From e943dc117080f265fa67bd0e6cba459e770b463b Mon Sep 17 00:00:00 2001 From: hagento Date: Wed, 28 Feb 2024 09:39:22 +0100 Subject: [PATCH 01/10] added new HDD/CDD calculation --- R/calcBAITpars.R | 76 ++ R/calcHDDCDD.R | 803 +++++++++++++++--- R/convertISIMIPbuildings.R | 80 ++ R/readISIMIPbuildings.R | 171 ++++ .../extdata/regional/regionmappingIEA_ETP.csv | 250 ++++++ .../extdata/sectoral/baitregression-files.csv | 45 + .../extdata/sectoral/correct_efficiencies.csv | 5 + inst/extdata/sectoral/efficiency_limits.csv | 4 + .../filemappings/GFDL-ESM4_historical.csv | 27 + .../filemappings/GFDL-ESM4_picontrol.csv | 59 ++ .../filemappings/GFDL-ESM4_ssp119.csv | 39 + .../filemappings/GFDL-ESM4_ssp126.csv | 39 + .../filemappings/GFDL-ESM4_ssp245.csv | 39 + .../filemappings/GFDL-ESM4_ssp245_test.csv | 7 + .../filemappings/GFDL-ESM4_ssp370.csv | 39 + .../filemappings/GFDL-ESM4_ssp585.csv | 39 + .../filemappings/IPSL-CM6A-LR_historical.csv | 27 + .../filemappings/IPSL-CM6A-LR_picontrol.csv | 59 ++ .../filemappings/IPSL-CM6A-LR_ssp119.csv | 39 + .../filemappings/IPSL-CM6A-LR_ssp126.csv | 39 + .../filemappings/IPSL-CM6A-LR_ssp245.csv | 39 + .../filemappings/IPSL-CM6A-LR_ssp245_test.csv | 7 + .../filemappings/IPSL-CM6A-LR_ssp370.csv | 39 + .../filemappings/IPSL-CM6A-LR_ssp460.csv | 39 + .../filemappings/IPSL-CM6A-LR_ssp534.csv | 27 + .../filemappings/IPSL-CM6A-LR_ssp585.csv | 39 + .../filemappings/MPI-ESM1-2-HR_historical.csv | 27 + .../filemappings/MPI-ESM1-2-HR_picontrol.csv | 59 ++ .../filemappings/MPI-ESM1-2-HR_ssp126.csv | 39 + .../filemappings/MPI-ESM1-2-HR_ssp245.csv | 39 + .../filemappings/MPI-ESM1-2-HR_ssp370.csv | 39 + .../filemappings/MPI-ESM1-2-HR_ssp585.csv | 39 + .../filemappings/MRI-ESM2-0_historical.csv | 27 + .../filemappings/MRI-ESM2-0_picontrol.csv | 59 ++ .../filemappings/MRI-ESM2-0_ssp119.csv | 39 + .../filemappings/MRI-ESM2-0_ssp126.csv | 39 + .../filemappings/MRI-ESM2-0_ssp245.csv | 39 + .../filemappings/MRI-ESM2-0_ssp370.csv | 39 + .../filemappings/MRI-ESM2-0_ssp460.csv | 39 + .../filemappings/MRI-ESM2-0_ssp534.csv | 27 + .../filemappings/MRI-ESM2-0_ssp585.csv | 39 + .../filemappings/UKESM1-0-LL_historical.csv | 27 + .../filemappings/UKESM1-0-LL_picontrol.csv | 59 ++ .../filemappings/UKESM1-0-LL_ssp126.csv | 39 + .../filemappings/UKESM1-0-LL_ssp245.csv | 39 + .../filemappings/UKESM1-0-LL_ssp370.csv | 39 + .../filemappings/UKESM1-0-LL_ssp534.csv | 27 + .../filemappings/UKESM1-0-LL_ssp585.csv | 39 + .../filemappings/start_GFDL-ESM4_picontrol.R | 46 + .../start_IPSL-CM6A-LR_picontrol.R | 46 + .../start_MPI-ESM1-2-HR_picontrol.R | 46 + .../filemappings/start_MRI-ESM2-0_picontrol.R | 46 + .../start_UKESM1-0-LL_picontrol.R | 46 + 53 files changed, 3069 insertions(+), 95 deletions(-) create mode 100644 R/calcBAITpars.R create mode 100644 R/convertISIMIPbuildings.R create mode 100644 R/readISIMIPbuildings.R create mode 100644 inst/extdata/regional/regionmappingIEA_ETP.csv create mode 100644 inst/extdata/sectoral/baitregression-files.csv create mode 100644 inst/extdata/sectoral/correct_efficiencies.csv create mode 100644 inst/extdata/sectoral/efficiency_limits.csv create mode 100644 inst/extdata/sectoral/filemappings/GFDL-ESM4_historical.csv create mode 100644 inst/extdata/sectoral/filemappings/GFDL-ESM4_picontrol.csv create mode 100644 inst/extdata/sectoral/filemappings/GFDL-ESM4_ssp119.csv create mode 100644 inst/extdata/sectoral/filemappings/GFDL-ESM4_ssp126.csv create mode 100644 inst/extdata/sectoral/filemappings/GFDL-ESM4_ssp245.csv create mode 100644 inst/extdata/sectoral/filemappings/GFDL-ESM4_ssp245_test.csv create mode 100644 inst/extdata/sectoral/filemappings/GFDL-ESM4_ssp370.csv create mode 100644 inst/extdata/sectoral/filemappings/GFDL-ESM4_ssp585.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_historical.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_picontrol.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_ssp119.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_ssp126.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_ssp245.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_ssp245_test.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_ssp370.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_ssp460.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_ssp534.csv create mode 100644 inst/extdata/sectoral/filemappings/IPSL-CM6A-LR_ssp585.csv create mode 100644 inst/extdata/sectoral/filemappings/MPI-ESM1-2-HR_historical.csv create mode 100644 inst/extdata/sectoral/filemappings/MPI-ESM1-2-HR_picontrol.csv create mode 100644 inst/extdata/sectoral/filemappings/MPI-ESM1-2-HR_ssp126.csv create mode 100644 inst/extdata/sectoral/filemappings/MPI-ESM1-2-HR_ssp245.csv create mode 100644 inst/extdata/sectoral/filemappings/MPI-ESM1-2-HR_ssp370.csv create mode 100644 inst/extdata/sectoral/filemappings/MPI-ESM1-2-HR_ssp585.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_historical.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_picontrol.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_ssp119.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_ssp126.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_ssp245.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_ssp370.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_ssp460.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_ssp534.csv create mode 100644 inst/extdata/sectoral/filemappings/MRI-ESM2-0_ssp585.csv create mode 100644 inst/extdata/sectoral/filemappings/UKESM1-0-LL_historical.csv create mode 100644 inst/extdata/sectoral/filemappings/UKESM1-0-LL_picontrol.csv create mode 100644 inst/extdata/sectoral/filemappings/UKESM1-0-LL_ssp126.csv create mode 100644 inst/extdata/sectoral/filemappings/UKESM1-0-LL_ssp245.csv create mode 100644 inst/extdata/sectoral/filemappings/UKESM1-0-LL_ssp370.csv create mode 100644 inst/extdata/sectoral/filemappings/UKESM1-0-LL_ssp534.csv create mode 100644 inst/extdata/sectoral/filemappings/UKESM1-0-LL_ssp585.csv create mode 100644 inst/extdata/sectoral/filemappings/start_GFDL-ESM4_picontrol.R create mode 100644 inst/extdata/sectoral/filemappings/start_IPSL-CM6A-LR_picontrol.R create mode 100644 inst/extdata/sectoral/filemappings/start_MPI-ESM1-2-HR_picontrol.R create mode 100644 inst/extdata/sectoral/filemappings/start_MRI-ESM2-0_picontrol.R create mode 100644 inst/extdata/sectoral/filemappings/start_UKESM1-0-LL_picontrol.R diff --git a/R/calcBAITpars.R b/R/calcBAITpars.R new file mode 100644 index 0000000..ffab327 --- /dev/null +++ b/R/calcBAITpars.R @@ -0,0 +1,76 @@ +#' calculate regression parameters for BAIT climate variables +#' +#' @description linear regression on historic data to determine regression +#' parameters for surface downdwelling shortwave radiation (rsds), near-surface +#' wind speed (sfcwind) and near-surface specific humidity (huss) with respect +#' to near-surface air temperature (tas). +#' The regression is done with on a simple linear model, where the historical +#' input data covers the years of 2000-2014. For rsds and sfcwind, a simple linear +#' relationship is assumed where for huss an exponential relation is assumed, +#' buildig upon the non-linear relation between water vapor pressure and temperature. +#' +#' @param model specify GCM responsible for data input +#' +#' @return terra SpatRaster covering one regression parameter per layer per cell +#' +#' @author Hagen Tockhorn +#' +#' @importFrom terra regress rast +#' @importFrom quitte removeColNa +#' @importFrom magclass as.magpie + + +calcBAITpars <- function(model = "GFDL-ESM4") { + + # READ-IN DATA---------------------------------------------------------------- + + files <- toolGetMapping("baitregression-files.csv", type = "sectoral") %>% + filter(.data[["gcm"]] == model) + + vars <- unique(files$variable) + + data <- sapply(vars, function(v) { + tmp <- sapply(files[files$variable == v,]$file, function(f) { + return(readSource("ISIMIPbuildings", subtype = f))}, + USE.NAMES = FALSE) %>% + rast() + }, + USE.NAMES = TRUE) + + print("Reading completed") + + + + # PROCESS DATA---------------------------------------------------------------- + + # convert huss into log scale + data$huss <- log(data$huss) + + # convert tas into [C] + data$tas <- data$tas - 273.15 + + + regPars <- sapply(vars[vars != "tas"], function(v) { + x <- data[["tas"]] + y <- data[[v]] + + r <- regress(x = x, y = y, formula = y ~ x) + + names(r) <- c(paste0("a_", v), paste0("b_", v)) + return(r) + }, + USE.NAMES = FALSE) %>% + rast() + + + + # OUTPUT---------------------------------------------------------------------- + + return(list(x = regPars, + class = "SpatRaster", + unit = "(unit)", + description = "Regression parameters for calcHDDCDD")) + +} + + diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index 03fb702..044b5fe 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -1,101 +1,714 @@ -#' HDD and CDD scenario and historic data +#' calculate HDD and CDD based on outdoor/indoor temperature difference #' -#' smoothes the HDD and CDD scenario data +#' @description heating and cooling degree days based on raw outside temperature +#' or bais-adjusted internal temperature (BAIT), driver for space heating and +#' cooling demand in buildings #' -#' @param tlimit Temperature threshold for computing HDD and CDD +#' @param mappingFile data.frame containing input data file names and directories +#' @param bait specify use of raw temperature or BAIT +#' @param multiscen specify if mappingFile covers more than one scenario #' -#' @author Antoine Levesque +#' @return magpie object of heating and cooling degree days #' -#' @importFrom madrat readSource calcOutput -#' @importFrom magclass setNames setYears as.magpie getNames lowpass getYears mbind -#' @importFrom dplyr %>% -#' @importFrom quitte as.quitte -#' @importFrom utils tail -#' @export - -calcHDDCDD <- function(tlimit = 18) { - - stopifnot(as.character(tlimit) %in% as.character(17:25)) - - data <- readSource("HDDCDD", subtype = tlimit) - - # yearsScenario = all years for which history is either NA or 0 - index <- apply(data[, , paste0("history.HDD.history.", tlimit)], c(2, 3), - function(x) { - return(all(is.na(x) | x == 0)) - }) - - yearsScenario <- c(gsub("y", "", attributes(index)$dimnames$period[index]) %>% as.integer(), 2100) - yearsHistory <- setdiff(getYears(data, as.integer = TRUE), yearsScenario) - - convergenceYear <- 2030 - convergenceYears <- c(max(yearsHistory), yearsScenario[yearsScenario <= convergenceYear]) - afterConvergenceYears <- yearsScenario[!yearsScenario %in% convergenceYears] - lambda <- c(rep(1, length(yearsHistory)), - tail(seq(1, 0, length.out = length(convergenceYears)), -1), - rep(0, length(afterConvergenceYears))) - names(lambda) <- c(yearsHistory, yearsScenario) - lambda <- as.magpie(lambda) - - rcps <- grep("history", getNames(data, TRUE)$rcp, value = TRUE, invert = TRUE) - ssps <- grep("history", getNames(data, TRUE)$scenario, value = TRUE, invert = TRUE) - variable <- grep("history", getNames(data, TRUE)$variable, value = TRUE, invert = TRUE) - - data[is.na(data)] <- 0 - data <- mbind(data, (1 / 3) * (setYears(data[, 2099, ], 2100) + - setYears(data[, 2098, ], 2100) + - setYears(data[, 2097, ], 2100))) # give a point for 2100 - - # distribute the history between all scenarios - data <- do.call("mbind", lapply(rcps, function(rcp) { - do.call("mbind", lapply(ssps, function(ssp) { - tmp <- data[, , rcp][, , ssp] - tmp[, yearsHistory, ] <- setNames(data[, yearsHistory, "history"][, , "history"], - getNames(tmp[, yearsHistory, ])) +#' @author Robin Krekeler, Hagen Tockhorn +#' @examples +#' +#' \dontrun{ +#' calcHDDCDD() +#' } +#' +#' @importFrom madrat getConfig +#' @importFrom raster cellStats +#' @importFrom ncdf4 nc_open +#' @importFrom tidyr %>% +#' @importFrom dplyr mutate +#' @importFrom rlang .data +#' @importFrom pracma integral2 +#' @importFrom stringr str_sub +#' @importFrom terra app nlyr tapp subset rast classify time + + +calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { + + + # FUNCTIONS------------------------------------------------------------------- + + # fill dates for unnamed data + # ---> was supposed to go to convertISIMIP, doesn't work with country check + fillDates <- function(r, filename, bait=FALSE) { + if (grepl(".nc|.nc4", filename)) { + filename <- gsub(".nc|.nc4", "", filename) + } + + if (bait) { + dStart <- as.Date(stringr::str_sub(filename, -17, -10), + format = "%Y%m%d") + n <- nlyr(r) + dates <- seq.Date(dStart, by = "day", length.out = n) + } + else { + yStart <- stringr::str_sub(filename, -9, -6) + dStart <- as.Date(paste0(yStart, "-1-1")) + n <- nlyr(r) + + dates <- seq.Date(dStart, by = "day", length.out = n) + } + + # fill dates + names(r) <- dates + return(r) + } + + + # prepare input for calcBAIT() + calcBaitInput <- function(frsds=NULL, fsfc=NULL, fhuss=NULL, baitInput=NULL, mean=FALSE) { + if(mean) { + # optional: calculate daily means over years to fill missing data + baitInputMean <- sapply( + names(baitInput), function(var) { + mean <- tapp(baitInput[[var]], + unique(substr(names(baitInput[[var]]), 6, 11)), + fun = "mean") + names(mean) <- gsub("\\.", "-", substr(names(mean), 2, 6)) + return(mean)}) + } + else { + input <- list( + "rsds" = readSource("ISIMIPbuildings", subtype = frsds, convert = TRUE), + "sfc" = readSource("ISIMIPbuildings", subtype = fsfc, convert = TRUE), + "huss" = readSource("ISIMIPbuildings", subtype = fhuss, convert = TRUE)) + return(input) + } + } + + + #check if time period is congruent and adapt if necessary + checkDates <- function(baitInput, temp) { + dates_t <- names(temp) + + baitInput <- sapply(names(baitInput), function(var) { + # fill missing data with means from previous years + # NOTE: "temp" and "baitInput" have the same global temporal lower + # boundary, since "temp" is the constraining dataset, only + # "baitInput" needs to be filled up. + + tmp <- baitInput[[var]] + + dates_b <- names(tmp) + + datesFill <- setdiff(dates_t, dates_b) # dates to fill up + daysFill <- unique(substr(datesFill, 6, 11)) + + datesKeep <- intersect(dates_b, dates_t) # dates to keep + keep <- ifelse(length(datesKeep) > 0, TRUE, FALSE) + + if (keep) { + tmp <- subset(tmp, datesKeep) + names(tmp) <- datesKeep + } + + if (length(daysFill) > 0) { + baitInputMean <- calcBaitInput(mean = TRUE, baitInput = baitInput) + + # fill up missing dates with yearly-average value for specific day/cell + baitInputFill <- rast(lapply( + daysFill, function(d){ + print(d) + idx <- which(grepl(d, stringr::str_sub(datesFill, -5, -1))) + r <- rast(replicate(length(idx), + baitInputMean[[var]][[d]])) + names(r) <- datesFill[idx] + return(r) + } + ) + ) + + # concatenate data + if (keep) {tmp <- rast(list(tmp, baitInputFill))} + else {tmp <- baitInputFill} + + # re-order dates + tmp <- rast(tmp[[order(names(tmp))]]) + } + + + if (!identical(names(tmp), names(temp))) { + return(print("Warning: Dates of Temperature and BAIT Input Data are not aligned.")) + } return(tmp) - })) - })) - - - # Smooth the trajectories - dataSmoothed <- lapply(ssps, function(scenario) { - lapply(rcps, function(rcp) { - thirdDim <- paste(scenario, variable, rcp, tlimit, sep = ".") - out <- lowpass(data[, , thirdDim], i = 100) - return(out) - }) - }) - dataSmoothed <- do.call("mbind", unlist(dataSmoothed, recursive = FALSE)) - - - # Smooth of the transition between historical and scenario values - data <- data * lambda + dataSmoothed * (1 - lambda) - - # replace negative values with 0 (large neg values only for small countries) - data[which(data < 0)] <- 0 - - # Set regions where there is either no value for history or for scenarios to 0 - data["GRL", , ] <- 0 - data[c("ASM", "FSM", "GUM", "MDV", "MHL", "TUV", "TKL", "SJM"), , ] <- 0 - - # Add the no Climate Change scenario - noC <- data[, , "rcp2p6"] - - # Fill with the mean of the five last periods - noC[, yearsScenario, ] <- - dimSums(noC[, c((max(yearsHistory) - 4):max(yearsHistory)), ], dim = 2) / 5 - getNames(noC) <- gsub("rcp2p6", "rcpNoC", getNames(noC)) - data <- mbind(data, noC) - - pop <- calcOutput("Population", aggregate = FALSE, FiveYearSteps = FALSE) - getNames(pop) <- tolower(sub("pop_(....).?