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Type 'q()' to quit R. > # R file to be executed by run runEddyProc.sh or reddy_runLocal.sh > > # stop("under construction, come back on 27th of June") > # setwd("/Net/Groups/BGI/services/REddyProcWeb/work/001") > # setwd("/Net/Groups/BGI/services/REddyProcWeb/work/845486497") > # setwd("m:/services/REddyProcWeb/work/001") > # setwd("~/bgi/services/REddyProcWeb/work/debug") > > dumpfileBasename <- "RBatch_dump" > options(error = quote({ + dump.frames(dumpfileBasename,TRUE); stop(paste("dump in ",dumpfileBasename))})) > options(warn = 1) # print all warnings (instead of a summary at the end) > > # add REddyProc/Library as first library path, see readme in Library folder > .libPaths( c("../../Library", tail(.libPaths(),1)) ) > .libPaths() [1] "/Net/Groups/BGI/services/REddyProcWeb/Library" [2] "/Net/Groups/Services/LNX_Local64/SLES11/apps/R/R-3.6.1/lib64/R/library" > library(REddyProc) > > cat('Using REddyProc version ' + , paste(unlist(packageVersion("REddyProc")),collapse = "."),"\n") Using REddyProc version 1.2 > Sys.time() [1] "2021-07-15 23:03:16 CEST" > > source("../../R/readConfig.R") > configurationFileName = "post.dcf" > eddyProcConfiguration <- compileAndValidateEddyProcConfiguration(configurationFileName) Entries supplied by the web form: SiteID ustarFilter ustarFilterMethod "US-PFn" "YES" "RTw" ustarFilterSeasoning ustarFilterUncertainty isGapfilling "Continuous" "NO" "YES" partitioningMethods.. partitioningMethods...1 Latitude "Reichstein05" "Lasslop10" "45.939217" Longitude Timezone temperatureVarName "-90.282317" "-6" "Tair" isRemoveUstarSuffix email fileIdentifier "TRUE" "bbutterworth@wisc.edu" "396063093" REddyProcWebRevision "75" > sourceFunctions(eddyProcConfiguration) > > res_time <- system.time( + # call as function for easier debugging with dump + retval <- #try( # no dump produced within try + processEddyData(eddyProcConfiguration = eddyProcConfiguration) + #) + ) List of 18 $ siteId : Named chr "US-PFn" ..- attr(*, "names")= chr "SiteID" $ isToApplyUStarFiltering: Named logi TRUE ..- attr(*, "names")= chr "ustarFilter" $ isToApplyGapFilling : logi TRUE $ isToApplyPartitioning : logi TRUE $ temperatureDataVariable: Named chr "Tair" ..- attr(*, "names")= chr "temperatureVarName" $ debugFlags : chr "" $ isCatchingErrorsEnabled: logi TRUE $ useDevelopLibraryPath : logi FALSE $ isBootstrapUStar : logi FALSE $ partitioningMethods : chr [1:2] "Reichstein05" "Lasslop10" $ input_format : chr "onlinetool" $ output_format : chr "reddyproc12" $ figureFormat : chr "pdf" $ uStarSeasoning : Factor w/ 1 level "Continuous": 1 ..- attr(*, "names")= chr "ustarFilterSeasoning" $ uStarMethod : Factor w/ 1 level "RTw": 1 ..- attr(*, "names")= chr "ustarFilterMethod" $ timezone : num -6 $ latitude : num 45.9 $ longitude : num -90.3 Loaded file input.txt with the following variables (units): *** Year(-) DoY(-) Hour(-) NEE(umolm-2s-1) LE(Wm-2) H(Wm-2) Rg(Wm-2) Tair(degC) Tsoil(degC) rH(%) Ustar(ms-1) Number of '-9999' convertered to NA: 9967 Converted time format 'YDH' to POSIX with column name 'DateTime'. 'data.frame': 5568 obs. of 12 variables: $ DateTime: POSIXct, format: "2019-06-20 00:30:00" "2019-06-20 01:00:00" ... $ Year : int 2019 2019 2019 2019 2019 2019 2019 2019 2019 2019 ... ..- attr(*, "varnames")= chr "Year" ..- attr(*, "units")= chr "-" $ DoY : int 171 171 171 171 171 171 171 171 171 171 ... ..- attr(*, "varnames")= chr "DoY" ..- attr(*, "units")= chr "-" $ Hour : num 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 ... ..- attr(*, "varnames")= chr "Hour" ..- attr(*, "units")= chr "-" $ NEE : num 5.59 5.34 3.35 5.59 6.54 ... ..- attr(*, "varnames")= chr "NEE" ..- attr(*, "units")= chr "umolm-2s-1" $ LE : num NA NA NA NA -1.86 ... ..