddyProc.C$sEstUstarThreshold()$uStarTh)
# plot saturation of NEE with UStar for one season
EddyProc.C$sPlotNEEVersusUStarForSeason( levels(uStarTh$season)[3] )
# Gapfilling by default it takes the annually aggregated estimate is used to mark periods with low uStar
# for other options see Example 4
EddyProc.C$sMDSGapFillAfterUstar('NEE')
colnames(EddyProc.C$sExportResults()) # Note the collumns with suffix _WithUstar
EddyProc.C$sMDSGapFill('Tair', FillAll.b=FALSE)
EddyProc.C$sMRFluxPartition(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1, Suffix.s='WithUstar') # Note suffix
EddyProc.C$sMRFluxPartition(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1, Suffix.s='WithUstar') # Note suffix
#+++ Export gap filled and partitioned data to standard data frame
FilledEddyData.F <- EddyProc.C$sExportResults()
#+++ Save results into (tab-delimited) text file in directory \out
CombinedData.F <- cbind(EddyData.F, FilledEddyData.F)
#+++ May rename variables to correspond to Ameriflux
colnames(CombinedDataAmeriflux.F <- renameVariablesInDataframe(CombinedData.F, getBGC05ToAmerifluxVariableNameMapping() ))
CombinedDataAmeriflux.F$TIMESTAMP_END <- POSIXctToBerkeleyJulianDate( EddyProc.C$sExportData()[[1]] )
head(tmp <- BerkeleyJulianDateToPOSIXct( CombinedDataAmeriflux.F$TIMESTAMP_END ))
#colnames(tmp <- renameVariablesInDataframe(CombinedData.F, getAmerifluxToBGC05VariableNameMapping() ))
fWriteDataframeToFile(CombinedData.F, 'DE-Tha-Results.txt', 'out')
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Example 1 for advanced users: Processing different setups on the same site data
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Initialize new sEddyProc processing class
EddySetups.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg','Tair','VPD','Ustar'))
EddySetups.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
#+++ When running several processing setup, a string suffix declaration is needed
#+++ Here: Gap filling with and without ustar threshold
EddySetups.C$sMDSGapFill('NEE', FillAll.b=FALSE, Suffix.s='NoUstar')
EddySetups.C$sMDSGapFillAfterUstar('NEE', FillAll.b=FALSE, UstarThres.df=0.3, UstarSuffix.s='Thres1')
EddySetups.C$sMDSGapFillAfterUstar('NEE', FillAll.b=FALSE, UstarThres.df=0.4, UstarSuffix.s='Thres2')
EddySetups.C$sMDSGapFill('Tair', FillAll.b=FALSE) # Gap-filled Tair needed for partitioning
EddySetups.C$sMDSGapFill('VPD', FillAll.b=FALSE) # Gap-filled VPD needed for daytime partitioning
colnames(EddySetups.C$sExportResults()) # Note the suffix in output columns
#+++ Flux partitioning of the different gap filling setups
EddySetups.C$sMRFluxPartition(Suffix.s='NoUstar')
EddySetups.C$sMRFluxPartition(Suffix.s='Thres1')
EddySetups.C$sMRFluxPartition(Suffix.s='Thres2')
EddySetups.C$sGLFluxPartition(Suffix.s='NoUstar')
colnames(EddySetups.C$sExportResults()) # Note the suffix in output columns also of GPP, Reco, GPP_DT, and Reco_DT
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Example 2 for advanced users: Extended usage of the gap filling algorithm
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Add some (non-sense) example vectors:
#+++ Quality flag vector (e.g. from applying ustar filter)
QF.V.n <- rep(c(1,0,1,0,1,0,0,0,0,0), nrow(EddyData.F)/10)
#+++ Dummy step function vector to simulate e.g. high/low water table
Step.V.n <- ifelse(EddyData.F$DoY < 200 | EddyData.F$DoY > 250, 0, 1)
#+++ Initialize new sEddyProc processing class with more columns
EddyTest.C <- sEddyProc$new('DE-Tha', cbind(EddyDataWithPosix.F, Step=Step.V.n, QF=QF.V.n),
c('NEE', 'LE', 'H', 'Rg', 'Tair', 'Tsoil', 'rH', 'VPD', 'QF', 'Step'))
EddyTest.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
#+++ Gap fill variable with (non-default) variables and limits including preselection of data with quality flag QF==0
EddyTest.C$sMDSGapFill('LE', QFVar.s='QF', QFValue.n=0, V1.s='Rg', T1.n=30, V2.s='Tsoil', T2.n=2, 'Step', 0.1)
#+++ Use individual gap filling subroutines with different window sizes and up to five variables and limits
EddyTest.C$sFillInit('NEE') #Initialize 'NEE' as variable to fill
Result_Step1.F <- EddyTest.C$sFillLUT(3, 'Rg',50, 'rH',30, 'Tair',2.5, 'Tsoil',2, 'Step',0.5)
Result_Step2.F <- EddyTest.C$sFillLUT(6, 'Tair',2.5, 'VPD',3, 'Step',0.5)
Result_Step3.F <- EddyTest.C$sFillMDC(3)
EddyTest.C$sPlotHHFluxesY('VAR_fall', Year.i=1998) #Individual fill result columns are called 'VAR_...'