$", "\\1", getNames(pop))) - pop <- pop[, , ssps] - y <- intersect(getYears(data), getYears(pop)) - data <- data[, y, ] - pop <- pop[, y, ] - - return(list(x = data, - weight = pop, - unit = "dK/yr", - description = "Heating degree days or cooling degree days")) + }, + USE.NAMES = TRUE + ) + return(baitInput) + } + + + #--- CALCULATE BAIT (building-adjusted internal temperature) + + # NOTE: The parameters are taken from Staffell et al. 2023 + + # counterfactuals for solar, wind, humidity, temperature + cfac <- function(t, type, params=NULL) { + if (is.null(params)) { + params <- switch(type, + s = c(100, 7), + w = c(4.5, -0.025), + h = c(1.1, 0.06), + t = c(16))} + + print(names(params)) + if (type == "s") {return(params[[1]] + params[[2]]*t)} + else if (type == "w") {return(params[[1]] + params[[2]]*t)} + else if (type == "h") {return(exp(params[[1]] + params[[2]]*t))} + else if (type == "t") {return(params[[1]])} + + else {print("No valid parameter type specified.")} + } + + + # smooth data + smooth <- function(r, weight) { + # smooth bait over preceding two days with smoothing parameter sigma + print("smooth") + + # one day indented + r1D <- r[[c(nlyr(r), 1:(nlyr(r) - 1))]] + r1D[[1]] <- 0 + + # two days indented + r2D <- r[[c(nlyr(r)-1, nlyr(r), 1:(nlyr(r) - 2))]] + r2D[[1:2]] <- 0 + + # smooth + rSmooth <- (r + weight[[4]]*r1D + weight[[4]]**2 * r2D) / (1 + weight[[4]] + weight[[4]]**2) + + return(rSmooth) + } + + blend <- function(baitDF, temp, weight) { + # weighted blend of BAIT and raw temperature + print("blend") + bBar <- (temp - 0.5*(weight[[6]] + weight[[5]])) * 10 / (weight[[6]] - weight[[5]]) + b <- weight[[7]] / (1 + exp(-bBar)) + + baitDF <- baitDF * (1 - b) + (temp * b) + return(baitDF) + } + + + calcBAIT <- function(baitInput, temp, weight=NULL, params=NULL) { + # weight=(x,y,z,sigma,bLower,bUpper,bMax) + if (is.null(weight)) { + print("Please give appropriate weights for the calculation of BAIT.") + weight <- c(1,1,1,1,1,1,1)} + + dates <- names(temp) + + solar <- baitInput$rsds + wind <- baitInput$sfc + hum <- baitInput$huss + + print("calc s") + s <- solar - cfac(temp, type="s", params = c(params[["a_rsds"]], params[["b_rsds"]])) + print("calc w") + w <- wind - cfac(temp, type="w", params = c(params[["a_sfcwind"]], params[["b_sfcwind"]])) + print("print h") + h <- hum - cfac(temp, type="h", params = c(params[["a_huss"]], params[["b_huss"]])) + print("calc t") + t <- temp - cfac(temp, type="t", params = NULL) + + # calc raw bait + print("calc bait") + bait <- temp + weight[[1]]*s + weight[[2]]*w + weight[[3]]*h*t + + bait <- smooth(bait, weight) + bait <- blend(bait, temp, weight) + + return(bait) + } + + + #--- CALCULATE HDD/CDD + + # Calculate heating factor matrix for normally distributed ambient + # and limit temperatures + + # t1 : ambient temperature variable + # t2 : limit temperature variable + + heatingFactor <- function(t2, t1, tamb, tamb_std, tlim, tlim_std) { + h <- dnorm(t2, mean=tlim, sd=tlim_std) * dnorm(t1, mean=tamb, sd=tamb_std) * (t2 - t1) + return(h) + } + + coolingFactor <- function(t2, t1, tamb, tamb_std, tlim, tlim_std) { + h <- dnorm(t2, mean=tlim, sd=tlim_std) * dnorm(t1, mean=tamb, sd=tamb_std) * (t1 - t2) + return(h) + } + + # check if ambient/limit temperature interval is reasonable + # e.g. t_lim = 17C and t_amb = -50C wouldn't give reasonable CDD + checkTDif <- function(tamb, tlim, typeDD, tamb_std, tlim_std) { + check <- TRUE + stdDif <- tamb_std + tlim_std + if (typeDD == "HDD") { + if (tamb - tlim > stdDif) { + check <- FALSE + } + } + else if (typeDD == "CDD") { + if (tlim - tamb > 2*stdDif) { + check <- FALSE + } + } + return(check) + } + + # calculate HDD/CDD values per day for given ambient/limit temp combination + calcHDDCDDFactors <- function(tlow, tup, tlim, tamb_std=5, tlim_std=5) { + t <- seq(tlow, tup, .1) + + hddcddFactors <- do.call( + "rbind", lapply( + c("HDD", "CDD"), function(typeDD) { + do.call( + "rbind", lapply( + t, function(tamb) { + do.call( + "rbind", lapply( + tlim[[typeDD]], function(.tlim) { + if (!checkTDif(tamb, .tlim, typeDD, tamb_std, tlim_std)) { + tmp <- data.frame("T_amb" = tamb, + "T_amb_K" = round(tamb + 273.15, 1), + "T_lim" = .tlim, + "factor" = 0, + "factor_err" = 0, + "typeDD" = typeDD) + } + else { + + # integration boundaries + x1 <- .tlim - 4*tlim_std + x2 <- .tlim + 4*tlim_std + y1 <- min(.tlim - 3*tlim_std, tamb - 3*tlim_std) + y2 <- max(.tlim + 3*tlim_std, tamb + 3*tlim_std) + + if (typeDD == "HDD") { + f <- pracma::integral2(heatingFactor, + xmin = x1, + xmax = x2, + ymin = y1, + ymax = function(x){x}, + tamb = tamb, + tamb_std = tamb_std, + tlim = .tlim, + tlim_std = tlim_std, + reltol = 1e-1 + ) + } + else { + f <- pracma::integral2(coolingFactor, + xmin = x1, + xmax = x2, + ymin = function(x){x}, + ymax = y2, + tamb = tamb, + tamb_std = tamb_std, + tlim = .tlim, + tlim_std = tlim_std, + reltol = 1e-1) + } + tmp <- data.frame("T_amb" = tamb, + "T_amb_K" = round(tamb + 273.15, 1), + "T_lim" = .tlim, + "factor" = f$Q, + "factor_err" = f$error, + "typeDD" = typeDD) + } + } + ) + ) + } + ) + ) + } + ) + ) + return(hddcddFactors) + } + + + + # fill HDD/CDD from factors for given ambient/limit temperature combination + calcCellHDDCDD <- function(temp, .typeDD, .tlim, factors) { + # extract years + dates <- names(temp) + + # add tolerance of 0.04K to avoid machine precision errors + factors <- factors %>% + filter(.data[["typeDD"]] == .typeDD, .data[["T_lim"]] == .tlim) %>% + dplyr::reframe(from = .data[["T_amb_K"]] - 0.049, + to = .data[["T_amb_K"]] + 0.049, + becomes = .data[["factor"]]) %>% + data.matrix() + + # swap ambient temperature values with corresponding DD values + hddcdd <- classify(temp, factors) + + terra::time(hddcdd) <- as.Date(dates) + + # aggregate to yearly HDD/CDD [K.d/a] + hddcdd <- tapp(hddcdd, "years", fun = sum, na.rm = TRUE) + + names(hddcdd) <- gsub("_", "", names(hddcdd)) + return(hddcdd) + } + + + # aggregate cellular HDD/CDD to country-wide average (population-weighted) + aggCells <- function(r, weight, mask) { + years_r <- names(r) + years_w <- names(weight) + + print(paste("years_r:", years_r)) + print(paste("years_w:", years_w)) + + if (!all(years_r %in% years_w)) { + stop("Time periods of raster file and aggregation weights do not match.") + } + + # loop: years in raster file r + hddcddAgg <- do.call( + "rbind", lapply( + years_r, function(y) { + browser() + tmp <- subset(r, y) * subset(weight, y) * mask + tmpTot <- subset(weight, y) * mask + + tmpSum <- terra::global(tmp, "sum", na.rm = TRUE)$sum + tmpTotSum <- terra::global(tmpTot, "sum", na.rm = TRUE)$sum + tmp <- tmpSum / tmpTotSum + tmp <- data.frame("region" = names(mask), + "period" = y, + "value" = round(tmp, 3)) + rownames(tmp) <- c() + return(tmp) + } + ) + ) + return(hddcddAgg) + } + + + # calculate all desired output from given tas file + calcStackHDDCDD <- function(file, tlim, countries, pop, factors, bait, + frsds = NULL, + fsfc = NULL, + fhuss = NULL, + wBAIT = NULL, + params = NULL) { + + # read cellular temperature + temp <- readSource("ISIMIPbuildings", subtype = file, convert = TRUE) + + dates <- names(temp) + + # optional: transform raw temperature into BAIT + if (bait) { + # note: easier to do in [C] + temp <- temp - 273.15 # [C] + + # read and prepare bait input data + baitInput <- calcBaitInput(frsds, fsfc, fhuss) %>% + checkDates(temp) + + # calculate bait + temp <- calcBAIT(baitInput, temp, weight = wBAIT, params = params) + + # convert back to [K] + temp <- temp + 273.15 # [K] + } + + temp <- terra::round(temp, digits = 1) + names(temp) <- dates + + print("Calculating HDD/CDDs per cell.") + + fSplit <- str_split(file, "_") %>% unlist() + + # loop: typeDD + hddcdd <- do.call( + "rbind", lapply( + c("HDD", "CDD"), function(typeDD) { + # loop: threshold temperatures + do.call( + "rbind", lapply( + tlim[[typeDD]], function(t) { + hddcdd_agg <- calcCellHDDCDD(temp, typeDD, t, factors) + + # save intermediate results + decades <- c(as.numeric(gsub("y", "", names(hddcdd_agg))) %% 10 == 0) + if (any(decades)) { + hddcdd_save <- hddcdd_agg[[decades]] + + y <- names(hddcdd_agg)[as.numeric(gsub("y", "", names(hddcdd_agg))) %% 10 == 0] + + y <- gsub("y", "", y) %>% + paste(collapse = "-") + + rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) + + if (bait) {rname <- paste0(rname, "_bait.nc")} + else {rname <- paste0(rname, ".nc")} + + + terra::writeRaster(hddcdd_save, + paste0("/p/tmp/hagento/output/rasterdata/", rname), + overwrite = TRUE) + } + + hddcdd_agg <- hddcdd_agg %>% + aggCells(pop, countries) %>% + mutate("variable" = typeDD, + "tlim" = t) # [C] + + return(hddcdd_agg) + } + ) + ) + } + ) + ) + } + + + # initialize full calculation + makeCalculations <- function(f, m, n, t_lim, countries, pop, hddcddFactor, + bait, wBAIT = NULL, params = NULL, suffix = NULL) { + + if (!is.null(suffix)) { + ftas <- gsub(".nc", + paste0("_", suffix, ".nc"), + f[f$variable == "tas" & f$gcm == m,][[n, "file"]]) + + if (bait){ + frsds <- gsub(".nc", + paste0("_", suffix, ".nc"), + f[f$variable == "rsds" & f$gcm == m,][[n, "file"]]) + + fsfc <- gsub(".nc", + paste0("_", suffix, ".nc"), + f[f$variable == "sfcwind" & f$gcm == m,][[n, "file"]]) + + fhuss <- gsub(".nc", + paste0("_", suffix, ".nc"), + f[f$variable == "huss" & f$gcm == m,][[n, "file"]]) + } + } + + else { + ftas <- f[f$variable == "tas" & f$gcm == m,][[n, "file"]] + frsds <- f[f$variable == "rsds" & f$gcm == m,][[n, "file"]] + fsfc <- f[f$variable == "sfcwind" & f$gcm == m,][[n, "file"]] + fhuss <- f[f$variable == "huss" & f$gcm == m,][[n, "file"]] + } + + print(paste("Processing temperature file:", ftas)) + + if(bait) { + hddcddCell <- calcStackHDDCDD(ftas, + t_lim, + countries, + pop, + hddcddFactor, + bait, + frsds = frsds, + fsfc = fsfc, + fhuss = fhuss, + wBAIT = wBAIT, + params = params)} + + else { + hddcddCell <- calcStackHDDCDD(ftas, + t_lim, + countries, + pop, + hddcddFactor, + bait)} + return(hddcddCell) + } + + + plotRaster <- function(r) { + library("ggplot2") + r_map <- stack(as.data.frame(raster::getValues(r))) + coords <- raster::xyFromCell(r, seq_len(raster::ncell(r))) + names(r_map) <- c('value', 'variable') + r_map <- cbind(coords, r_map) + ggplot(r_map) + + geom_tile(aes(x, y, fill = value)) + + facet_wrap(~ variable) + + scale_fill_gradientn(colours = rev(terrain.colors(225))) + + coord_equal() + } + + + + # PARAMETERS------------------------------------------------------------------ + + terra::setGDALconfig(c("BIGTIFF = YES")) + + + # threshold temperature for heating and cooling [C] + # NOTE: Staffel gives global average of T_heat = 14, T_cool = 20 + # t_lim <- list("HDD" = seq(12, 18), "CDD" = seq(20, 26)) + t_lim <- list("HDD" = seq(14), "CDD" = seq(22)) + + # standard deviations for temperature distributions + tlim_std <- 5 # threshold + tamb_std <- 5 # ambient + + # historical years -> NOTE: not sure if further needed + firstHistYear <- 1950 + lastHistYear <- 2009 + + # range of pre-calculated HDD/CDD-values, e.g. [223, 232] K, converted to [C] + tlow <- 223 - 273.15 + tup <- 323 - 273.15 + + + + #--- BAIT parameters + + # The weights (x,y,z) for calcBAIT and the smoothing coefficient are assumed to + # be region-independent and equal to the mean of the values given in Staffell + # et al. 2023 + x <- 0.012 + y <- -0.20 + z <- 0.05 + + # smoothing coefficient + sig <- 0.50 + + # The blending parameters for the blending of BAIT and raw temperature are like-wise + # taken from the paper. + bLower <- 15 + bUpper <- 23 + bMax <- 0.5 + + # concatenate to vector + wBAIT <- c(x, y, z, sig, bLower, bUpper, bMax) + + # BAIT parameter names + parNames <- c("a_rsds", "b_rsds", "a_sfcwind", "b_sfcwind", "a_huss", "b_huss") + + + + # READ-IN DATA---------------------------------------------------------------- + + # list of files that are processed + files <- toolGetMapping(mappingFile, type = "sectoral", where = "mappingfolder") %>% + filter(variable != "") + + # cells -> country mask + fCM <- file.path(files[files$variable == "CountryMask", "file"]) + countries <- readSource("ISIMIPbuildings", subtype = fCM, convert = FALSE) + + + + # PROCESS DATA---------------------------------------------------------------- + + # calculate HDD/CDD-factors + hddcddFactor <- calcHDDCDDFactors(tlow=-100.15, tup=74.85, t_lim, tamb_std, tlim_std) + + ssp <- unique(files$ssp[files$variable == "tas"]) + rcp <- unique(files$rcp[files$variable == "tas"]) + model <- unique(files$gcm[files$variable == "tas"]) + + # read population data + fpop <- files %>% filter(variable == "pop") + pop <- readSource("ISIMIPbuildings", subtype = fpop$file, + convert = FALSE) + + if (bait) { + baitPars <- calcOutput("BAITpars", aggregate = FALSE, model = model) + names(baitPars) <- parNames + } + + hddcdd <- do.call( # file iteration + "rbind", + lapply( + seq(nrow(filter(files, files$variable == "tas"))), + function(n) { + split <- files[files$variable == "tas" & files$gcm == model,][[n, "split"]] + + # split large raster files to save memory / speed up processing + if (as.logical(split)) { + hddcddCell <- do.call( + "rbind", + lapply(list("A", "B"), function(suffix) { + tmp <- makeCalculations(f = files, + m = model, + n = n, + t_lim = t_lim, + countries = countries, + pop = pop, + hddcddFactor = hddcddFactor, + bait = bait, + wBAIT = wBAIT, + params = baitPars, + suffix = suffix) + return(tmp) + } + ) + ) + } + + # smaller raster files + else { + hddcddCell <- makeCalculations(f = files, + m = model, + n = n, + t_lim = t_lim, + countries = countries, + pop = pop, + hddcddFactor = hddcddFactor, + bait = bait, + wBAIT = wBAIT, + params = baitPars) + } + + hddcddCell <- hddcddCell %>% + mutate("model" = model, + "ssp" = ssp, + "rcp" = rcp) + + return(hddcddCell) + } + ) + ) + + rownames(hddcdd) <- c() + + + + # OUTPUT---------------------------------------------------------------------- + + # might be necessary for magpie output + data <- hddcdd %>% + mutate(period = gsub("y", "", .data[["period"]])) %>% + unite("variable", .data[["variable"]], .data[["tlim"]], sep = "_") %>% + unite("scenario", .data[["ssp"]], .data[["rcp"]], sep = "_") + + # prepare output + data <- data %>% + as.quitte() %>% + as.magpie() %>% + toolCountryFill() + + + return(list(x = data)) } + + + diff --git a/R/convertISIMIPbuildings.R b/R/convertISIMIPbuildings.R new file mode 100644 index 0000000..78f3c9e --- /dev/null +++ b/R/convertISIMIPbuildings.R @@ -0,0 +1,80 @@ +#' convert ISIMIP data for mredgebuildings +#' +#' @param x MAgPIE object on cellular level +#' @param subtype filename +#' +#' @importFrom stringr str_detect +#' +#' @author Hagen Tockhorn +#' +#' @return rasterBrick object + +convertISIMIPbuildings <- function(x, subtype) { + + # FUNCTIONS------------------------------------------------------------------- + + # fill dates for unnamed data + fillDates <- function(r, filename, pop = FALSE) { + + if (grepl("_A.nc|_A.nc4", filename)) { + yStart <- stringr::str_sub(filename, -14, -11) + } + else if (grepl("_B.nc|_B.nc4", filename)) { + yEnd <- stringr::str_sub(filename, -9, -6) + yStart <- as.character(as.numeric(yEnd) - 4) + } + else { + yStart <- stringr::str_sub(filename, -12, -9) + } + + n <- terra::nlyr(r) + + if (!pop) { + dStart <- as.Date(paste0(yStart, "-1-1")) + dates <- seq.Date(dStart, by = "day", length.out = n) + } else { + print("pop") + dates <- seq(yStart, by = 1, length.out = n) %>% as.character() + } + + # fill dates + names(r) <- dates + return(r) + } + + + # PARAMETERS------------------------------------------------------------------ + + # variable units + unitMapping <- list( + "tas" = "K", + "rsds" = "Wm-2", + "sfcWind" = "ms-1", + "huss" = "gkg-1" + ) + + edgeVars <- c("tas", "rsds", "sfcwind", "huss", "population") + + + # PROCESS DATA---------------------------------------------------------------- + + if (grepl(paste(edgeVars, collapse = "|"), subtype)) { + var <- edgeVars[str_detect(subtype, edgeVars)] + + if (var == "population") { + x <- fillDates(x, subtype, pop = TRUE) + } else { + x <- fillDates(x, subtype) + } + + if (var == "huss") { + # convert kg/kg -> g/kg + x <- x * 1e3 + } + } + + return(list(x = x, + class = "SpatRaster", + unit = unitMapping[var], + cache = FALSE)) +} diff --git a/R/readISIMIPbuildings.R b/R/readISIMIPbuildings.R new file mode 100644 index 0000000..b7e320e --- /dev/null +++ b/R/readISIMIPbuildings.R @@ -0,0 +1,171 @@ +#' Read relevant ISIMIP data for mredgebuildings +#' +#' +#' @param subtype filename +#' +#' @author Hagen Tockhorn +#' +#' @importFrom stringr str_split +#' @importFrom raster brick dropLayer res extent aggregate +#' +#' @note +#' Argument subtype consist of full filenames of the respective raster files. In +#' order to improve runtime and general problems that come with processing large +#' raster files, filenames marked with "_A" or "_B" before the filetype declaration, +#' will be split in the respective first and second half of the dataset. +#' +#' @note +#' folder structure in inputdata/sources/ISIMIPbuildings is expected to be: +#' country masks : var/ +#' population : var/scenario +#' other : var/scenario/model +#' +#' @note currently, this function only reads data from ISIMIP3b + + +readISIMIPbuildings <- function(subtype) { + + # PARAMETERS------------------------------------------------------------------ + + baitVars <- c("tas", "sfcwind", "rsds", "huss") + + firstHistYear <- 1960 + + + # FUNCTIONS------------------------------------------------------------------- + + splitSubtype <- function(subtype) { + vars <- list() + + if (grepl("countrymask", subtype)) { + vars[["variable"]] = "countrymask" + } + + else if (grepl("population", subtype)) { + subSplit <- str_split(subtype, "_") %>% unlist() + + vars[["variable"]] <- subSplit[[1]] + vars[["scenario"]] <- subSplit[[2]] + } + + else if (any(sapply(baitVars, grepl, x = subtype))) { + subSplit <- str_split(subtype, "_") %>% unlist() + + vars[["variable"]] <- subSplit[[5]] + vars[["scenario"]] <- subSplit[[4]] + vars[["model"]] <- subSplit[[1]] + + if (grepl("A", tail(subSplit, 1))) { + vars[["yStart"]] <- subSplit[[length(subSplit) - 2]] + + yEnd <- as.numeric(vars[["yStart"]]) + 4 # 5 year period + vars[["yEnd"]] <- as.character(yEnd) + + vars[["subtype"]] <- sub("_A.nc", ".nc", subtype) + } + + else if (grepl("B", tail(subSplit, 1))) { + vars[["yEnd"]] <- subSplit[[length(subSplit) - 1]] + + yStart <- as.numeric(vars[["yEnd"]]) - 4 # 5 year period + vars[["yStart"]] <- as.character(yStart) + + vars[["yStartFile"]] <- subSplit[[length(subSplit) - 2]] + + vars[["subtype"]] <- sub("_B.nc", ".nc", subtype) + } + } + + else {stop("Invalid subtype given.")} + + return(vars) + } + + + # determine period range of subset + getIdxRange <- function(yStart, yEnd, vars) { + dStart <- as.Date(paste0(yStart, "-01-01")) + dEnd <- as.Date(paste0(yEnd, "-12-31")) + dRange <- seq.Date(from = dStart, to = dEnd, by = "day") + + if (is.null(vars[["yStartFile"]])) { + idx <- seq(1:length(dRange)) + } + else { + dStartFile <- as.Date(paste0(vars[["yStartFile"]], "-01-01")) + dRangeFile <- seq.Date(from = dStartFile, to = dEnd, by = "day") + idx <- seq(length(dRangeFile) - length(dRange) + 1, length(dRangeFile)) + } + + return(idx) + } + + + # PROCESS DATA---------------------------------------------------------------- + + vars <- splitSubtype(subtype) + + if (vars[["variable"]] == "countrymask"){ + fpath <- file.path("countrymasks", subtype) + varNames <- names(ncdf4::nc_open(fpath)[["var"]]) + countries <- list() + for (var in varNames) { + countries[[var]] <- suppressWarnings(terra::rast(fpath, subds = var)) + } + r <- terra::rast(countries) + names(r) <- gsub("m_", "", varNames) + + x <- list(x = r, class = "SpatRaster") + } + + + else if (vars[["variable"]] == "population") { + fpath <- file.path(vars[["variable"]], vars[["scenario"]], subtype) + + if (vars[["scenario"]] == "picontrol") { + r <- suppressWarnings(terra::rast(fpath)) + } + else { + r <- suppressWarnings(terra::rast(fpath, subds = "total-population")) + } + + subtype <- gsub(".nc", "", subtype) + + # rename years + years <- tail(strsplit(subtype, "_")[[1]], 2) + names(r) <- paste0("y", years[1]:years[2]) + + # filter relevant years + r <- terra::subset(r, as.numeric(substr(names(r), 2, 5)) > firstHistYear) + + # aggregate to common resolution of 0.5 deg + if (any(raster::res(r) != 0.5)) { + r <- terra::aggregate(r, fun = "sum", + fact = round(0.5 / terra::res(r), 3)) + terra::res(r) <- 0.5 + terra::ext(r) <- round(terra::ext(r)) + } + + x <- list(x = r, class = "SpatRaster", cache = FALSE) + } + + + else if (any(vars[["variable"]] %in% baitVars)) { + + if (!is.null(vars[["yStart"]])) { + fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], vars[["subtype"]]) + idx <- getIdxRange(vars[["yStart"]], vars[["yEnd"]], vars) + r <- suppressWarnings(terra::rast(fpath, lyrs = idx)) + } + else { + fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], subtype) + r <- suppressWarnings(terra::rast(fpath)) + } + + x <- list(x = r, class = "SpatRaster", cache = FALSE) + } + + else {stop("Subtype was incorrectly split or invalid subtype given.")} + + return(x) +} diff --git a/inst/extdata/regional/regionmappingIEA_ETP.csv b/inst/extdata/regional/regionmappingIEA_ETP.csv new file mode 100644 index 0000000..7331c04 --- /dev/null +++ b/inst/extdata/regional/regionmappingIEA_ETP.csv @@ -0,0 +1,250 @@ +X;CountryCode;EEAReg;OECD +Afghanistan;AFG;rest;Non-OECD +Aland Islands;ALA;EUR;Non-OECD +Albania;ALB;rest;Non-OECD +Algeria;DZA;rest;Non-OECD +American Samoa;ASM;rest;Non-OECD +Andorra;AND;rest;Non-OECD +Angola;AGO;rest;Non-OECD +Anguilla;AIA;rest;Non-OECD +Antarctica;ATA;rest;Non-OECD +Antigua and Barbuda;ATG;rest;Non-OECD +Argentina;ARG;rest;Non-OECD +Armenia;ARM;rest;Non-OECD +Aruba;ABW;rest;Non-OECD +Australia;AUS;rest;OECD +Austria;AUT;EUR;OECD +Azerbaijan;AZE;rest;Non-OECD +Bahamas;BHS;rest;Non-OECD +Bahrain;BHR;rest;Non-OECD +Bangladesh;BGD;rest;Non-OECD +Barbados;BRB;rest;Non-OECD +Belarus;BLR;rest;Non-OECD +Belgium;BEL;EUR;OECD +Belize;BLZ;rest;Non-OECD +Benin;BEN;rest;Non-OECD +Bermuda;BMU;rest;Non-OECD +Bhutan;BTN;rest;Non-OECD +Bolivia, Plurinational State of;BOL;rest;Non-OECD +Bonaire, Sint Eustatius and Saba;BES;rest;Non-OECD +Bosnia and Herzegovina;BIH;rest;Non-OECD +Botswana;BWA;rest;Non-OECD +Bouvet Island;BVT;rest;Non-OECD +Brazil;BRA;BRA;Non-OECD +British Indian Ocean Territory;IOT;rest;Non-OECD +Brunei Darussalam;BRN;ASEAN;Non-OECD +Bulgaria;BGR;EUR;Non-OECD +Burkina Faso;BFA;rest;Non-OECD +Burundi;BDI;rest;Non-OECD +Cambodia;KHM;ASEAN;Non-OECD +Cameroon;CMR;rest;Non-OECD +Canada;CAN;rest;OECD +Cape Verde;CPV;rest;Non-OECD +Cayman Islands;CYM;rest;Non-OECD +Central African Republic;CAF;rest;Non-OECD +Chad;TCD;rest;Non-OECD +Chile;CHL;rest;OECD +China;CHN;CHN;Non-OECD +Christmas Island;CXR;rest;Non-OECD +Cocos (Keeling) Islands;CCK;rest;Non-OECD +Colombia;COL;rest;OECD +Comoros;COM;rest;Non-OECD +Congo;COG;rest;Non-OECD +Congo, the Democratic Republic of the;COD;rest;Non-OECD +Cook Islands;COK;rest;Non-OECD +Costa Rica;CRI;rest;Non-OECD +Cote d Ivoire;CIV;rest;Non-OECD +Croatia;HRV;EUR;Non-OECD +Cuba;CUB;rest;Non-OECD +Curacao;CUW;rest;Non-OECD +Cyprus;CYP;EUR;Non-OECD +Czech Republic;CZE;EUR;OECD +Denmark;DNK;EUR;OECD +Djibouti;DJI;rest;Non-OECD +Dominica;DMA;rest;Non-OECD +Dominican Republic;DOM;rest;Non-OECD +Ecuador;ECU;rest;Non-OECD +Egypt;EGY;rest;Non-OECD +El Salvador;SLV;rest;Non-OECD +Equatorial Guinea;GNQ;rest;Non-OECD +Eritrea;ERI;rest;Non-OECD +Estonia;EST;EUR;OECD +Ethiopia;ETH;rest;Non-OECD +Falkland Islands (Malvinas);FLK;rest;Non-OECD 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States;USA;USA;OECD +United States Minor Outlying Islands;UMI;rest;Non-OECD +Uruguay;URY;rest;Non-OECD +Uzbekistan;UZB;rest;Non-OECD +Vanuatu;VUT;rest;Non-OECD +Venezuela, Bolivarian Republic of;VEN;rest;Non-OECD +Viet Nam;VNM;ASEAN;Non-OECD +Virgin Islands, British;VGB;rest;Non-OECD +Virgin Islands, U.S.;VIR;rest;Non-OECD +Wallis and Futuna;WLF;rest;Non-OECD +Western Sahara;ESH;rest;Non-OECD +Yemen;YEM;rest;Non-OECD +Zambia;ZMB;rest;Non-OECD +Zimbabwe;ZWE;rest;Non-OECD diff --git a/inst/extdata/sectoral/baitregression-files.csv b/inst/extdata/sectoral/baitregression-files.csv new file mode 100644 index 0000000..23f1693 --- /dev/null +++ b/inst/extdata/sectoral/baitregression-files.csv @@ -0,0 +1,45 @@ +gcm,rcp,ssp,variable,start,end,folder,file +GFDL-ESM4,historical,historical,tas,2001,2010,/p/projects/isimip/isimip/ISIMIP3b/InputData/climate/atmosphere/bias-adjusted/global/daily/historical/GFDL-ESM4,gfdl-esm4_r1i1p1f1_w5e5_historical_tas_global_daily_2001_2010.nc 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+parameter,space_cooling.elec,water_heating.elec,space_heating.elec +Asym,3.10,1.5,1.5 +R0,0.50,, +lrc,-10.12663,, +phi3,NA,, \ No newline at end of file diff --git a/inst/extdata/sectoral/efficiency_limits.csv b/inst/extdata/sectoral/efficiency_limits.csv new file mode 100644 index 0000000..9b6eb07 --- /dev/null +++ b/inst/extdata/sectoral/efficiency_limits.csv @@ -0,0 +1,4 @@ +variable,value +cooking.biomod,0.35 +space_heating.biomod,0.35 +space_heating.biotrad,0.35 \ No newline at end of file diff --git a/inst/extdata/sectoral/filemappings/GFDL-ESM4_historical.csv b/inst/extdata/sectoral/filemappings/GFDL-ESM4_historical.csv new file mode 100644 index 0000000..eb2c343 --- /dev/null +++ b/inst/extdata/sectoral/filemappings/GFDL-ESM4_historical.csv @@ -0,0 +1,27 @@ +"";"gcm";"rcp";"ssp";"variable";"start";"end";"folder";"file";"split" +"1";"";"";"";"CountryMask";NA;NA;"/p/projects/isimip/isimip/ISIMIP3b/InputData/geo_conditions/countrymasks";"countrymasks.nc";NA 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b/inst/extdata/sectoral/filemappings/start_GFDL-ESM4_picontrol.R new file mode 100644 index 0000000..022178c --- /dev/null +++ b/inst/extdata/sectoral/filemappings/start_GFDL-ESM4_picontrol.R @@ -0,0 +1,46 @@ +library(mrremind) +library(mrcommons) + + +# load libraries +devtools::load_all("dev/mredgebuildings") +#library(mredgebuildings) + +setConfig(outputfolder = "/p/tmp/hagento/output", + mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", + ignorecache = c("ISIMIPbuildings")) + +#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") + +# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. +# Each list element is a named vector with: +# - the first element "regionmapping": region mapping used for the aggregation of the input data +# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping + +# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" + +# Current input data revision (.) #### +revision <- "0.1" # should be a number with two decimal places for production +cachetype <- "def" +dev <- "" +puc <- identical(dev, '') +renv <- FALSE + +sessionInfo() + + +# HISTORICAL + +f <- "GFDL-ESM4_picontrol.csv" + +# EDGE mapping +# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, +# dev = dev, cachetype = cachetype, puc = puc, renv = renv) + +dev <- paste0("_", gsub(".csv", "", f)) + +# ISO +retrieveData(model = "hddcdd", file = f, rev = revision, + dev = dev, cachetype = cachetype, puc = puc, renv = renv) + + diff --git a/inst/extdata/sectoral/filemappings/start_IPSL-CM6A-LR_picontrol.R b/inst/extdata/sectoral/filemappings/start_IPSL-CM6A-LR_picontrol.R new file mode 100644 index 0000000..b2e57f5 --- /dev/null +++ b/inst/extdata/sectoral/filemappings/start_IPSL-CM6A-LR_picontrol.R @@ -0,0 +1,46 @@ +library(mrremind) +library(mrcommons) + + +# load libraries +devtools::load_all("dev/mredgebuildings") +#library(mredgebuildings) + +setConfig(outputfolder = "/p/tmp/hagento/output", + mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", + ignorecache = c("ISIMIPbuildings")) + +#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") + +# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. +# Each list element is a named vector with: +# - the first element "regionmapping": region mapping used for the aggregation of the input data +# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping + +# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" + +# Current input data revision (.) #### +revision <- "0.1" # should be a number with two decimal places for production +cachetype <- "def" +dev <- "" +puc <- identical(dev, '') +renv <- FALSE + +sessionInfo() + + +# HISTORICAL + +f <- "IPSL-CM6A-LR_picontrol.csv" + +# EDGE mapping +# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, +# dev = dev, cachetype = cachetype, puc = puc, renv = renv) + +dev <- paste0("_", gsub(".csv", "", f)) + +# ISO +retrieveData(model = "hddcdd", file = f, rev = revision, + dev = dev, cachetype = cachetype, puc = puc, renv = renv) + + diff --git a/inst/extdata/sectoral/filemappings/start_MPI-ESM1-2-HR_picontrol.R b/inst/extdata/sectoral/filemappings/start_MPI-ESM1-2-HR_picontrol.R new file mode 100644 index 0000000..91232d7 --- /dev/null +++ b/inst/extdata/sectoral/filemappings/start_MPI-ESM1-2-HR_picontrol.R @@ -0,0 +1,46 @@ +library(mrremind) +library(mrcommons) + + +# load libraries +devtools::load_all("dev/mredgebuildings") +#library(mredgebuildings) + +setConfig(outputfolder = "/p/tmp/hagento/output", + mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", + ignorecache = c("ISIMIPbuildings")) + +#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") + +# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. +# Each list element is a named vector with: +# - the first element "regionmapping": region mapping used for the aggregation of the input data +# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping + +# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" + +# Current input data revision (.) #### +revision <- "0.1" # should be a number with two decimal places for production +cachetype <- "def" +dev <- "" +puc <- identical(dev, '') +renv <- FALSE + +sessionInfo() + + +# HISTORICAL + +f <- "MPI-ESM1-2-HR_historical.csv" + +# EDGE mapping +# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, +# dev = dev, cachetype = cachetype, puc = puc, renv = renv) + +dev <- paste0("_", gsub(".csv", "", f)) + +# ISO +retrieveData(model = "hddcdd", file = f, rev = revision, + dev = dev, cachetype = cachetype, puc = puc, renv = renv) + + diff --git a/inst/extdata/sectoral/filemappings/start_MRI-ESM2-0_picontrol.R b/inst/extdata/sectoral/filemappings/start_MRI-ESM2-0_picontrol.R new file mode 100644 index 0000000..c525d21 --- /dev/null +++ b/inst/extdata/sectoral/filemappings/start_MRI-ESM2-0_picontrol.R @@ -0,0 +1,46 @@ +library(mrremind) +library(mrcommons) + + +# load libraries +devtools::load_all("dev/mredgebuildings") +#library(mredgebuildings) + +setConfig(outputfolder = "/p/tmp/hagento/output", + mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", + ignorecache = c("ISIMIPbuildings")) + +#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") + +# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. +# Each list element is a named vector with: +# - the first element "regionmapping": region mapping used for the aggregation of the input data +# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping + +# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" + +# Current input data revision (.) #### +revision <- "0.1" # should be a number with two decimal places for production +cachetype <- "def" +dev <- "" +puc <- identical(dev, '') +renv <- FALSE + +sessionInfo() + + +# HISTORICAL + +f <- "MRI-ESM2-0_historical.csv" + +# EDGE mapping +# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, +# dev = dev, cachetype = cachetype, puc = puc, renv = renv) + +dev <- paste0("_", gsub(".csv", "", f)) + +# ISO +retrieveData(model = "hddcdd", file = f, rev = revision, + dev = dev, cachetype = cachetype, puc = puc, renv = renv) + + diff --git a/inst/extdata/sectoral/filemappings/start_UKESM1-0-LL_picontrol.R b/inst/extdata/sectoral/filemappings/start_UKESM1-0-LL_picontrol.R new file mode 100644 index 0000000..b32d630 --- /dev/null +++ b/inst/extdata/sectoral/filemappings/start_UKESM1-0-LL_picontrol.R @@ -0,0 +1,46 @@ +library(mrremind) +library(mrcommons) + + +# load libraries +devtools::load_all("dev/mredgebuildings") +#library(mredgebuildings) + +setConfig(outputfolder = "/p/tmp/hagento/output", + mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", + ignorecache = c("ISIMIPbuildings")) + +#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") + +# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. +# Each list element is a named vector with: +# - the first element "regionmapping": region mapping used for the aggregation of the input data +# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping + +# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" + +# Current input data revision (.) #### +revision <- "0.1" # should be a number with two decimal places for production +cachetype <- "def" +dev <- "" +puc <- identical(dev, '') +renv <- FALSE + +sessionInfo() + + +# HISTORICAL + +f <- "UKESM1-0-LL_historical.csv" + +# EDGE mapping +# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, +# dev = dev, cachetype = cachetype, puc = puc, renv = renv) + +dev <- paste0("_", gsub(".csv", "", f)) + +# ISO +retrieveData(model = "hddcdd", file = f, rev = revision, + dev = dev, cachetype = cachetype, puc = puc, renv = renv) + + From 2e4e74959d2b0669a59679640184959a249d8f9a Mon Sep 17 00:00:00 2001 From: hagento <118460073+hagento@users.noreply.github.com> Date: Sun, 3 Mar 2024 13:03:27 +0100 Subject: [PATCH 02/10] integrate changes in R/calcHDDCDD.R Co-authored-by: Robin Hasse <76682203+robinhasse@users.noreply.github.com> --- R/calcHDDCDD.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index 044b5fe..4158b67 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -1,7 +1,7 @@ #' calculate HDD and CDD based on outdoor/indoor temperature difference #' #' @description heating and cooling degree days based on raw outside temperature -#' or bais-adjusted internal temperature (BAIT), driver for space heating and +#' or bias-adjusted internal temperature (BAIT), driver for space heating and #' cooling demand in buildings #' #' @param mappingFile data.frame containing input data file names and directories From d92a1b6e1a6c2428b0bbc18ab92bdd6d5828e846 Mon Sep 17 00:00:00 2001 From: hagento <118460073+hagento@users.noreply.github.com> Date: Sun, 3 Mar 2024 13:16:50 +0100 Subject: [PATCH 03/10] Apply suggestions from code review Co-authored-by: Robin Hasse <76682203+robinhasse@users.noreply.github.com> --- R/calcHDDCDD.R | 50 ++++++++++++++++++++++++++------------------------ 1 file changed, 26 insertions(+), 24 deletions(-) diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index 4158b67..8bae8db 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -4,13 +4,13 @@ #' or bias-adjusted internal temperature (BAIT), driver for space heating and #' cooling demand in buildings #' -#' @param mappingFile data.frame containing input data file names and directories -#' @param bait specify use of raw temperature or BAIT -#' @param multiscen specify if mappingFile covers more than one scenario +#' @param mappingFile file name of sectoral mapping containing input data file names and directories +#' @param bait boolean, use BAIT instead of raw temperature +#' @param multiscen boolean, does \code{mappingFile} cover more than one scenario? #' #' @return magpie object of heating and cooling degree days #' -#' @author Robin Krekeler, Hagen Tockhorn +#' @author Robin Hasse, Hagen Tockhorn #' @examples #' #' \dontrun{ @@ -18,14 +18,14 @@ #' } #' #' @importFrom madrat getConfig -#' @importFrom raster cellStats +#' @importFrom raster cellStats xyFromCell ncell #' @importFrom ncdf4 nc_open #' @importFrom tidyr %>% #' @importFrom dplyr mutate #' @importFrom rlang .data #' @importFrom pracma integral2 #' @importFrom stringr str_sub -#' @importFrom terra app nlyr tapp subset rast classify time +#' @importFrom terra app nlyr tapp subset rast classify time setGDALconfig calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { @@ -40,14 +40,14 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { filename <- gsub(".nc|.nc4", "", filename) } - if (bait) { - dStart <- as.Date(stringr::str_sub(filename, -17, -10), + if (isTRUE(bait)) { + dStart <- as.Date(str_sub(filename, -17, -10), format = "%Y%m%d") n <- nlyr(r) dates <- seq.Date(dStart, by = "day", length.out = n) } else { - yStart <- stringr::str_sub(filename, -9, -6) + yStart <- str_sub(filename, -9, -6) dStart <- as.Date(paste0(yStart, "-1-1")) n <- nlyr(r) @@ -62,7 +62,7 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { # prepare input for calcBAIT() calcBaitInput <- function(frsds=NULL, fsfc=NULL, fhuss=NULL, baitInput=NULL, mean=FALSE) { - if(mean) { + if (isTRUE(mean)) { # optional: calculate daily means over years to fill missing data baitInputMean <- sapply( names(baitInput), function(var) { @@ -100,7 +100,7 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { daysFill <- unique(substr(datesFill, 6, 11)) datesKeep <- intersect(dates_b, dates_t) # dates to keep - keep <- ifelse(length(datesKeep) > 0, TRUE, FALSE) + keep <- length(datesKeep) > 0 if (keep) { tmp <- subset(tmp, datesKeep) @@ -133,7 +133,7 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { if (!identical(names(tmp), names(temp))) { - return(print("Warning: Dates of Temperature and BAIT Input Data are not aligned.")) + warning("Dates of Temperature and BAIT Input Data are not aligned.") } return(tmp) }, @@ -157,12 +157,13 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { t = c(16))} print(names(params)) - if (type == "s") {return(params[[1]] + params[[2]]*t)} - else if (type == "w") {return(params[[1]] + params[[2]]*t)} - else if (type == "h") {return(exp(params[[1]] + params[[2]]*t))} - else if (type == "t") {return(params[[1]])} - - else {print("No valid parameter type specified.")} + return(switch(type, + s = {params[[1]] + params[[2]] * t}, + w = {params[[1]] + params[[2]] * t}, + h = {exp(params[[1]] + params[[2]] * t)}, + t = {params[[1]]}, + error("No valid parameter type specified.") + )) } @@ -458,11 +459,10 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) - if (bait) {rname <- paste0(rname, "_bait.nc")} - else {rname <- paste0(rname, ".nc")} + rname <- paste0(rname, if (bait) "_bait" else "", ".nc") - terra::writeRaster(hddcdd_save, + writeRaster(hddcdd_save, paste0("/p/tmp/hagento/output/rasterdata/", rname), overwrite = TRUE) } @@ -542,7 +542,7 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { plotRaster <- function(r) { library("ggplot2") r_map <- stack(as.data.frame(raster::getValues(r))) - coords <- raster::xyFromCell(r, seq_len(raster::ncell(r))) + coords <- xyFromCell(r, seq_len(ncell(r))) names(r_map) <- c('value', 'variable') r_map <- cbind(coords, r_map) ggplot(r_map) + @@ -556,12 +556,12 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { # PARAMETERS------------------------------------------------------------------ - terra::setGDALconfig(c("BIGTIFF = YES")) + setGDALconfig(c("BIGTIFF = YES")) # threshold temperature for heating and cooling [C] # NOTE: Staffel gives global average of T_heat = 14, T_cool = 20 - # t_lim <- list("HDD" = seq(12, 18), "CDD" = seq(20, 26)) + # t_lim <- list("HDD" = seq(12, 22), "CDD" = seq(20, 26)) t_lim <- list("HDD" = seq(14), "CDD" = seq(22)) # standard deviations for temperature distributions @@ -592,6 +592,8 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { # The blending parameters for the blending of BAIT and raw temperature are like-wise # taken from the paper. + # At bLower, we consider only BAIT. For higher temperatures, we assume a mix with the ambient + # temperature reaching the highest contribution of bMax at bUpper following a sigmoid function bLower <- 15 bUpper <- 23 bMax <- 0.5 From 22730a194c33393f31029b2643394f38de75cefc Mon Sep 17 00:00:00 2001 From: hagento Date: Mon, 4 Mar 2024 11:36:26 +0100 Subject: [PATCH 04/10] rearranging functions with added individual roxygen headers --- R/calcHDDCDD.R | 1115 +++++++++++++++++++++++++++--------------------- 1 file changed, 621 insertions(+), 494 deletions(-) diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index 8bae8db..1baa975 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -1,4 +1,4 @@ -#' calculate HDD and CDD based on