- attr(*, "varnames")= chr "LE" ..- attr(*, "units")= chr "Wm-2" $ H : num -33.8 -40.5 -30.3 -51.2 -48.8 ... ..- attr(*, "varnames")= chr "H" ..- attr(*, "units")= chr "Wm-2" $ Rg : num -4.32 -4.03 -3.97 -4.02 -4.01 ... ..- attr(*, "varnames")= chr "Rg" ..- attr(*, "units")= chr "Wm-2" $ Tair : num 10.81 10.44 10.13 9.75 9.34 ... ..- attr(*, "varnames")= chr "Tair" ..- attr(*, "units")= chr "degC" $ Tsoil : num 12.3 12.1 12 11.8 11.7 ... ..- attr(*, "varnames")= chr "Tsoil" ..- attr(*, "units")= chr "degC" $ rH : num 75.1 77.3 79.8 81.7 83.2 ... ..- attr(*, "varnames")= chr "rH" ..- attr(*, "units")= chr "%" $ Ustar : num 0.179 0.192 0.178 0.246 0.225 ... ..- attr(*, "varnames")= chr "Ustar" ..- attr(*, "units")= chr "ms-1" [1] "Calculating VPD from rH and Tair." Warning in fCheckOutsideRange(cbind(RelHumidity_Percent = rH), "RelHumidity_Percent", : fCalcVPDfromRHandTair:::fCheckOutsideRange::: Variable outside (plausible) range in 147 cases! Invalid values with 'RelHumidity_Percent < 0 | > 100': 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 101, 101, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 101, 100, 101, 101, 101, 101, 100, 100, 100, 100, 100, 100, 100, 100 ... Data variables picked for gap filling (dataVariablesToFill): NEE,LE,H,Rg,VPD,rH,Tair,Tsoil No additional columns picked to keep in processing Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c("<", -50), SubCallFunc.s) : sEddyProc.initialize:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 14 cases! Invalid values with 'NEE < -50': -61, -115, -70, -114, -96, -117, -72, -54, -69, -64, -68, -88, -63, -67 ... Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c(">", 100), SubCallFunc.s) : sEddyProc.initialize:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 1 cases! Invalid values with 'NEE > 100': 112 ... Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c("<", 0), SubCallFunc.s) : sEddyProc.initialize:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 1394 cases! Invalid values with 'Rg < 0': -4.32, -4.03, -3.97, -4.02, -4.01, -3.60, -2.31, -0.87, -4.12, -4.40, -4.41, -3.78, -4.31, -4.26, -4.08, -4.08, -4.28, -4.28, -3.63, -3.82, -3.55, -3.44, -3.44, -2.79, -3.59, -3.46, -3.20, -3.16, -3.54, -3.71, -3.85, -4.24, -3.79, -3.91, -4.34, -4.14, -3.98, -4.01, -4.13, -3.45, -2.47, -3.67, -2.59, -0.92, -0.82, -0.84, -0.93, -1.09, -0.81, -0.39 ... Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c("<", 0), SubCallFunc.s) : sEddyProc.initialize:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 147 cases! Invalid values with 'VPD < 0': -0.0114, -0.0177, -0.0281, -0.0298, -0.0451, -0.0385, -0.0173, -0.0013, -0.0306, -0.0070, -0.0254, -0.0278, -0.0097, -0.0049, -0.0186, -0.0271, -0.0700, -0.0827, -0.0952, -0.1181, -0.0622, -0.0187, -0.0151, -0.0057, -0.0364, -0.0157, -0.0257, -0.0159, -0.0133, -0.0024, -0.0059, -0.0097, -0.0182, -0.0308, -0.0588, -0.0465, -0.1039, -0.0670, -0.0884, -0.1049, -0.1307, -0.1253, -0.0912, -0.0832, -0.0878, -0.0027, -0.0083, -0.0402, -0.0154, -0.0046 ... New sEddyProc class for site 'US-PFn' [1] "------------- u* Threshold estimation ---------------" Warning in .estimateUStarSeason(...) : sEstUstarThreshold: too few finite records within season (n = 494) for 7 temperature classes. Need at least n = 700. Returning NA for this Season. Estimated UStar threshold of: 0.2 by using controls: taClasses UstarClasses 7 20 swThr minRecordsWithinTemp 10 100 minRecordsWithinSeason minRecordsWithinYear 160 3000 isUsingOneBigSeasonOnFewRecords 1 aggregationMode seasonYear season uStar 2 year 2019 0.2024744 [1] "------------- Gapfilling ---------------" Ustar filtering (u * Th_1 = 0.202474375), marked 32% of the data as gap Initialized variable 'NEE' with 2597 real