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Example 3 for advanced users: Explicit demonstration of MDS gap filling algorithm for filling NEE
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Initialize new sEddyProc processing class
EddyTestMDS.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE', 'Rg', 'Tair', 'VPD'))
EddyTestMDS.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
#Initialize 'NEE' as variable to fill
EddyTestMDS.C$sFillInit('NEE')
# Set variables and tolerance intervals
# twutz: \u00B1 is the unicode for '+over-'
V1.s='Rg'; T1.n=50 # Global radiation 'Rg' within \u00B150 W m-2
V2.s='VPD'; T2.n=5 # Vapour pressure deficit 'VPD' within 5 hPa
V3.s='Tair'; T3.n=2.5 # Air temperature 'Tair' within \u00B12.5 degC
# Step 1: Look-up table with window size \u00B17 days
Result_Step1.F <- EddyTestMDS.C$sFillLUT(7, V1.s, T1.n, V2.s, T2.n, V3.s, T3.n)
# Step 2: Look-up table with window size \u00B114 days
Result_Step2.F <- EddyTestMDS.C$sFillLUT(14, V1.s, T1.n, V2.s, T2.n, V3.s, T3.n)
# Step 3: Look-up table with window size \u00B17 days, Rg only
Result_Step3.F <- EddyTestMDS.C$sFillLUT(7, V1.s, T1.n)
# Step 4: Mean diurnal course with window size 0 (same day)
Result_Step4.F <- EddyTestMDS.C$sFillMDC(0)
# Step 5: Mean diurnal course with window size \u00B11, \u00B12 days
Result_Step5a.F <- EddyTestMDS.C$sFillMDC(1)
Result_Step5b.F <- EddyTestMDS.C$sFillMDC(2)
# Step 6: Look-up table with window size \u00B121, \u00B128, ..., \u00B170
for( WinDays.i in seq(21,70,7) ) Result_Step6.F <- EddyTestMDS.C$sFillLUT(WinDays.i, V1.s, T1.n, V2.s, T2.n, V3.s, T3.n)
# Step 7: Look-up table with window size \u00B114, \u00B121, ..., \u00B170, Rg only
for( WinDays.i in seq(14,70,7) ) Result_Step7.F <- EddyTestMDS.C$sFillLUT(WinDays.i, V1.s, T1.n)
# Step 8: Mean diurnal course with window size \u00B17, \u00B114, ..., \u00B1210 days
for( WinDays.i in seq(7,210,7) ) Result_Step8.F <- EddyTestMDS.C$sFillMDC(WinDays.i)
# Export results, columns are named 'VAR_'
FilledEddyData.F <- EddyTestMDS.C$sExportResults()
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Example 4 for advanced users: Processing of different UStar-Threshold setups
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Provide a single user-defined uStarThreshold
EddySetups.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg','Tair','VPD','Ustar'))
EddySetups.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
Ustar.V.n <- 0.46
EddySetups.C$sMDSGapFillAfterUstar('NEE', UstarThres.df=Ustar.V.n)