outdoor/indoor temperature difference +#' Calculate HDD and CDD based on outdoor/indoor temperature difference #' #' @description heating and cooling degree days based on raw outside temperature #' or bias-adjusted internal temperature (BAIT), driver for space heating and @@ -11,477 +11,21 @@ #' @return magpie object of heating and cooling degree days #' #' @author Robin Hasse, Hagen Tockhorn -#' @examples #' #' \dontrun{ #' calcHDDCDD() #' } #' -#' @importFrom madrat getConfig -#' @importFrom raster cellStats xyFromCell ncell -#' @importFrom ncdf4 nc_open +#' @importFrom madrat toolGetMapping readSource calcOutput toolCountryFill #' @importFrom tidyr %>% #' @importFrom dplyr mutate #' @importFrom rlang .data -#' @importFrom pracma integral2 #' @importFrom stringr str_sub -#' @importFrom terra app nlyr tapp subset rast classify time setGDALconfig +#' @importFrom terra setGDALconfig calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { - - # FUNCTIONS------------------------------------------------------------------- - - # fill dates for unnamed data - # ---> was supposed to go to convertISIMIP, doesn't work with country check - fillDates <- function(r, filename, bait=FALSE) { - if (grepl(".nc|.nc4", filename)) { - filename <- gsub(".nc|.nc4", "", filename) - } - - if (isTRUE(bait)) { - dStart <- as.Date(str_sub(filename, -17, -10), - format = "%Y%m%d") - n <- nlyr(r) - dates <- seq.Date(dStart, by = "day", length.out = n) - } - else { - yStart <- str_sub(filename, -9, -6) - dStart <- as.Date(paste0(yStart, "-1-1")) - n <- nlyr(r) - - dates <- seq.Date(dStart, by = "day", length.out = n) - } - - # fill dates - names(r) <- dates - return(r) - } - - - # prepare input for calcBAIT() - calcBaitInput <- function(frsds=NULL, fsfc=NULL, fhuss=NULL, baitInput=NULL, mean=FALSE) { - if (isTRUE(mean)) { - # optional: calculate daily means over years to fill missing data - baitInputMean <- sapply( - names(baitInput), function(var) { - mean <- tapp(baitInput[[var]], - unique(substr(names(baitInput[[var]]), 6, 11)), - fun = "mean") - names(mean) <- gsub("\\.", "-", substr(names(mean), 2, 6)) - return(mean)}) - } - else { - input <- list( - "rsds" = readSource("ISIMIPbuildings", subtype = frsds, convert = TRUE), - "sfc" = readSource("ISIMIPbuildings", subtype = fsfc, convert = TRUE), - "huss" = readSource("ISIMIPbuildings", subtype = fhuss, convert = TRUE)) - return(input) - } - } - - - #check if time period is congruent and adapt if necessary - checkDates <- function(baitInput, temp) { - dates_t <- names(temp) - - baitInput <- sapply(names(baitInput), function(var) { - # fill missing data with means from previous years - # NOTE: "temp" and "baitInput" have the same global temporal lower - # boundary, since "temp" is the constraining dataset, only - # "baitInput" needs to be filled up. - - tmp <- baitInput[[var]] - - dates_b <- names(tmp) - - datesFill <- setdiff(dates_t, dates_b) # dates to fill up - daysFill <- unique(substr(datesFill, 6, 11)) - - datesKeep <- intersect(dates_b, dates_t) # dates to keep - keep <- length(datesKeep) > 0 - - if (keep) { - tmp <- subset(tmp, datesKeep) - names(tmp) <- datesKeep - } - - if (length(daysFill) > 0) { - baitInputMean <- calcBaitInput(mean = TRUE, baitInput = baitInput) - - # fill up missing dates with yearly-average value for specific day/cell - baitInputFill <- rast(lapply( - daysFill, function(d){ - print(d) - idx <- which(grepl(d, stringr::str_sub(datesFill, -5, -1))) - r <- rast(replicate(length(idx), - baitInputMean[[var]][[d]])) - names(r) <- datesFill[idx] - return(r) - } - ) - ) - - # concatenate data - if (keep) {tmp <- rast(list(tmp, baitInputFill))} - else {tmp <- baitInputFill} - - # re-order dates - tmp <- rast(tmp[[order(names(tmp))]]) - } - - - if (!identical(names(tmp), names(temp))) { - warning("Dates of Temperature and BAIT Input Data are not aligned.") - } - return(tmp) - }, - USE.NAMES = TRUE - ) - return(baitInput) - } - - - #--- CALCULATE BAIT (building-adjusted internal temperature) - - # NOTE: The parameters are taken from Staffell et al. 2023 - - # counterfactuals for solar, wind, humidity, temperature - cfac <- function(t, type, params=NULL) { - if (is.null(params)) { - params <- switch(type, - s = c(100, 7), - w = c(4.5, -0.025), - h = c(1.1, 0.06), - t = c(16))} - - print(names(params)) - return(switch(type, - s = {params[[1]] + params[[2]] * t}, - w = {params[[1]] + params[[2]] * t}, - h = {exp(params[[1]] + params[[2]] * t)}, - t = {params[[1]]}, - error("No valid parameter type specified.") - )) - } - - - # smooth data - smooth <- function(r, weight) { - # smooth bait over preceding two days with smoothing parameter sigma - print("smooth") - - # one day indented - r1D <- r[[c(nlyr(r), 1:(nlyr(r) - 1))]] - r1D[[1]] <- 0 - - # two days indented - r2D <- r[[c(nlyr(r)-1, nlyr(r), 1:(nlyr(r) - 2))]] - r2D[[1:2]] <- 0 - - # smooth - rSmooth <- (r + weight[[4]]*r1D + weight[[4]]**2 * r2D) / (1 + weight[[4]] + weight[[4]]**2) - - return(rSmooth) - } - - blend <- function(baitDF, temp, weight) { - # weighted blend of BAIT and raw temperature - print("blend") - bBar <- (temp - 0.5*(weight[[6]] + weight[[5]])) * 10 / (weight[[6]] - weight[[5]]) - b <- weight[[7]] / (1 + exp(-bBar)) - - baitDF <- baitDF * (1 - b) + (temp * b) - return(baitDF) - } - - - calcBAIT <- function(baitInput, temp, weight=NULL, params=NULL) { - # weight=(x,y,z,sigma,bLower,bUpper,bMax) - if (is.null(weight)) { - print("Please give appropriate weights for the calculation of BAIT.") - weight <- c(1,1,1,1,1,1,1)} - - dates <- names(temp) - - solar <- baitInput$rsds - wind <- baitInput$sfc - hum <- baitInput$huss - - print("calc s") - s <- solar - cfac(temp, type="s", params = c(params[["a_rsds"]], params[["b_rsds"]])) - print("calc w") - w <- wind - cfac(temp, type="w", params = c(params[["a_sfcwind"]], params[["b_sfcwind"]])) - print("print h") - h <- hum - cfac(temp, type="h", params = c(params[["a_huss"]], params[["b_huss"]])) - print("calc t") - t <- temp - cfac(temp, type="t", params = NULL) - - # calc raw bait - print("calc bait") - bait <- temp + weight[[1]]*s + weight[[2]]*w + weight[[3]]*h*t - - bait <- smooth(bait, weight) - bait <- blend(bait, temp, weight) - - return(bait) - } - - - #--- CALCULATE HDD/CDD - - # Calculate heating factor matrix for normally distributed ambient - # and limit temperatures - - # t1 : ambient temperature variable - # t2 : limit temperature variable - - heatingFactor <- function(t2, t1, tamb, tamb_std, tlim, tlim_std) { - h <- dnorm(t2, mean=tlim, sd=tlim_std) * dnorm(t1, mean=tamb, sd=tamb_std) * (t2 - t1) - return(h) - } - - coolingFactor <- function(t2, t1, tamb, tamb_std, tlim, tlim_std) { - h <- dnorm(t2, mean=tlim, sd=tlim_std) * dnorm(t1, mean=tamb, sd=tamb_std) * (t1 - t2) - return(h) - } - - # check if ambient/limit temperature interval is reasonable - # e.g. t_lim = 17C and t_amb = -50C wouldn't give reasonable CDD - checkTDif <- function(tamb, tlim, typeDD, tamb_std, tlim_std) { - check <- TRUE - stdDif <- tamb_std + tlim_std - if (typeDD == "HDD") { - if (tamb - tlim > stdDif) { - check <- FALSE - } - } - else if (typeDD == "CDD") { - if (tlim - tamb > 2*stdDif) { - check <- FALSE - } - } - return(check) - } - - # calculate HDD/CDD values per day for given ambient/limit temp combination - calcHDDCDDFactors <- function(tlow, tup, tlim, tamb_std=5, tlim_std=5) { - t <- seq(tlow, tup, .1) - - hddcddFactors <- do.call( - "rbind", lapply( - c("HDD", "CDD"), function(typeDD) { - do.call( - "rbind", lapply( - t, function(tamb) { - do.call( - "rbind", lapply( - tlim[[typeDD]], function(.tlim) { - if (!checkTDif(tamb, .tlim, typeDD, tamb_std, tlim_std)) { - tmp <- data.frame("T_amb" = tamb, - "T_amb_K" = round(tamb + 273.15, 1), - "T_lim" = .tlim, - "factor" = 0, - "factor_err" = 0, - "typeDD" = typeDD) - } - else { - - # integration boundaries - x1 <- .tlim - 4*tlim_std - x2 <- .tlim + 4*tlim_std - y1 <- min(.tlim - 3*tlim_std, tamb - 3*tlim_std) - y2 <- max(.tlim + 3*tlim_std, tamb + 3*tlim_std) - - if (typeDD == "HDD") { - f <- pracma::integral2(heatingFactor, - xmin = x1, - xmax = x2, - ymin = y1, - ymax = function(x){x}, - tamb = tamb, - tamb_std = tamb_std, - tlim = .tlim, - tlim_std = tlim_std, - reltol = 1e-1 - ) - } - else { - f <- pracma::integral2(coolingFactor, - xmin = x1, - xmax = x2, - ymin = function(x){x}, - ymax = y2, - tamb = tamb, - tamb_std = tamb_std, - tlim = .tlim, - tlim_std = tlim_std, - reltol = 1e-1) - } - tmp <- data.frame("T_amb" = tamb, - "T_amb_K" = round(tamb + 273.15, 1), - "T_lim" = .tlim, - "factor" = f$Q, - "factor_err" = f$error, - "typeDD" = typeDD) - } - } - ) - ) - } - ) - ) - } - ) - ) - return(hddcddFactors) - } - - - - # fill HDD/CDD from factors for given ambient/limit temperature combination - calcCellHDDCDD <- function(temp, .typeDD, .tlim, factors) { - # extract years - dates <- names(temp) - - # add tolerance of 0.04K to avoid machine precision errors - factors <- factors %>% - filter(.data[["typeDD"]] == .typeDD, .data[["T_lim"]] == .tlim) %>% - dplyr::reframe(from = .data[["T_amb_K"]] - 0.049, - to = .data[["T_amb_K"]] + 0.049, - becomes = .data[["factor"]]) %>% - data.matrix() - - # swap ambient temperature values with corresponding DD values - hddcdd <- classify(temp, factors) - - terra::time(hddcdd) <- as.Date(dates) - - # aggregate to yearly HDD/CDD [K.d/a] - hddcdd <- tapp(hddcdd, "years", fun = sum, na.rm = TRUE) - - names(hddcdd) <- gsub("_", "", names(hddcdd)) - return(hddcdd) - } - - - # aggregate cellular HDD/CDD to country-wide average (population-weighted) - aggCells <- function(r, weight, mask) { - years_r <- names(r) - years_w <- names(weight) - - print(paste("years_r:", years_r)) - print(paste("years_w:", years_w)) - - if (!all(years_r %in% years_w)) { - stop("Time periods of raster file and aggregation weights do not match.") - } - - # loop: years in raster file r - hddcddAgg <- do.call( - "rbind", lapply( - years_r, function(y) { - browser() - tmp <- subset(r, y) * subset(weight, y) * mask - tmpTot <- subset(weight, y) * mask - - tmpSum <- terra::global(tmp, "sum", na.rm = TRUE)$sum - tmpTotSum <- terra::global(tmpTot, "sum", na.rm = TRUE)$sum - tmp <- tmpSum / tmpTotSum - tmp <- data.frame("region" = names(mask), - "period" = y, - "value" = round(tmp, 3)) - rownames(tmp) <- c() - return(tmp) - } - ) - ) - return(hddcddAgg) - } - - - # calculate all desired output from given tas file - calcStackHDDCDD <- function(file, tlim, countries, pop, factors, bait, - frsds = NULL, - fsfc = NULL, - fhuss = NULL, - wBAIT = NULL, - params = NULL) { - - # read cellular temperature - temp <- readSource("ISIMIPbuildings", subtype = file, convert = TRUE) - - dates <- names(temp) - - # optional: transform raw temperature into BAIT - if (bait) { - # note: easier to do in [C] - temp <- temp - 273.15 # [C] - - # read and prepare bait input data - baitInput <- calcBaitInput(frsds, fsfc, fhuss) %>% - checkDates(temp) - - # calculate bait - temp <- calcBAIT(baitInput, temp, weight = wBAIT, params = params) - - # convert back to [K] - temp <- temp + 273.15 # [K] - } - - temp <- terra::round(temp, digits = 1) - names(temp) <- dates - - print("Calculating HDD/CDDs per cell.") - - fSplit <- str_split(file, "_") %>% unlist() - - # loop: typeDD - hddcdd <- do.call( - "rbind", lapply( - c("HDD", "CDD"), function(typeDD) { - # loop: threshold temperatures - do.call( - "rbind", lapply( - tlim[[typeDD]], function(t) { - hddcdd_agg <- calcCellHDDCDD(temp, typeDD, t, factors) - - # save intermediate results - decades <- c(as.numeric(gsub("y", "", names(hddcdd_agg))) %% 10 == 0) - if (any(decades)) { - hddcdd_save <- hddcdd_agg[[decades]] - - y <- names(hddcdd_agg)[as.numeric(gsub("y", "", names(hddcdd_agg))) %% 10 == 0] - - y <- gsub("y", "", y) %>% - paste(collapse = "-") - - rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) - - rname <- paste0(rname, if (bait) "_bait" else "", ".nc") - - - writeRaster(hddcdd_save, - paste0("/p/tmp/hagento/output/rasterdata/", rname), - overwrite = TRUE) - } - - hddcdd_agg <- hddcdd_agg %>% - aggCells(pop, countries) %>% - mutate("variable" = typeDD, - "tlim" = t) # [C] - - return(hddcdd_agg) - } - ) - ) - } - ) - ) - } - - # initialize full calculation makeCalculations <- function(f, m, n, t_lim, countries, pop, hddcddFactor, bait, wBAIT = NULL, params = NULL, suffix = NULL) { @@ -539,20 +83,6 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { } - plotRaster <- function(r) { - library("ggplot2") - r_map <- stack(as.data.frame(raster::getValues(r))) - coords <- xyFromCell(r, seq_len(ncell(r))) - names(r_map) <- c('value', 'variable') - r_map <- cbind(coords, r_map) - ggplot(r_map) + - geom_tile(aes(x, y, fill = value)) + - facet_wrap(~ variable) + - scale_fill_gradientn(colours = rev(terrain.colors(225))) + - coord_equal() - } - - # PARAMETERS------------------------------------------------------------------ @@ -560,34 +90,33 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { # threshold temperature for heating and cooling [C] - # NOTE: Staffel gives global average of T_heat = 14, T_cool = 20 - # t_lim <- list("HDD" = seq(12, 22), "CDD" = seq(20, 26)) - t_lim <- list("HDD" = seq(14), "CDD" = seq(22)) + # NOTE: Staffel at. al 2023 gives global average of T_heat = 14, T_cool = 20 + tLim <- list("HDD" = seq(12, 22), "CDD" = seq(20, 26)) # standard deviations for temperature distributions - tlim_std <- 5 # threshold - tamb_std <- 5 # ambient - - # historical years -> NOTE: not sure if further needed - firstHistYear <- 1950 - lastHistYear <- 2009 + tlimStd <- 5 # threshold + tambStd <- 5 # ambient - # range of pre-calculated HDD/CDD-values, e.g. [223, 232] K, converted to [C] - tlow <- 223 - 273.15 - tup <- 323 - 273.15 + # range of pre-calculated HDD/CDD-values, e.g. [173, 348] K, converted to [C] + tlow <- 173 - 273.15 + tup <- 348 - 273.15 #--- BAIT parameters - # The weights (x,y,z) for calcBAIT and the smoothing coefficient are assumed to + # The weights (wRSDS, wSFC, wHUSS) for calcBAIT and the smoothing coefficient are assumed to # be region-independent and equal to the mean of the values given