gaps for gap filling of all 5568 values (to estimate uncertainties). Full MDS algorithm for gap filling of 'NEE.Ustar_uStar_fqc_0' with LUT(Rg, VPD, Tair) and MDC. Look up table with window size of 7 days with Rg VPD Tair .......................................................3469 Look up table with window size of 14 days with Rg VPD Tair ....................53 Look up table with window size of 7 days with Rg ....................34 Mean diurnal course with window size of 0 days: . ....................723 Mean diurnal course with window size of 1 days: . ............1019 Mean diurnal course with window size of 2 days: . ..193 Look up table with window size of 21 days with Rg VPD Tair 0 Look up table with window size of 28 days with Rg VPD Tair 0 Look up table with window size of 35 days with Rg VPD Tair 0 Look up table with window size of 42 days with Rg VPD Tair 0 Look up table with window size of 49 days with Rg VPD Tair 0 Look up table with window size of 56 days with Rg VPD Tair 0 Look up table with window size of 63 days with Rg VPD Tair 0 Look up table with window size of 70 days with Rg VPD Tair 0 Look up table with window size of 14 days with Rg 0 Look up table with window size of 21 days with Rg 0 Look up table with window size of 28 days with Rg 0 Look up table with window size of 35 days with Rg 0 Look up table with window size of 42 days with Rg 0 Look up table with window size of 49 days with Rg 0 Look up table with window size of 56 days with Rg 0 Look up table with window size of 63 days with Rg 0 Look up table with window size of 70 days with Rg 0 Mean diurnal course with window size of 7 days: . 77 Finished gap filling of 'NEE' in 2 seconds. Artificial gaps filled: 5568, real gaps filled: 2597, unfilled (long) gaps: 0. Initialized variable 'LE' with 1777 real gaps for gap filling of all 5568 values (to estimate uncertainties). Full MDS algorithm for gap filling of 'LE' with LUT(Rg, VPD, Tair) and MDC. Look up table with window size of 7 days with Rg VPD Tair .......................................................3487 Look up table with window size of 14 days with Rg VPD Tair ....................38 Look up table with window size of 7 days with Rg ....................31 Mean diurnal course with window size of 0 days: . ....................1014 Mean diurnal course with window size of 1 days: . .........875 Mean diurnal course with window size of 2 days: . .107 Look up table with window size of 21 days with Rg VPD Tair 0 Look up table with window size of 28 days with Rg VPD Tair 0 Look up table with window size of 35 days with Rg VPD Tair 0 Look up table with window size of 42 days with Rg VPD Tair 0 Look up table with window size of 49 days with Rg VPD Tair 0 Look up table with window size of 56 days with Rg VPD Tair 0 Look up table with window size of 63 days with Rg VPD Tair 0 Look up table with window size of 70 days with Rg VPD Tair 0 Look up table with window size of 14 days with Rg 0 Look up table with window size of 21 days with Rg 0 Look up table with window size of 28 days with Rg 0 Look up table with window size of 35 days with Rg 0 Look up table with window size of 42 days with Rg 0 Look up table with window size of 49 days with Rg 0 Look up table with window size of 56 days with Rg 0 Look up table with window size of 63 days with Rg 0 Look up table with window size of 70 days with Rg 0 Mean diurnal course with window size of 7 days: . 