# Type 'vignette(DEGebExample)' to view an example
# - using tailored seasons of differing uStar dynamics with vegetation changes (crop)
# - using seasonal instead of annual uStar threshold estimates in gapfilling
# - Bootstrapping uncertainty associated with uStar Threshold estimation
# - Using change point detection instead of moving point method
# The vignette is only available if REddyProc was installed from binary package.
# A version can be seen at https://github.com/bgctw/REddyProc/blob/master/vignettes/DEGebExample.md
}
}
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sEddyProc.example <- function( ) {
##title<<
## sEddyProc - Example code
##description<<
## Dummy function to show code example for sEddyProc provided below
##author<<
## AMM
# Empty function just to write attribute with example code for documenation
}
attr(sEddyProc.example,'ex') <- function( ){
#+++ Simple example code for using the sEddyProc reference class +++
if( FALSE ) { #Do not always execute example code (e.g. on package installation)
#library(REddyProc)
#+++ Load data with one header and one unit row from (tab-delimited) text file
Dir.s <- paste(system.file(package='REddyProc'), 'examples', sep='/')
EddyData.F <- fLoadTXTIntoDataframe('Example_DETha98.txt', Dir.s)
# note: use \code{fFilterAttr} to subset rows while keeping the units attributes
#+++ If not provided, calculate VPD from Tair and rH
EddyData.F <- cbind(EddyData.F,VPD=fCalcVPDfromRHandTair(EddyData.F$rH, EddyData.F$Tair))
#+++ Add time stamp in POSIX time format
EddyDataWithPosix.F <- fConvertTimeToPosix(EddyData.F, 'YDH', Year.s='Year', Day.s='DoY', Hour.s='Hour')
#+++ Initalize R5 reference class sEddyProc for processing of eddy data
#+++ with all variables needed for processing later
EddyProc.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg','Tair','VPD', 'Ustar'))
EddyProc.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
#+++ Generate plots of all data in directory \plots (of current R working dir)
EddyProc.C$sPlotHHFluxes('NEE')
EddyProc.C$sPlotFingerprint('Rg')
EddyProc.C$sPlotDiurnalCycle('Tair')
#+++ Plot individual months/years to screen (of current R graphics device)
EddyProc.C$sPlotHHFluxesY('NEE', Year.i=1998)
EddyProc.C$sPlotFingerprintY('NEE', Year.i=1998)
#+++ Fill gaps in variables with MDS gap filling algorithm (without prior ustar filtering)
EddyProc.C$sMDSGapFill('NEE', FillAll.b=TRUE) #Fill all values to estimate flux uncertainties
EddyProc.C$sMDSGapFill('Rg', FillAll.b=FALSE) #Fill only the gaps for the meteo condition, e.g. 'Rg'
#+++ Example plots of filled data to screen or to directory \plots
EddyProc.C$sPlotFingerprintY('NEE_f', Year.i=1998)
EddyProc.C$sPlotDailySumsY('NEE_f','NEE_fsd', Year.i=1998) #Plot of sums with uncertainties
EddyProc.C$sPlotDailySums('NEE_f','NEE_fsd')
#+++ Partition NEE into GPP and respiration
EddyProc.C$sMDSGapFill('Tair', FillAll.b=FALSE) # Gap-filled Tair (and NEE) needed for partitioning
EddyProc.C$sMDSGapFill('VPD', FillAll.b=FALSE) # Gap-filled Tair (and NEE) needed for partitioning
EddyProc.C$sMRFluxPartition() # night time partitioning -> Reco, GPP
EddyProc.C$sGLFluxPartition() # day time partitioning -> Reco_DT, GPP_DT
#EddyProc.C$sGLFluxPartition(controlGLPart.l=partGLControl(isBoundLowerNEEUncertainty=FALSE)) # day time partitioning -> Reco_DT, GPP_DT
#plot( EddyProc.C$sTEMP$GPP_DT ~ EddyProc.C$sTEMP$GPP_f); abline(0,1)
#plot( -EddyProc.C$sTEMP$GPP_DT + EddyProc.C$sTEMP$Reco_DT ~ EddyProc.C$sTEMP$NEE_f ); abline(0,1)
#names(EddyProc.C$sTEMP)
# there are some constraints, that might be too strict for some datasets
# e.g. in the tropics the required temperature range might be too large.
# Its possible to change these constraints
#EddyProc.C$sMRFluxPartition(parsE0Regression=list(TempRange.n=2.0, optimAlgorithm="LM") )
#+++ Example plots of calculated GPP and respiration
EddyProc.C$sPlotFingerprintY('GPP_f', Year.i=1998)
EddyProc.C$sPlotFingerprint('GPP_f')
EddyProc.C$sPlotHHFluxesY('Reco', Year.i=1998)
EddyProc.C$sPlotHHFluxes('Reco')
#+++ Processing with ustar filtering before
EddyProc.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg','Tair','VPD', 'Ustar'))
EddyProc.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
# estimating the thresholds based on the data
(uStarTh <- EddyProc.C$sEstUstarThreshold()$uStarTh)
# plot saturation of NEE with UStar for one season
EddyProc.C$sPlotNEEVersusUStarForSeason( levels(uStarTh$season)[3] )
# Gapfilling by default it takes the annually aggregated estimate is used to mark periods with low uStar
# for other options see Example 4
EddyProc.C$sMDSGapFillAfterUstar('NEE')
colnames(EddyProc.C$sExportResults()) # Note the collumns with suffix _WithUstar
EddyProc.C$sMDSGapFill('Tair', FillAll.b=FALSE)
EddyProc.C$sMRFluxPartition(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1, Suffix.s='WithUstar') # Note suffix
EddyProc.C$sMRFluxPartition(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1, Suffix.s='WithUstar') # Note suffix
#+++ Export gap filled and partitioned data to standard data frame
FilledEddyData.F <- EddyProc.C$sExportResults()
#+++ Save results into (tab-delimited) text file in directory \out
CombinedData.F <- cbind(EddyData.F, FilledEddyData.F)
#+++ May rename variables to correspond to Ameriflux
colnames(CombinedDataAmeriflux.F <- renameVariablesInDataframe(CombinedData.F, getBGC05ToAmerifluxVariableNameMapping() ))
CombinedDataAmeriflux.F$TIMESTAMP_END <- POSIXctToBerkeleyJulianDate( EddyProc.C$sExportData()[[1]] )
head(tmp <- BerkeleyJulianDateToPOSIXct( CombinedDataAmeriflux.F$TIMESTAMP_END ))
#colnames(tmp <- renameVariablesInDataframe(CombinedData.F, getAmerifluxToBGC05VariableNameMapping() ))
fWriteDataframeToFile(CombinedData.F, 'DE-Tha-Results.txt', 'out')
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Example 1 for advanced users: Processing different setups on the same site data
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Initialize new sEddyProc processing class
EddySetups.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg','Tair','VPD','Ustar'))
EddySetups.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
#+++ When running several processing setup, a string suffix declaration is needed
#+++ Here: Gap filling with and without ustar threshold
EddySetups.C$sMDSGapFill('NEE', FillAll.b=FALSE, Suffix.s='NoUstar')
EddySetups.C$sMDSGapFillAfterUstar('NEE', FillAll.b=FALSE, UstarThres.df=0.3, UstarSuffix.s='Thres1')
EddySetups.C$sMDSGapFillAfterUstar('NEE', FillAll.b=FALSE, UstarThres.df=0.4, UstarSuffix.s='Thres2')
EddySetups.C$sMDSGapFill('Tair', FillAll.b=FALSE) # Gap-filled Tair needed for partitioning
EddySetups.C$sMDSGapFill('VPD', FillAll.b=FALSE) # Gap-filled VPD needed for daytime partitioning
colnames(EddySetups.C$sExportResults()) # Note the suffix in output columns
#+++ Flux partitioning of the different gap filling setups
EddySetups.C$sMRFluxPartition(Suffix.s='NoUstar')
EddySetups.C$sMRFluxPartition(Suffix.s='Thres1')
EddySetups.C$sMRFluxPartition(Suffix.s='Thres2')
EddySetups.C$sGLFluxPartition(Suffix.s='NoUstar')
colnames(EddySetups.C$sExportResults()) # Note the suffix in output columns also of GPP, Reco, GPP_DT, and Reco_DT
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Example 2 for advanced users: Extended usage of the gap filling algorithm
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Add some (non-sense) example vectors:
#+++ Quality flag vector (e.g. from applying ustar filter)
QF.V.n <- rep(c(1,0,1,0,1,0,0,0,0,0), nrow(EddyData.F)/10)
#+++ Dummy step function vector to simulate e.g. high/low water table
Step.V.n <- ifelse(EddyData.F$DoY < 200 | EddyData.F$DoY > 250, 0, 1)
#+++ Initialize new sEddyProc processing class with more columns
EddyTest.C <- sEddyProc$new('DE-Tha', cbind(EddyDataWithPosix.F, Step=Step.V.n, QF=QF.V.n),
c('NEE', 'LE', 'H', 'Rg', 'Tair', 'Tsoil', 'rH', 'VPD', 'QF', 'Step'))
EddyTest.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
#+++ Gap fill variable with (non-default) variables and limits including preselection of data with quality flag QF==0
EddyTest.C$sMDSGapFill('LE', QFVar.s='QF', QFValue.n=0, V1.s='Rg', T1.n=30, V2.s='Tsoil', T2.n=2, 'Step', 0.1)
#+++ Use individual gap filling subroutines with different window sizes and up to five variables and limits
EddyTest.C$sFillInit('NEE') #Initialize 'NEE' as variable to fill
Result_Step1.F <- EddyTest.C$sFillLUT(3, 'Rg',50, 'rH',30, 'Tair',2.5, 'Tsoil',2, 'Step',0.5)
Result_Step2.F <- EddyTest.C$sFillLUT(6, 'Tair',2.5, 'VPD',3, 'Step',0.5)
Result_Step3.F <- EddyTest.C$sFillMDC(3)
EddyTest.C$sPlotHHFluxesY('VAR_fall', Year.i=1998) #Individual fill result columns are called 'VAR_...'