16 Finished gap filling of 'LE' in 1 seconds. Artificial gaps filled: 5568, real gaps filled: 1777, unfilled (long) gaps: 0. Initialized variable 'H' with 1561 real gaps for gap filling of all 5568 values (to estimate uncertainties). Full MDS algorithm for gap filling of 'H' with LUT(Rg, VPD, Tair) and MDC. Look up table with window size of 7 days with Rg VPD Tair .......................................................3489 Look up table with window size of 14 days with Rg VPD Tair ....................37 Look up table with window size of 7 days with Rg ....................30 Mean diurnal course with window size of 0 days: . ....................1119 Mean diurnal course with window size of 1 days: . ........792 Mean diurnal course with window size of 2 days: . .86 Look up table with window size of 21 days with Rg VPD Tair 0 Look up table with window size of 28 days with Rg VPD Tair 0 Look up table with window size of 35 days with Rg VPD Tair 0 Look up table with window size of 42 days with Rg VPD Tair 0 Look up table with window size of 49 days with Rg VPD Tair 0 Look up table with window size of 56 days with Rg VPD Tair 0 Look up table with window size of 63 days with Rg VPD Tair 0 Look up table with window size of 70 days with Rg VPD Tair 0 Look up table with window size of 14 days with Rg 0 Look up table with window size of 21 days with Rg 0 Look up table with window size of 28 days with Rg 0 Look up table with window size of 35 days with Rg 0 Look up table with window size of 42 days with Rg 0 Look up table with window size of 49 days with Rg 0 Look up table with window size of 56 days with Rg 0 Look up table with window size of 63 days with Rg 0 Look up table with window size of 70 days with Rg 0 Mean diurnal course with window size of 7 days: . 15 Finished gap filling of 'H' in 1 seconds. Artificial gaps filled: 5568, real gaps filled: 1561, unfilled (long) gaps: 0. Initialized variable 'Rg' with 2011 real gaps for gap filling. Restriced MDS algorithm for gap filling of 'Rg' with no meteo conditions and hence only MDC. Mean diurnal course with window size of 0 days: . ....................200 Mean diurnal course with window size of 1 days: . ..................1278 Mean diurnal course with window size of 2 days: . .....396 Mean diurnal course with window size of 7 days: . .137 Finished gap filling of 'Rg' in 0 seconds. Artificial gaps filled: 5568, real gaps filled: 2011, unfilled (long) gaps: 0. Initialized variable 'VPD' with 521 real gaps for gap filling. Limited MDS algorithm for gap filling of 'VPD' with LUT(Rg only) and MDC. Look up table with window size of 7 days with Rg .....0 Mean diurnal course with window size of 0 days: . .....61 Mean diurnal course with window size of 1 days: . ....409 Mean diurnal course with window size of 2 days: . 48 Look up table with window size of 14 days with Rg 0 Look up table with window size of 21 days with Rg 0 Look up table with window size of 28 days with Rg 0 Look up table with window size of 35 days with Rg 0 Look up table with window size of 42 days with Rg 0 Look up table with window size of 49 days with Rg 0 Look up table with window size of 56 days with Rg 0 Look up table with window size of 63 days with Rg 0 Look up table with window size of 70 days with Rg 0 Mean diurnal course with window size of 7 days: . 3 Finished gap filling of 'VPD' in 0 seconds. Artificial gaps filled: 5568, real gaps filled: 521, unfilled (long) gaps: 0. Initialized variable 'rH' with 521 real gaps for gap filling. Full MDS algorithm for gap filling of 'rH' with LUT(Rg, VPD, Tair) and MDC. Look up table with window size of 7 days with Rg VPD Tair .....0 Look up table with window size of 14 days with Rg VPD Tair .....0 Look up table with window size of 7 days with Rg .....0 Mean diurnal course with window size of 0 days: . .....61 Mean diurnal course with window size of 1 days: . ....409 Mean diurnal course with window size of 2 days: . 