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Example 3 for advanced users: Explicit demonstration of MDS gap filling algorithm for filling NEE
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Initialize new sEddyProc processing class
EddyTestMDS.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE', 'Rg', 'Tair', 'VPD'))
EddyTestMDS.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
#Initialize 'NEE' as variable to fill
EddyTestMDS.C$sFillInit('NEE')
# Set variables and tolerance intervals
# twutz: \u00B1 is the unicode for '+over-'
V1.s='Rg'; T1.n=50 # Global radiation 'Rg' within \u00B150 W m-2
V2.s='VPD'; T2.n=5 # Vapour pressure deficit 'VPD' within 5 hPa
V3.s='Tair'; T3.n=2.5 # Air temperature 'Tair' within \u00B12.5 degC
# Step 1: Look-up table with window size \u00B17 days
Result_Step1.F <- EddyTestMDS.C$sFillLUT(7, V1.s, T1.n, V2.s, T2.n, V3.s, T3.n)
# Step 2: Look-up table with window size \u00B114 days
Result_Step2.F <- EddyTestMDS.C$sFillLUT(14, V1.s, T1.n, V2.s, T2.n, V3.s, T3.n)
# Step 3: Look-up table with window size \u00B17 days, Rg only
Result_Step3.F <- EddyTestMDS.C$sFillLUT(7, V1.s, T1.n)
# Step 4: Mean diurnal course with window size 0 (same day)
Result_Step4.F <- EddyTestMDS.C$sFillMDC(0)
# Step 5: Mean diurnal course with window size \u00B11, \u00B12 days
Result_Step5a.F <- EddyTestMDS.C$sFillMDC(1)
Result_Step5b.F <- EddyTestMDS.C$sFillMDC(2)
# Step 6: Look-up table with window size \u00B121, \u00B128, ..., \u00B170
for( WinDays.i in seq(21,70,7) ) Result_Step6.F <- EddyTestMDS.C$sFillLUT(WinDays.i, V1.s, T1.n, V2.s, T2.n, V3.s, T3.n)
# Step 7: Look-up table with window size \u00B114, \u00B121, ..., \u00B170, Rg only
for( WinDays.i in seq(14,70,7) ) Result_Step7.F <- EddyTestMDS.C$sFillLUT(WinDays.i, V1.s, T1.n)
# Step 8: Mean diurnal course with window size \u00B17, \u00B114, ..., \u00B1210 days
for( WinDays.i in seq(7,210,7) ) Result_Step8.F <- EddyTestMDS.C$sFillMDC(WinDays.i)
# Export results, columns are named 'VAR_'
FilledEddyData.F <- EddyTestMDS.C$sExportResults()
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Example 4 for advanced users: Processing of different UStar-Threshold setups
#++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#+++ Provide a single user-defined uStarThreshold
EddySetups.C <- sEddyProc$new('DE-Tha', EddyDataWithPosix.F, c('NEE','Rg','Tair','VPD','Ustar'))
EddySetups.C$sSetLocationInfo(Lat_deg.n=51.0, Long_deg.n=13.6, TimeZone_h.n=1) #Location of DE-Tharandt
Ustar.V.n <- 0.46
EddySetups.C$sMDSGapFillAfterUstar('NEE', UstarThres.df=Ustar.V.n)
# Type 'vignette(DEGebExample)' to view an example
# - using tailored seasons of differing uStar dynamics with vegetation changes (crop)
# - using seasonal instead of annual uStar threshold estimates in gapfilling
# - Bootstrapping uncertainty associated with uStar Threshold estimation
# - Using change point detection instead of moving point method
# The vignette is only available if REddyProc was installed from binary package.
# A version can be seen at https://github.com/bgctw/REddyProc/blob/master/vignettes/DEGebExample.md
}
}
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