48 Look up table with window size of 21 days with Rg VPD Tair 0 Look up table with window size of 28 days with Rg VPD Tair 0 Look up table with window size of 35 days with Rg VPD Tair 0 Look up table with window size of 42 days with Rg VPD Tair 0 Look up table with window size of 49 days with Rg VPD Tair 0 Look up table with window size of 56 days with Rg VPD Tair 0 Look up table with window size of 63 days with Rg VPD Tair 0 Look up table with window size of 70 days with Rg VPD Tair 0 Look up table with window size of 14 days with Rg 0 Look up table with window size of 21 days with Rg 0 Look up table with window size of 28 days with Rg 0 Look up table with window size of 35 days with Rg 0 Look up table with window size of 42 days with Rg 0 Look up table with window size of 49 days with Rg 0 Look up table with window size of 56 days with Rg 0 Look up table with window size of 63 days with Rg 0 Look up table with window size of 70 days with Rg 0 Mean diurnal course with window size of 7 days: . 3 Finished gap filling of 'rH' in 0 seconds. Artificial gaps filled: 5568, real gaps filled: 521, unfilled (long) gaps: 0. Initialized variable 'Tair' with 521 real gaps for gap filling. Limited MDS algorithm for gap filling of 'Tair' with LUT(Rg only) and MDC. Look up table with window size of 7 days with Rg .....0 Mean diurnal course with window size of 0 days: . .....61 Mean diurnal course with window size of 1 days: . ....409 Mean diurnal course with window size of 2 days: . 48 Look up table with window size of 14 days with Rg 0 Look up table with window size of 21 days with Rg 0 Look up table with window size of 28 days with Rg 0 Look up table with window size of 35 days with Rg 0 Look up table with window size of 42 days with Rg 0 Look up table with window size of 49 days with Rg 0 Look up table with window size of 56 days with Rg 0 Look up table with window size of 63 days with Rg 0 Look up table with window size of 70 days with Rg 0 Mean diurnal course with window size of 7 days: . 3 Finished gap filling of 'Tair' in 0 seconds. Artificial gaps filled: 5568, real gaps filled: 521, unfilled (long) gaps: 0. Initialized variable 'Tsoil' with 836 real gaps for gap filling. Full MDS algorithm for gap filling of 'Tsoil' with LUT(Rg, VPD, Tair) and MDC. Look up table with window size of 7 days with Rg VPD Tair ........209 Look up table with window size of 14 days with Rg VPD Tair ......16 Look up table with window size of 7 days with Rg ......6 Mean diurnal course with window size of 0 days: . ......62 Mean diurnal course with window size of 1 days: . .....415 Mean diurnal course with window size of 2 days: . .64 Look up table with window size of 21 days with Rg VPD Tair 0 Look up table with window size of 28 days with Rg VPD Tair 0 Look up table with window size of 35 days with Rg VPD Tair 0 Look up table with window size of 42 days with Rg VPD Tair 0 Look up table with window size of 49 days with Rg VPD Tair 0 Look up table with window size of 56 days with Rg VPD Tair 0 Look up table with window size of 63 days with Rg VPD Tair 0 Look up table with window size of 70 days with Rg VPD Tair 0 Look up table with window size of 14 days with Rg 0 Look up table with window size of 21 days with Rg 0 Look up table with window size of 28 days with Rg 0 Look up table with window size of 35 days with Rg 0 Look up table with window size of 42 days with Rg 0 Look up table with window size of 49 days with Rg 0 Look up table with window size of 56 days with Rg 0 Look up table with window size of 63 days with Rg 0 Look up table with window size of 70 days with Rg 0 Mean diurnal course with window size of 7 days: . 64 Finished gap filling of 'Tsoil' in 0 seconds. Artificial gaps filled: 5568, real gaps filled: 836, unfilled (long) gaps: 0. Saved plot to: ./US-PFn_2019_FP_NEE.pdf Saved plot to: ./US-PFn_2019_Flux_NEE.pdf Saved plot to: ./US-PFn_2019_FP_LE.pdf Saved plot to: ./US-PFn_2019_Flux_LE.pdf Saved plot to: ./US-PFn_2019_FP_H.pdf Saved plot to: ./US-PFn_2019_Flux_H.pdf Saved plot to: ./US-PFn_2019_FP_Rg.pdf Saved plot to: ./US-PFn_2019_Flux_Rg.pdf Saved plot to: ./US-PFn_2019_FP_VPD.pdf Saved plot to: ./US-PFn_2019_Flux_VPD.pdf Saved plot to: ./US-PFn_2019_FP_rH.pdf Saved plot to: ./US-PFn_2019_Flux_rH.pdf Saved plot to: ./US-PFn_2019_FP_Tair.pdf Saved plot to: ./US-PFn_2019_Flux_Tair.pdf Saved plot to: ./US-PFn_2019_FP_Tsoil.pdf Saved plot to: ./US-PFn_2019_Flux_Tsoil.pdf Saved plot to: ./US-PFn_2019_FP_NEE_uStar_f.pdf Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: January! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: February! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: March! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: April! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: May! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: November! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month) : .sPlotDiurnalCycleM::: No data available for month: December! Saved plot to: ./US-PFn_2019_DC_NEE_uStar_f.pdf Saved plot to: ./US-PFn_2019_DSumU_NEE_uStar_f.pdf Saved plot to: ./US-PFn_2019_Flux_NEE_uStar_f.pdf Saved plot to: ./US-PFn_2019_FP_LE_f.pdf Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: January! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: February! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: March! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: April! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: May! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: November! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month) : .sPlotDiurnalCycleM::: No data available for month: December! Saved plot to: ./US-PFn_2019_DC_LE_f.pdf Saved plot to: ./US-PFn_2019_DSumU_LE_f.pdf Saved plot to: ./US-PFn_2019_Flux_LE_f.pdf Saved plot to: ./US-PFn_2019_FP_H_f.pdf Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: January! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: February! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: March! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: April! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: May! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: November! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month) : .sPlotDiurnalCycleM::: No data available for month: December! Saved plot to: ./US-PFn_2019_DC_H_f.pdf Saved plot to: ./US-PFn_2019_DSumU_H_f.pdf Saved plot to: ./US-PFn_2019_Flux_H_f.pdf Saved plot to: ./US-PFn_2019_FP_Rg_f.pdf Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: January! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: February! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: March! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: April! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: May! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: November! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month) : .sPlotDiurnalCycleM::: No data available for month: December! Saved plot to: ./US-PFn_2019_DC_Rg_f.pdf Saved plot to: ./US-PFn_2019_DSum_Rg_f.pdf Saved plot to: ./US-PFn_2019_Flux_Rg_f.pdf Saved plot to: ./US-PFn_2019_FP_VPD_f.pdf Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: January! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: February! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: March! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: April! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: May! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: November! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month) : .sPlotDiurnalCycleM::: No data available for month: December! Saved plot to: ./US-PFn_2019_DC_VPD_f.pdf Saved plot to: ./US-PFn_2019_DSum_VPD_f.pdf Saved plot to: ./US-PFn_2019_Flux_VPD_f.pdf Saved plot to: ./US-PFn_2019_FP_rH_f.pdf Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: January! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: February! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: March! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: April! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: May! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: November! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month) : .sPlotDiurnalCycleM::: No data available for month: December! Saved plot to: ./US-PFn_2019_DC_rH_f.pdf Saved plot to: ./US-PFn_2019_DSum_rH_f.pdf Saved plot to: ./US-PFn_2019_Flux_rH_f.pdf Saved plot to: ./US-PFn_2019_FP_Tair_f.pdf Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: January! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: February! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: March! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: April! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: May! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: November! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month) : .sPlotDiurnalCycleM::: No data available for month: December! Saved plot to: ./US-PFn_2019_DC_Tair_f.pdf Saved plot to: ./US-PFn_2019_DSum_Tair_f.pdf Saved plot to: ./US-PFn_2019_Flux_Tair_f.pdf Saved plot to: ./US-PFn_2019_FP_Tsoil_f.pdf Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: January! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: February! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: March! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: April! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: May! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month, Legend = F) : .sPlotDiurnalCycleM::: No data available for month: November! Warning in .self$.sPlotDiurnalCycleM(Var, QFVar, QFValue, Month) : .sPlotDiurnalCycleM::: No data available for month: December! Saved plot to: ./US-PFn_2019_DC_Tsoil_f.pdf Saved plot to: ./US-PFn_2019_DSum_Tsoil_f.pdf Saved plot to: ./US-PFn_2019_Flux_Tsoil_f.pdf [1] "------------- Flux Partitioning ---------------" Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c("<", -50), SubCallFunc.s) : sMRFluxPartition:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 16 cases! Invalid values with 'NEE_uStar_f < -50': -61, -57, -115, -70, -96, -117, -72, -54, -69, -64, -68, -88, -63, -67, -55, -55 ... Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c(">", 100), SubCallFunc.s) : sMRFluxPartition:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 1 cases! Invalid values with 'NEE_uStar_f > 100': 112 ... Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c("<", 0), SubCallFunc.s) : sMRFluxPartition:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 1394 cases! Invalid values with 'Rg < 0': -4.32, -4.03, -3.97, -4.02, -4.01, -3.60, -2.31, -0.87, -4.12, -4.40, -4.41, -3.78, -4.31, -4.26, -4.08, -4.08, -4.28, -4.28, -3.63, -3.82, -3.55, -3.44, -3.44, -2.79, -3.59, -3.46, -3.20, -3.16, -3.54, -3.71, -3.85, -4.24, -3.79, -3.91, -4.34, -4.14, -3.98, -4.01, -4.13, -3.45, -2.47, -3.67, -2.59, -0.92, -0.82, -0.84, -0.93, -1.09, -0.81, -0.39 ... Start flux partitioning for variable NEE_uStar_f with temperature Tair_f. Estimate of the temperature sensitivity E_0 from short term data: 150.92. Regression of reference temperature R_ref for 28 periods. Start daytime flux partitioning for variable NEE_uStar_f with temperature Tair_f. Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c("<", -50), SubCallFunc.s) : sGLFluxPartition:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 16 cases! Invalid values with 'NEE_uStar_f < -50': -61, -57, -115, -70, -96, -117, -72, -54, -69, -64, -68, -88, -63, -67, -55, -55 ... Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c(">", 100), SubCallFunc.s) : sGLFluxPartition:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 1 cases! Invalid values with 'NEE_uStar_f > 100': 112 ... Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c("<", 0), SubCallFunc.s) : sGLFluxPartition:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 147 cases! Invalid values with 'VPD_f < 0': -0.0114, -0.0177, -0.0281, -0.0298, -0.0451, -0.0385, -0.0173, -0.0013, -0.0306, -0.0070, -0.0254, -0.0278, -0.0097, -0.0049, -0.0186, -0.0271, -0.0700, -0.0827, -0.0952, -0.1181, -0.0622, -0.0187, -0.0151, -0.0057, -0.0364, -0.0157, -0.0257, -0.0159, -0.0133, -0.0024, -0.0059, -0.0097, -0.0182, -0.0308, -0.0588, -0.0465, -0.1039, -0.0670, -0.0884, -0.1049, -0.1307, -0.1253, -0.0912, -0.0832, -0.0878, -0.0027, -0.0083, -0.0402, -0.0154, -0.0046 ... Warning in fCheckOutsideRange(Data.F, VarName.V.s[v.i], c("<", 0), SubCallFunc.s) : sGLFluxPartition:::fCheckColPlausibility:::fCheckOutsideRange::: Variable outside (plausible) range in 2082 cases! Invalid values with 'Rg_f < 0': -4.32, -4.03, -3.97, -4.02, -4.01, -3.60, -2.31, -0.87, -4.12, -4.40, -4.41, -3.78, -4.31, -4.26, -4.08, -4.08, -4.28, -4.28, -3.63, -3.82, -3.55, -3.44, -3.44, -2.79, -3.59, -3.46, -3.20, -3.16, -3.54, -3.71, -3.85, -4.24, -3.79, -3.91, -4.34, -4.14, -3.98, -4.01, -4.13, -3.45, -2.47, -3.67, -2.59, -0.92, -0.82, -0.84, -0.93, -1.09, -0.81, -0.39 ... Estimating temperature sensitivity from night time NEE , 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113 increase window size to 24, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113 increase window size to 48, 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113 Smoothing temperature sensitivity estimates Estimating respiration at reference temperature for smoothed temperature sensitivity from night time NEE , 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113 Estimating light response curve parameters from day time NEE , 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113 Warning: `select_()` is deprecated as of dplyr 0.7.0. Please use `select()` instead. This warning is displayed once every 8 hours. Call `lifecycle::last_warnings()` to see where this warning was generated. Saved plot to: ./US-PFn_2019_FP_Reco_uStar.pdf Saved plot to: ./US-PFn_2019_FP_GPP_uStar_f.pdf Saved plot to: ./US-PFn_2019_FP_Reco_DT_uStar.pdf Saved plot to: ./US-PFn_2019_FP_GPP_DT_uStar.pdf Number of NA convertered to '-9999': 222725 Wrote tab separated textfile: output.txt > > # done in shell script > print("converting generated output pdf files to png format for display on results page.") [1] "converting generated output pdf files to png format for display on results page." > > # write logEntry that will be appended by to reddy_jobs_out.txt by reddy_bjobs.sh > # time, task-code(uStar,gapfilling,partitioning), inputSize > logEntry <- if (exists("res_time")) { + paste(res_time["user.self"], retval$mode, retval$inputSize, sep = ",") + } else paste("error", encodeEddyProcTasks(eddyProcConfiguration), "NA", sep = ",") > cat( logEntry, file = "logEntry.txt") > > # if error occured and caught without dumping make sure to exit with error code. > if (exists("retval") && !inherits(retval,"try-error") && length(retval$err)){ + fDump <- function(retval, eddyProcConfiguration) + stop("First caught error: ", retval$err) # does not quit but only dump + fDump(retval, eddyProcConfiguration) + } > if (file.exists(paste0(dumpfileBasename,".rda"))) { + quit(save = "no", status = 1L) + } > > > > > proc.time() user system elapsed 25.385 0.172 25.671