in Staffell - # et al. 2023 - x <- 0.012 - y <- -0.20 - z <- 0.05 + # et al. 2023. + + # The weights below give the respective weight of the difference between the + # climate factor and its counterfactual in calculating BAIT. + wRSDS <- 0.012 + wSFC <- -0.20 + wHUSS <- 0.05 - # smoothing coefficient + # The smoothing coefficient defines the assumed thermal intertia of the building + # and weighs the influence of the internal temperature of the preceding two days. sig <- 0.50 # The blending parameters for the blending of BAIT and raw temperature are like-wise @@ -599,10 +128,17 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { bMax <- 0.5 # concatenate to vector - wBAIT <- c(x, y, z, sig, bLower, bUpper, bMax) + wBAIT <- list("wRSDS" = wRSDS, + "wSFC" = wSFC, + "wHUSS" = wHUSS, + "sig" = sig, + "bLower" = bLower, + "bUpper" = bUpper, + "bMax" = bMax) # BAIT parameter names - parNames <- c("a_rsds", "b_rsds", "a_sfcwind", "b_sfcwind", "a_huss", "b_huss") + # (note: this is really just a check in case calcBAITpars returns no layer names) + parNames <- c("aRSDS", "bRSDS", "aSFC", "bSFC", "aHUSS", "bHUSS") @@ -621,7 +157,7 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { # PROCESS DATA---------------------------------------------------------------- # calculate HDD/CDD-factors - hddcddFactor <- calcHDDCDDFactors(tlow=-100.15, tup=74.85, t_lim, tamb_std, tlim_std) + hddcddFactor <- calcHDDCDDFactors(tlow = tlow, tup = tup, tLim, tambStd, tlimStd) ssp <- unique(files$ssp[files$variable == "tas"]) rcp <- unique(files$rcp[files$variable == "tas"]) @@ -685,6 +221,8 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { "ssp" = ssp, "rcp" = rcp) + gc() + return(hddcddCell) } ) @@ -713,4 +251,593 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { } +#--- CALCULATE BAIT (building-adjusted internal temperature) + + +#' Check if time period of BAIT input data (rsds, sfc, huss) is congruent with +#' near-surface temperature data (tas). +#' +#' @param baitInput list of raster data encompassing different climate variables +#' @param tasData raster data on near-surface atmosphere temperature +#' +#' @return baitInput with congruent time periods w.r.t. tasData +#' +#' @importFrom stringr substr +#' @importFrom terra rast + +checkDates <- function(baitInput, tasData) { + dates_t <- names(tasData) + + baitInput <- sapply(names(baitInput), function(var) { + # fill missing data with means from previous years + # NOTE: "temp" and "baitInput" have the same global temporal lower + # boundary, since "temp" is the constraining dataset, only + # "baitInput" needs to be filled up. + + tmp <- baitInput[[var]] + + dates_b <- names(tmp) + + datesFill <- setdiff(dates_t, dates_b) # dates to fill up + daysFill <- unique(substr(datesFill, 6, 11)) + + datesKeep <- intersect(dates_b, dates_t) # dates to keep + keep <- length(datesKeep) > 0 + + if (keep) { + tmp <- subset(tmp, datesKeep) + names(tmp) <- datesKeep + } + + if (length(daysFill) > 0) { + baitInputMean <- prepBaitInput(mean = TRUE, baitInput = baitInput) + + # fill up missing dates with yearly-average value for specific day/cell + baitInputFill <- rast(lapply( + daysFill, function(d){ + idx <- which(grepl(d, stringr::str_sub(datesFill, -5, -1))) + r <- rast(replicate(length(idx), + baitInputMean[[var]][[d]])) + names(r) <- datesFill[idx] + return(r) + } + ) + ) + + # concatenate data + if (keep) {tmp <- rast(list(tmp, baitInputFill))} + else {tmp <- baitInputFill} + + # re-order dates + tmp <- rast(tmp[[order(names(tmp))]]) + } + + + if (!identical(names(tmp), names(tasData))) { + warning("Dates of Temperature and BAIT Input Data are not aligned.") + } + return(tmp) + }, + USE.NAMES = TRUE + ) + return(baitInput) +} + + +#' Read in necessary climate data to calculate BAIT or calculate mean values of +#' said climate data to fill missing data in case of temporal mismatch between +#' near-surface atmospherical temperature and other considered climate data. +#' +#' @param frsds file path to raster data on surface downdwelling shortwave radiation +#' @param fsfc file path to raster data on near-surface wind speed +#' @param fhuss file path to raster data on near-surface specific humidity +#' @param baitInput named list of climate data +#' @param fillWithMean boolean, only mean is calculated and returned +#' +#' @return named list with read-in or meaned climate data +#' +#' @importFrom terra tapp +#' @importFrom madrat readSource + +prepBaitInput <- function(frsds=NULL, fsfc=NULL, fhuss=NULL, baitInput=NULL, fillWithMean=FALSE) { + if (isTRUE(fillWithMean)) { + # optional: calculate daily means over years to fill missing data + baitInputMean <- sapply( + names(baitInput), function(var) { + meanData <- tapp(baitInput[[var]], + unique(substr(names(baitInput[[var]]), 6, 11)), + fun = "mean") + names(meanData) <- gsub("\\.", "-", substr(names(mean), 2, 6)) + return(meanData)}) + return(baitInputMean) + } + else { + input <- list( + "rsds" = readSource("ISIMIPbuildings", subtype = frsds, convert = TRUE), + "sfc" = readSource("ISIMIPbuildings", subtype = fsfc, convert = TRUE), + "huss" = readSource("ISIMIPbuildings", subtype = fhuss, convert = TRUE)) + return(input) + } +} + + +#' Calculate counterfactuals for solar radiation, wind speed, specific humidity +#' and near-surface temperature as function of raster data on near-surface temperature. +#' +#' The expected value of the respective climate variable (except temperature) is +#' calculated from parameters taken from a preceding linear regression done in +#' calcBAITpars where the respective variable is correlated with the near-surface +#' atmospherical temperature. +#' If no cell-resoluted parameters are given, the globally-meaned parameters from +#' Staffel et. all 2023 are taken +#' (see \link{https://static-content.springer.com/esm/art%3A10.1038%2Fs41560-023-01341-5/MediaObjects/41560_2023_1341_MOESM1_ESM.pdf}) +#' +#' @param t raster data on near-surface atmospherical temperature +#' @param type considered climate variable +#' @param params regression parameters as vector or raster object +#' +#' @return counterfactuals for respective climate variable + +cfac <- function(t, type, params=NULL) { + if (is.null(params)) { + params <- switch(type, + s = c(100, 7), + w = c(4.5, -0.025), + h = c(1.1, 0.06), + t = c(16))} + + return(switch(type, + s = {params[[1]] + params[[2]] * t}, + w = {params[[1]] + params[[2]] * t}, + h = {exp(params[[1]] + params[[2]] * t)}, + t = {params[[1]]}, + error("No valid parameter type specified.") + )) +} + + +#' Smooth data over preceding two days +#' +#' @param r raster data to be smoothed +#' @param weight named list with smoothing parameter sig +#' +#' @return smoothed raster data +#' +#' @importFrom terra nlyr + +smooth <- function(r, weight) { + # smooth bait over preceding two days with smoothing parameter sigma + print("smooth") + + # one day indented + r1D <- r[[c(nlyr(r), 1:(nlyr(r) - 1))]] + r1D[[1]] <- 0 + + # two days indented + r2D <- r[[c(nlyr(r)-1, nlyr(r), 1:(nlyr(r) - 2))]] + r2D[[1:2]] <- 0 + + # smooth + rSmooth <- (r + weight[["sig"]]*r1D + weight[["sig"]]**2 * r2D) / (1 + weight[["sig"]] + weight[["sig"]]**2) + + return(rSmooth) +} + + +#' Weighted blend of BAIT and near-surface atmospherical temperature +#' +#' To adress loss of buildings' internal memory of previous conditions at high +#' outside temperatures due to window opening, etc., BAIT and outside temperature +#' are blended. The blend is active between a lower and upper temperature threshold, +#' \code{bLower} and \code{bUpper}, which are mapped to a range of -5 to +5 of a +#' sigmoid function (corresponding to a 1% and 99% blend). The maximum amount of +#' blending (i.e. the amplitude of the sigmoid function) is given by a parameter \code{bMax}. +#' +#' @param bait raster data on BAIT +#' @param tas raster data on near-surface atmospherical temperature +#' @param weight named list with blending parameters bLower, bUpper, bMax +#' +#' @return blended raster data + +blend <- function(bait, tas, weight) { + print("blend") + bBar <- (tas - 0.5*(weight[["bUpper"]] + weight[["bLower"]])) * 10 / (weight[["bUpper"]] - weight[["bLower"]]) + b <- weight[["bMax"]] / (1 + exp(-bBar)) + + blend <- bait * (1 - b) + (tas * b) + return(blend) +} + + +#' Calculate bias-adjusted internal temperature (BAIT) +#' +#' BAIT is calculated from raster data on near-surface atmospherical temperature +#' (tas), surface downdwelling shortwave radiation (rsds), near-surface wind speed +#' (sfcwind) and near-surface specific humidity (huss). The latter three climate +#' parameters are incorporated in the calculation of BAIT as the difference from +#' their real value to the their expected value w.r.t. the near-surface temperature +#' (see \code{\link{cfac}}). These are then incorporated in a weighted sum to +#' account for the respective influence of each climate parameter on BAIT. +#' The raster data containing BAIT is smoothed to account for the +#' buildings' thermal inertia (see \code{\link{smooth}}) and blended with the +#' near-surface temperature (see \code{\link{blend}}). +#' +#' @param baitInput named list containing rsds, sfcwind, huss climate data +#' @param tas raster data on near-surface atmospherical temperature +#' @param weight named list with weights +#' @param params optional named list with regression parameters from calcBAITpars +#' +#' @return raster object with BAIT values + +calcBAIT <- function(baitInput, tasData, weight=NULL, params=NULL) { + if (is.null(weight)) { + warning("Please give appropriate weights for the calculation of BAIT.") + weight <- list("wRSDS" = wRSDS, + "wSFC" = wSFC, + "wHUSS" = wHUSS, + "sig" = sig, + "bLower" = bLower, + "bUpper" = bUpper, + "bMax" = bMax)} + + dates <- names(tasData) + + solar <- baitInput$rsds + wind <- baitInput$sfc + hum <- baitInput$huss + + print("calc s") + s <- solar - cfac(tasData, type="s", params = c(params[["aRSDS"]], params[["bRSDS"]])) + print("calc w") + w <- wind - cfac(tasData, type="w", params = c(params[["aSFC"]], params[["bSFC"]])) + print("print h") + h <- hum - cfac(tasData, type="h", params = c(params[["aHUSS"]], params[["bHUSS"]])) + print("calc t") + t <- tasData - cfac(tasData, type="t", params = NULL) + + # calc bait + print("calc bait") + bait <- tasData + weight[["wRSDS"]]*s + weight[["wSFC"]]*w + weight[["wHUSS"]]*h*t + + bait <- smooth(bait, weight) + bait <- blend(bait, tasData, weight) + + rm(baitInput, solar, wind, hum, s, w, h, t, bait) + + return(bait) +} + + +#--- CALCULATE HDD/CDD + +#' Calculate HDD/CDD values for different ambient/limit temperature combinations +#' +#' HDD/CDD values are pre-calculated for an interval \code{tlow}-\code{tup} and +#' for a set of limit temperatures \code{tlim} with a temperature resolution of +#' 0.1C. +#' +#' The respective heating/cooling degree days are calculated as the difference +#' between the assumed ambient and a limit temperature, aggregated to a full day. +#' The latter defines a threshold above/below which cooling/heating is assumed to +#' be initiated. +#' +#' To account for heterogenity in heating/cooling behavior, the ambient and limit +#' temperature, \code{tamb} and \code{tlim}, are assumed to be normally distributed. +#' This changes the calculation of a degree day to a double integration of +#' \code{T_limit - T_ambient_day} with integration boundaries set at 3 standard +#' deviations, \code{tambStd} and \code{tlimStd}, from \code{tamb} and \code{tlim} +#' respectively. +#' +#' As consequence, the ramp function of \code{HDD_day = max(0, T_limit - T_ambient_day)} +#' changes to a curved function that is above zero even if the mean of \code{T_ambient_day} +#' is above the mean of \code{T_limit}. +#' +#' @param tlow lower temperature boundary +#' @param tup upper temperature boundary +#' @param tlim named list of limit temperature sequences for \code{HDD} and \code{CDD} +#' @param tambStd std of ambient temperature +#' @param tlimStd std of limit temperature +#' +#' @return data frame of HDD/CDD values +#' +#' @importFrom stats dnorm +#' @importFrom pracma integral2 + +calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd=5, tlimStd=5) { + + # t1 : ambient temperature variable + # t2 : limit temperature variable + + # HDD + heatingFactor <- function(t2, t1, tamb, tambStd, tlim, tlimStd) { + h <- dnorm(t2, mean=tlim, sd=tlimStd) * dnorm(t1, mean=tamb, sd=tambStd) * (t2 - t1) + return(h) + } + + # CDD + coolingFactor <- function(t2, t1, tamb, tambStd, tlim, tlimStd) { + h <- dnorm(t2, mean=tlim, sd=tlimStd) * dnorm(t1, mean=tamb, sd=tambStd) * (t1 - t2) + return(h) + } + + # check if ambient/limit temperature interval is reasonable + # e.g. t_lim = 17C and t_amb = -50C wouldn't give reasonable CDD + checkTDif <- function(tamb, tlim, typeDD, tambStd, tlimStd) { + check <- TRUE + stdDif <- tambStd + tlimStd + if (typeDD == "HDD") { + if (tamb - tlim > stdDif) { + check <- FALSE + } + } + else if (typeDD == "CDD") { + if (tlim - tamb > 2*stdDif) { + check <- FALSE + } + } + return(check) + } + + t <- seq(tlow, tup, .1) + + hddcddFactors <- do.call( + "rbind", lapply( + c("HDD", "CDD"), function(typeDD) { + do.call( + "rbind", lapply( + t, function(tamb) { + do.call( + "rbind", lapply( + tlim[[typeDD]], function(.tlim) { + if (!checkTDif(tamb, .tlim, typeDD, tambStd, tlimStd)) { + tmp <- data.frame("T_amb" = tamb, + "T_amb_K" = round(tamb + 273.15, 1), + "T_lim" = .tlim, + "factor" = 0, + "factor_err" = 0, + "typeDD" = typeDD) + } + else { + + # integration boundaries + x1 <- .tlim - 4*tlimStd + x2 <- .tlim + 4*tlimStd + y1 <- min(.tlim - 3*tlimStd, tamb - 3*tlimStd) + y2 <- max(.tlim + 3*tlimStd, tamb + 3*tlimStd) + + if (typeDD == "HDD") { + f <- integral2(heatingFactor, + xmin = x1, + xmax = x2, + ymin = y1, + ymax = function(x){x}, + tamb = tamb, + tambStd = tambStd, + tlim = .tlim, + tlimStd = tlimStd, + reltol = 1e-1 + ) + } + else { + f <- integral2(coolingFactor, + xmin = x1, + xmax = x2, + ymin = function(x){x}, + ymax = y2, + tamb = tamb, + tambStd = tambStd, + tlim = .tlim, + tlimStd = tlimStd, + reltol = 1e-1) + } + tmp <- data.frame("T_amb" = tamb, + "T_amb_K" = round(tamb + 273.15, 1), + "T_lim" = .tlim, + "factor" = f$Q, + "factor_err" = f$error, + "typeDD" = typeDD) + } + } + ) + ) + } + ) + ) + } + ) + ) + return(hddcddFactors) +} + + +#' Assign HDD/CDD values for given ambient/limit temperature +#' +#' @param temp raster data containing temperature/BAIT values +#' @param typeDD type of degree day +#' @param tlim limit temperature +#' @param factors data frame with degree day values for \code{temp/tlim} combination +#' +#' @return raster object with HDD/CDD values +#' +#' @importFrom terra classify tapp + +calcCellHDDCDD <- function(temp, typeDD, tlim, factors) { + # extract years + dates <- names(temp) + + # add tolerance of 0.04K to avoid machine precision errors + factors <- factors %>% + filter(.data[["typeDD"]] == typeDD, .data[["T_lim"]] == tlim) %>% + dplyr::reframe(from = .data[["T_amb_K"]] - 0.049, + to = .data[["T_amb_K"]] + 0.049, + becomes = .data[["factor"]]) %>% + data.matrix() + + # swap ambient temperature values with corresponding DD values + hddcdd <- classify(temp, factors) + + terra::time(hddcdd) <- as.Date(dates) + + # aggregate to yearly HDD/CDD [K.d/a] + hddcdd <- tapp(hddcdd, "years", fun = sum, na.rm = TRUE) + + names(hddcdd) <- gsub("_", "", names(hddcdd)) + return(hddcdd) +} + + +#' Aggregate cellular HDD/CDD values to country-wide average (population-weighted) +#' +#' @param data raster object containing HDD/CDD values +#' @param weight raster object containing aggregation weights +#' @param mask raster opbject defining (regional) aggregation boundaries +#' +#' @return data frame containing regionally meaned HDD/CDD values +#' +#' @importFrom terra subset + +aggCells <- function(data, weight, mask) { + yearsData <- names(data) + yearsWeight <- names(weight) + + if (!all(yearsData %in% yearsWeight)) { + stop("Time periods of raster file and aggregation weights do not match.") + } + + # loop: years in raster file r + hddcddAgg <- do.call( + "rbind", lapply( + yearsData, function(y) { + # mask data and weights to considered regions + regData <- subset(data, y) * subset(weight, y) * mask + regWeight <- subset(weight, y) * mask + + # aggregate regional data + regDataAgg <- terra::global(regData, "sum", na.rm = TRUE)$sum + regWeightAgg <- terra::global(regWeight, "sum", na.rm = TRUE)$sum + + # calculate weighted sum + weightedAgg <- regDataAgg / regWeightAgg + + tmp <- data.frame("region" = names(mask), + "period" = y, + "value" = round(weightedAgg, 3)) + + rm(regData, regWeight, regDataAgg, regWeightAgg, weightedAgg) + + rownames(tmp) <- c() + return(tmp) + } + ) + ) + return(hddcddAgg) +} + + +#' Calculate country-wise population-weighted HDD/CDD values +#' +#' This function calculates country-wise population-weighted HDD/CDD values for +#' raw ambient temperature or bias-adjusted internal temperature for a given set +#' of limit temperatures from raster data on (various) climate variables. +#' +#' For further processing, raster objects containing degree day data are written +#' for an interval of ten years. +#' +#' @param ftas file name of data on near-surface atmospherical temperature +#' @param tlim named list of limit temperature sequences for \code{HDD} and \code{CDD} +#' @param countries raster opbject defining (regional) aggregation boundaries +#' @param pop raster object containing population data +#' @param factors data frame with degree day values for \code{temp/tlim} combination +#' @param bait boolean, BAIT is used as ambient temperature +#' @param frsds file name of data on surface downdwelling shortwave radiation (optional) +#' @param fsfc file name of data on near-surface wind speed (optional) +#' @param fhuss file name of data on near-surface specific humidity (optional) +#' @param wBAIT named list containing BAIT weights (optional) +#' @param params raster object containing regression parameters from \code{calcBAITpars} (optional) +#' +#' @importFrom raster writeRaster +#' @importFrom stringr str_split + +calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, + frsds = NULL, + fsfc = NULL, + fhuss = NULL, + wBAIT = NULL, + params = NULL) { + + # read cellular temperature + temp <- readSource("ISIMIPbuildings", subtype = ftas, convert = TRUE) + + dates <- names(temp) + + # optional: transform raw temperature into BAIT + if (bait) { + # note: easier to do in [C] + tasData <- temp - 273.15 # [C] + + # read and prepare bait input data + baitInput <- prepBaitInput(frsds, fsfc, fhuss) %>% + checkDates(tasData) + + # calculate bait + temp <- calcBAIT(baitInput, tasData, weight = wBAIT, params = params) + + # convert back to [K] + temp <- temp + 273.15 # [K] + } + + temp <- terra::round(temp, digits = 1) + names(temp) <- dates + + print("Calculating HDD/CDDs per cell.") + + fSplit <- str_split(ftas, "_") %>% unlist() + + # loop: typeDD + hddcdd <- do.call( + "rbind", lapply( + c("HDD", "CDD"), function(typeDD) { + # loop: threshold temperatures + do.call( + "rbind", lapply( + tlim[[typeDD]], function(t) { + hddcddAgg <- calcCellHDDCDD(temp, typeDD, t, factors) + + # save intermediate results + decades <- c(as.numeric(gsub("y", "", names(hddcddAgg))) %% 10 == 0) + if (any(decades)) { + hddcdd_save <- hddcddAgg[[decades]] + + y <- names(hddcddAgg)[as.numeric(gsub("y", "", names(hddcddAgg))) %% 10 == 0] + + y <- gsub("y", "", y) %>% + paste(collapse = "-") + + rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) + + rname <- paste0(rname, if (bait) "_bait" else "", ".nc") + + + writeRaster(hddcdd_save, + paste0("/p/tmp/hagento/output/rasterdata/", rname), + overwrite = TRUE) + } + + hddcddAgg <- hddcddAgg %>% + aggCells(pop, countries) %>% + mutate("variable" = typeDD, + "tlim" = t) # [C] + + return(hddcddAgg) + } + ) + ) + } + ) + ) + rm(tasData, baitInput, temp) + + return(hddcdd) +} + From 97adce8d047d66be288f0e3751cdb6da216c263d Mon Sep 17 00:00:00 2001 From: hagento Date: Thu, 14 Mar 2024 20:45:19 +0000 Subject: [PATCH 05/10] incorporated PR comments and added single-year split feature --- R/calcHDDCDD.R | 42 +++++++++-------- R/convertISIMIPbuildings.R | 17 ++----- R/readISIMIPbuildings.R | 94 +++++++++++++++++++++----------------- 3 files changed, 79 insertions(+), 74 deletions(-) diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index 1baa975..2f2350c 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -145,7 +145,7 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { # READ-IN DATA---------------------------------------------------------------- # list of files that are processed - files <- toolGetMapping(mappingFile, type = "sectoral", where = "mappingfolder") %>% + files <- toolGetMapping(mappingFile, type = "sectoral", where = "mredgebuildings") %>% filter(variable != "") # cells -> country mask @@ -169,7 +169,11 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { convert = FALSE) if (bait) { - baitPars <- calcOutput("BAITpars", aggregate = FALSE, model = model) + baitPars <- calcOutput("BAITpars", + aggregate = FALSE, + model = model, + cacheDir = cacheDir) + names(baitPars) <- parNames } @@ -499,7 +503,10 @@ calcBAIT <- function(baitInput, tasData, weight=NULL, params=NULL) { print("calc bait") bait <- tasData + weight[["wRSDS"]]*s + weight[["wSFC"]]*w + weight[["wHUSS"]]*h*t + # smooth bait bait <- smooth(bait, weight) + + # # blend bait bait <- blend(bait, tasData, weight) rm(baitInput, solar, wind, hum, s, w, h, t, bait) @@ -662,12 +669,15 @@ calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd=5, tlimStd=5) { #' @importFrom terra classify tapp calcCellHDDCDD <- function(temp, typeDD, tlim, factors) { + browser() # extract years dates <- names(temp) # add tolerance of 0.04K to avoid machine precision errors + factors <- factors[factors$typeDD == typeDD, ] + factors <- factors %>% - filter(.data[["typeDD"]] == typeDD, .data[["T_lim"]] == tlim) %>% + filter(.data[["T_lim"]] == tlim) %>% dplyr::reframe(from = .data[["T_amb_K"]] - 0.049, to = .data[["T_amb_K"]] + 0.049, becomes = .data[["factor"]]) %>% @@ -786,11 +796,13 @@ calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, temp <- temp + 273.15 # [K] } + # round and assign dates temp <- terra::round(temp, digits = 1) names(temp) <- dates print("Calculating HDD/CDDs per cell.") + fSplit <- str_split(ftas, "_") %>% unlist() # loop: typeDD @@ -801,27 +813,19 @@ calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, do.call( "rbind", lapply( tlim[[typeDD]], function(t) { + browser() hddcddAgg <- calcCellHDDCDD(temp, typeDD, t, factors) - # save intermediate results - decades <- c(as.numeric(gsub("y", "", names(hddcddAgg))) %% 10 == 0) - if (any(decades)) { - hddcdd_save <- hddcddAgg[[decades]] - - y <- names(hddcddAgg)[as.numeric(gsub("y", "", names(hddcddAgg))) %% 10 == 0] - - y <- gsub("y", "", y) %>% - paste(collapse = "-") - - rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) + # write raster files + y <- names(hddcddAgg) - rname <- paste0(rname, if (bait) "_bait" else "", ".nc") + rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) + rname <- paste0(rname, if (bait) "_bait" else "", ".nc") - writeRaster(hddcdd_save, - paste0("/p/tmp/hagento/output/rasterdata/", rname), - overwrite = TRUE) - } + terra::writeCDF(hddcddAgg, + file.path("/p/tmp/hagento/output/rasterdata", fSplit[[1]], rname), + overwrite = TRUE) hddcddAgg <- hddcddAgg %>% aggCells(pop, countries) %>% diff --git a/R/convertISIMIPbuildings.R b/R/convertISIMIPbuildings.R index 78f3c9e..60816cc 100644 --- a/R/convertISIMIPbuildings.R +++ b/R/convertISIMIPbuildings.R @@ -16,17 +16,7 @@ convertISIMIPbuildings <- function(x, subtype) { # fill dates for unnamed data fillDates <- function(r, filename, pop = FALSE) { - if (grepl("_A.nc|_A.nc4", filename)) { - yStart <- stringr::str_sub(filename, -14, -11) - } - else if (grepl("_B.nc|_B.nc4", filename)) { - yEnd <- stringr::str_sub(filename, -9, -6) - yStart <- as.character(as.numeric(yEnd) - 4) - } - else { - yStart <- stringr::str_sub(filename, -12, -9) - } - + yStart <- stringr::str_sub(filename, -12, -9) n <- terra::nlyr(r) if (!pop) { @@ -61,9 +51,12 @@ convertISIMIPbuildings <- function(x, subtype) { if (grepl(paste(edgeVars, collapse = "|"), subtype)) { var <- edgeVars[str_detect(subtype, edgeVars)] + if (var == "population") { x <- fillDates(x, subtype, pop = TRUE) - } else { + } + # dates have specific length of n = 10 + else if (!all(nchar(names(x)) == 10)) { x <- fillDates(x, subtype) } diff --git a/R/readISIMIPbuildings.R b/R/readISIMIPbuildings.R index b7e320e..dedd4ce 100644 --- a/R/readISIMIPbuildings.R +++ b/R/readISIMIPbuildings.R @@ -1,5 +1,12 @@ #' Read relevant ISIMIP data for mredgebuildings #' +#' Relevant data such as region masks, population and relevant climate data are +#' read in. The relevant file is declared in the subtype with the full file name. +#' +#' If the file name includes a suffix in the form of an integer such as +#' \code{_.filetype}, the file is split into a single year period, e.g. +#' \code{_2001_2010_2.nc} will return data for the second year of the +#' 2001-2010 period, here 2002. #' #' @param subtype filename #' @@ -9,12 +16,6 @@ #' @importFrom raster brick dropLayer res extent aggregate #' #' @note -#' Argument subtype consist of full filenames of the respective raster files. In -#' order to improve runtime and general problems that come with processing large -#' raster files, filenames marked with "_A" or "_B" before the filetype declaration, -#' will be split in the respective first and second half of the dataset. -#' -#' @note #' folder structure in inputdata/sources/ISIMIPbuildings is expected to be: #' country masks : var/ #' population : var/scenario @@ -55,24 +56,24 @@ readISIMIPbuildings <- function(subtype) { vars[["scenario"]] <- subSplit[[4]] vars[["model"]] <- subSplit[[1]] - if (grepl("A", tail(subSplit, 1))) { - vars[["yStart"]] <- subSplit[[length(subSplit) - 2]] - - yEnd <- as.numeric(vars[["yStart"]]) + 4 # 5 year period - vars[["yEnd"]] <- as.character(yEnd) + # raster data will be split into individual years + if (length(subSplit) > 9) { + # split index defines the year + vars[["idx"]] <- gsub(".nc", "", tail(subSplit, 1)) %>% + as.numeric() - vars[["subtype"]] <- sub("_A.nc", ".nc", subtype) - } - - else if (grepl("B", tail(subSplit, 1))) { - vars[["yEnd"]] <- subSplit[[length(subSplit) - 1]] + # temporal range of data + vars[["yStart"]] <- subSplit[[8]] + vars[["yEnd"]] <- subSplit[[9]] - yStart <- as.numeric(vars[["yEnd"]]) - 4 # 5 year period - vars[["yStart"]] <- as.character(yStart) + # year of interest + vars[["year"]] <- seq(vars[["yStart"]] %>% + as.numeric(), + vars[["yEnd"]] %>% + as.numeric())[[vars[["idx"]]]] %>% + as.character() - vars[["yStartFile"]] <- subSplit[[length(subSplit) - 2]] - - vars[["subtype"]] <- sub("_B.nc", ".nc", subtype) + vars[["subtype"]] <- sub("_(1[0-9]|\\d)\\.nc$", ".nc", subtype) } } @@ -83,28 +84,29 @@ readISIMIPbuildings <- function(subtype) { # determine period range of subset - getIdxRange <- function(yStart, yEnd, vars) { - dStart <- as.Date(paste0(yStart, "-01-01")) - dEnd <- as.Date(paste0(yEnd, "-12-31")) - dRange <- seq.Date(from = dStart, to = dEnd, by = "day") - - if (is.null(vars[["yStartFile"]])) { - idx <- seq(1:length(dRange)) - } - else { - dStartFile <- as.Date(paste0(vars[["yStartFile"]], "-01-01")) - dRangeFile <- seq.Date(from = dStartFile, to = dEnd, by = "day") - idx <- seq(length(dRangeFile) - length(dRange) + 1, length(dRangeFile)) - } - - return(idx) + getRanges <- function(vars) { + # total temporal range + dRange <- seq.Date(from = as.Date(paste0(vars[["yStart"]], "-01-01")), + to = as.Date(paste0(vars[["yEnd"]], "-12-31")), + by = "day") + + # temporal range of interest + yRange <- seq.Date(from = as.Date(paste0(vars[["year"]], "-01-01")), + to = as.Date(paste0(vars[["year"]], "-12-31")), + by = "day") + + # indices of range of interest + idxRange <- match(yRange, dRange) + + return(list("yRange" = yRange, + "idxRange" = idxRange)) } # PROCESS DATA---------------------------------------------------------------- - vars <- splitSubtype(subtype) + # region mask if (vars[["variable"]] == "countrymask"){ fpath <- file.path("countrymasks", subtype) varNames <- names(ncdf4::nc_open(fpath)[["var"]]) @@ -119,6 +121,7 @@ readISIMIPbuildings <- function(subtype) { } + # population else if (vars[["variable"]] == "population") { fpath <- file.path(vars[["variable"]], vars[["scenario"]], subtype) @@ -133,10 +136,10 @@ readISIMIPbuildings <- function(subtype) { # rename years years <- tail(strsplit(subtype, "_")[[1]], 2) - names(r) <- paste0("y", years[1]:years[2]) + names(r) <- years[1]:years[2] # filter relevant years - r <- terra::subset(r, as.numeric(substr(names(r), 2, 5)) > firstHistYear) + r <- terra::subset(r, as.numeric(names(r)) >= firstHistYear) # aggregate to common resolution of 0.5 deg if (any(raster::res(r) != 0.5)) { @@ -150,13 +153,18 @@ readISIMIPbuildings <- function(subtype) { } + # climate data else if (any(vars[["variable"]] %in% baitVars)) { - + # slice single years if (!is.null(vars[["yStart"]])) { - fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], vars[["subtype"]]) - idx <- getIdxRange(vars[["yStart"]], vars[["yEnd"]], vars) - r <- suppressWarnings(terra::rast(fpath, lyrs = idx)) + fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], vars[["subtype"]]) + ranges <- getRanges(vars) + + r <- suppressWarnings(terra::rast(fpath, lyrs = ranges[["idxRange"]])) + names(r) <- ranges[["yRange"]] } + + # full data set else { fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], subtype) r <- suppressWarnings(terra::rast(fpath)) From b940ac683124818513a194dad96df770d401a932 Mon Sep 17 00:00:00 2001 From: hagento Date: Fri, 15 Mar 2024 09:10:24 +0000 Subject: [PATCH 06/10] lucode style adaptions --- NAMESPACE | 26 +- R/calcBAITpars.R | 16 +- R/calcHDDCDD.R | 248 ++++++++++-------- R/convertISIMIPbuildings.R | 13 +- R/readISIMIPbuildings.R | 39 +-- .../filemappings/start_GFDL-ESM4_picontrol.R | 46 ---- .../start_IPSL-CM6A-LR_picontrol.R | 46 ---- .../start_MPI-ESM1-2-HR_picontrol.R | 46 ---- .../filemappings/start_MRI-ESM2-0_picontrol.R | 46 ---- .../start_UKESM1-0-LL_picontrol.R | 46 ---- 10 files changed, 190 insertions(+), 382 deletions(-) delete mode 100644 inst/extdata/sectoral/filemappings/start_GFDL-ESM4_picontrol.R delete mode 100644 inst/extdata/sectoral/filemappings/start_IPSL-CM6A-LR_picontrol.R delete mode 100644 inst/extdata/sectoral/filemappings/start_MPI-ESM1-2-HR_picontrol.R delete mode 100644 inst/extdata/sectoral/filemappings/start_MRI-ESM2-0_picontrol.R delete mode 100644 inst/extdata/sectoral/filemappings/start_UKESM1-0-LL_picontrol.R diff --git a/NAMESPACE b/NAMESPACE index 2fb3c2b..d73b747 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -7,7 +7,6 @@ export(calcCostDemolition) export(calcCostRenovation) export(calcFEdemandBuildings) export(calcFloorspacePast) -export(calcHDDCDD) export(calcHeatingCapacity) export(calcHeatingSystem) export(calcHouseholdSize) @@ -35,6 +34,7 @@ export(convertGDL) export(convertHDDCDD) export(convertOdyssee) export(convertUNHouseholds) +export(convertWEO) export(fullEDGEBUILDINGS) export(getFEbyEUEC) export(readCensusHub) @@ -53,6 +53,7 @@ export(readHotmaps) export(readIEAfloorspace) export(readOdyssee) export(readUNHouseholds) +export(readWEO) export(toolCountryFillAvg) export(toolDisaggregate) export(toolUnitConversion) @@ -111,16 +112,15 @@ importFrom(magclass,dimSums) importFrom(magclass,getItems) importFrom(magclass,getNames) importFrom(magclass,getSets) -importFrom(magclass,getYears) -importFrom(magclass,lowpass) importFrom(magclass,mbind) importFrom(magclass,mselect) importFrom(magclass,setNames) -importFrom(magclass,setYears) importFrom(magclass,time_interpolate) importFrom(openxlsx,read.xlsx) importFrom(plyr,revalue) +importFrom(pracma,integral2) importFrom(quadprog,solve.QP) +importFrom(quitte,aggregate_map) importFrom(quitte,as.quitte) importFrom(quitte,calc_addVariable) importFrom(quitte,getPeriods) @@ -130,14 +130,32 @@ importFrom(quitte,interpolate_missing_periods) importFrom(quitte,removeColNa) importFrom(quitte,replace_column) importFrom(quitte,revalue.levels) +importFrom(raster,writeRaster) importFrom(readxl,read_xlsx) +importFrom(rlang,.data) importFrom(stats,coef) +importFrom(stats,dnorm) importFrom(stats,lm) importFrom(stats,median) importFrom(stats,na.omit) importFrom(stats,nls) importFrom(stats,predict) importFrom(stats,pweibull) +importFrom(stringr,str_detect) +importFrom(stringr,str_split) +importFrom(stringr,str_sub) +importFrom(terra,aggregate) +importFrom(terra,classify) +importFrom(terra,ext) +importFrom(terra,nlyr) +importFrom(terra,rast) +importFrom(terra,regress) +importFrom(terra,res) +importFrom(terra,setGDALconfig) +importFrom(terra,subset) +importFrom(terra,tapp) +importFrom(terra,writeCDF) +importFrom(tidyr,"%>%") importFrom(tidyr,complete) importFrom(tidyr,gather) importFrom(tidyr,pivot_longer) diff --git a/R/calcBAITpars.R b/R/calcBAITpars.R index ffab327..8e613da 100644 --- a/R/calcBAITpars.R +++ b/R/calcBAITpars.R @@ -16,8 +16,9 @@ #' @author Hagen Tockhorn #' #' @importFrom terra regress rast -#' @importFrom quitte removeColNa +#' @importFrom madrat toolGetMapping #' @importFrom magclass as.magpie +#' @importFrom madrat readSource calcBAITpars <- function(model = "GFDL-ESM4") { @@ -30,10 +31,14 @@ calcBAITpars <- function(model = "GFDL-ESM4") { vars <- unique(files$variable) data <- sapply(vars, function(v) { - tmp <- sapply(files[files$variable == v,]$file, function(f) { - return(readSource("ISIMIPbuildings", subtype = f))}, - USE.NAMES = FALSE) %>% + tmp <- sapply(files[files$variable == v, ]$file, + function(f) { + return(readSource("ISIMIPbuildings", subtype = f)) + }, + USE.NAMES = FALSE) %>% rast() + + return(tmp) }, USE.NAMES = TRUE) @@ -70,7 +75,4 @@ calcBAITpars <- function(model = "GFDL-ESM4") { class = "SpatRaster", unit = "(unit)", description = "Regression parameters for calcHDDCDD")) - } - - diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index 2f2350c..c1328b7 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -7,6 +7,8 @@ #' @param mappingFile file name of sectoral mapping containing input data file names and directories #' @param bait boolean, use BAIT instead of raw temperature #' @param multiscen boolean, does \code{mappingFile} cover more than one scenario? +#' @param rasDir absolute path to directory for saving raster files +#' @param cacheDir absolute path to directory for pre-calculated BAIT regression parameters #' #' @return magpie object of heating and cooling degree days #' @@ -24,44 +26,47 @@ #' @importFrom terra setGDALconfig -calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { +calcHDDCDD <- function(mappingFile, + bait = FALSE, + multiscen = FALSE, + rasDir = NULL, + cacheDir = NULL) { # initialize full calculation - makeCalculations <- function(f, m, n, t_lim, countries, pop, hddcddFactor, - bait, wBAIT = NULL, params = NULL, suffix = NULL) { + makeCalculations <- function(f, m, n, tLim, countries, pop, hddcddFactor, + bait, wBAIT = NULL, params = NULL, suffix = NULL, + rasDir = NULL) { if (!is.null(suffix)) { ftas <- gsub(".nc", paste0("_", suffix, ".nc"), - f[f$variable == "tas" & f$gcm == m,][[n, "file"]]) + f[f$variable == "tas" & f$gcm == m, ][[n, "file"]]) - if (bait){ + if (bait) { frsds <- gsub(".nc", paste0("_", suffix, ".nc"), - f[f$variable == "rsds" & f$gcm == m,][[n, "file"]]) + f[f$variable == "rsds" & f$gcm == m, ][[n, "file"]]) fsfc <- gsub(".nc", paste0("_", suffix, ".nc"), - f[f$variable == "sfcwind" & f$gcm == m,][[n, "file"]]) + f[f$variable == "sfcwind" & f$gcm == m, ][[n, "file"]]) fhuss <- gsub(".nc", paste0("_", suffix, ".nc"), - f[f$variable == "huss" & f$gcm == m,][[n, "file"]]) + f[f$variable == "huss" & f$gcm == m, ][[n, "file"]]) } - } - - else { - ftas <- f[f$variable == "tas" & f$gcm == m,][[n, "file"]] - frsds <- f[f$variable == "rsds" & f$gcm == m,][[n, "file"]] - fsfc <- f[f$variable == "sfcwind" & f$gcm == m,][[n, "file"]] - fhuss <- f[f$variable == "huss" & f$gcm == m,][[n, "file"]] + } else { + ftas <- f[f$variable == "tas" & f$gcm == m, ][[n, "file"]] + frsds <- f[f$variable == "rsds" & f$gcm == m, ][[n, "file"]] + fsfc <- f[f$variable == "sfcwind" & f$gcm == m, ][[n, "file"]] + fhuss <- f[f$variable == "huss" & f$gcm == m, ][[n, "file"]] } print(paste("Processing temperature file:", ftas)) - if(bait) { + if (bait) { hddcddCell <- calcStackHDDCDD(ftas, - t_lim, + tLim, countries, pop, hddcddFactor, @@ -70,15 +75,17 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { fsfc = fsfc, fhuss = fhuss, wBAIT = wBAIT, - params = params)} - - else { + params = params, + rasDir = rasDir) + } else { hddcddCell <- calcStackHDDCDD(ftas, - t_lim, + tLim, countries, pop, hddcddFactor, - bait)} + bait, + rasDir = rasDir) + } return(hddcddCell) } @@ -146,7 +153,7 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { # list of files that are processed files <- toolGetMapping(mappingFile, type = "sectoral", where = "mredgebuildings") %>% - filter(variable != "") + filter(.data[["variable"]] != "") # cells -> country mask fCM <- file.path(files[files$variable == "CountryMask", "file"]) @@ -164,7 +171,7 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { model <- unique(files$gcm[files$variable == "tas"]) # read population data - fpop <- files %>% filter(variable == "pop") + fpop <- files %>% filter(.data[["variable"]] == "pop") pop <- readSource("ISIMIPbuildings", subtype = fpop$file, convert = FALSE) @@ -180,9 +187,9 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { hddcdd <- do.call( # file iteration "rbind", lapply( - seq(nrow(filter(files, files$variable == "tas"))), + seq_len(nrow(filter(files, files$variable == "tas"))), function(n) { - split <- files[files$variable == "tas" & files$gcm == model,][[n, "split"]] + split <- files[files$variable == "tas" & files$gcm == model, ][[n, "split"]] # split large raster files to save memory / speed up processing if (as.logical(split)) { @@ -192,32 +199,31 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { tmp <- makeCalculations(f = files, m = model, n = n, - t_lim = t_lim, + tLim = tLim, countries = countries, pop = pop, hddcddFactor = hddcddFactor, bait = bait, wBAIT = wBAIT, params = baitPars, - suffix = suffix) + suffix = suffix, + rasDir = rasDir) return(tmp) } ) ) - } - - # smaller raster files - else { + } else { hddcddCell <- makeCalculations(f = files, m = model, n = n, - t_lim = t_lim, + tLim = tLim, countries = countries, pop = pop, hddcddFactor = hddcddFactor, bait = bait, wBAIT = wBAIT, - params = baitPars) + params = baitPars, + rasDir = rasDir) } hddcddCell <- hddcddCell %>% @@ -266,11 +272,10 @@ calcHDDCDD <- function(mappingFile, bait=FALSE, multiscen = FALSE) { #' #' @return baitInput with congruent time periods w.r.t. tasData #' -#' @importFrom stringr substr #' @importFrom terra rast checkDates <- function(baitInput, tasData) { - dates_t <- names(tasData) + datesT <- names(tasData) baitInput <- sapply(names(baitInput), function(var) { # fill missing data with means from previous years @@ -280,12 +285,12 @@ checkDates <- function(baitInput, tasData) { tmp <- baitInput[[var]] - dates_b <- names(tmp) + datesBait <- names(tmp) - datesFill <- setdiff(dates_t, dates_b) # dates to fill up + datesFill <- setdiff(datesT, datesBait) # dates to fill up daysFill <- unique(substr(datesFill, 6, 11)) - datesKeep <- intersect(dates_b, dates_t) # dates to keep + datesKeep <- intersect(datesBait, datesT) # dates to keep keep <- length(datesKeep) > 0 if (keep) { @@ -297,20 +302,24 @@ checkDates <- function(baitInput, tasData) { baitInputMean <- prepBaitInput(mean = TRUE, baitInput = baitInput) # fill up missing dates with yearly-average value for specific day/cell - baitInputFill <- rast(lapply( - daysFill, function(d){ - idx <- which(grepl(d, stringr::str_sub(datesFill, -5, -1))) - r <- rast(replicate(length(idx), - baitInputMean[[var]][[d]])) - names(r) <- datesFill[idx] - return(r) - } - ) + baitInputFill <- rast( + lapply( + daysFill, + function(d) { + idx <- which(grepl(d, stringr::str_sub(datesFill, -5, -1))) + r <- rast(replicate(length(idx), baitInputMean[[var]][[d]])) + names(r) <- datesFill[idx] + return(r) + } + ) ) # concatenate data - if (keep) {tmp <- rast(list(tmp, baitInputFill))} - else {tmp <- baitInputFill} + if (keep) { + tmp <- rast(list(tmp, baitInputFill)) + } else { + tmp <- baitInputFill + } # re-order dates tmp <- rast(tmp[[order(names(tmp))]]) @@ -343,19 +352,26 @@ checkDates <- function(baitInput, tasData) { #' @importFrom terra tapp #' @importFrom madrat readSource -prepBaitInput <- function(frsds=NULL, fsfc=NULL, fhuss=NULL, baitInput=NULL, fillWithMean=FALSE) { +prepBaitInput <- function(frsds = NULL, + fsfc = NULL, + fhuss = NULL, + baitInput = NULL, + fillWithMean = FALSE) { + if (isTRUE(fillWithMean)) { # optional: calculate daily means over years to fill missing data baitInputMean <- sapply( - names(baitInput), function(var) { + names(baitInput), + function(var) { meanData <- tapp(baitInput[[var]], unique(substr(names(baitInput[[var]]), 6, 11)), fun = "mean") names(meanData) <- gsub("\\.", "-", substr(names(mean), 2, 6)) - return(meanData)}) + return(meanData) + } + ) return(baitInputMean) - } - else { + } else { input <- list( "rsds" = readSource("ISIMIPbuildings", subtype = frsds, convert = TRUE), "sfc" = readSource("ISIMIPbuildings", subtype = fsfc, convert = TRUE), @@ -374,7 +390,7 @@ prepBaitInput <- function(frsds=NULL, fsfc=NULL, fhuss=NULL, baitInput=NULL, fil #' atmospherical temperature. #' If no cell-resoluted parameters are given, the globally-meaned parameters from #' Staffel et. all 2023 are taken -#' (see \link{https://static-content.springer.com/esm/art%3A10.1038%2Fs41560-023-01341-5/MediaObjects/41560_2023_1341_MOESM1_ESM.pdf}) +#' (see \link{https://doi.org/10.1038/s41560-023-01341-5}) #' #' @param t raster data on near-surface atmospherical temperature #' @param type considered climate variable @@ -382,21 +398,25 @@ prepBaitInput <- function(frsds=NULL, fsfc=NULL, fhuss=NULL, baitInput=NULL, fil #' #' @return counterfactuals for respective climate variable -cfac <- function(t, type, params=NULL) { +cfac <- function(t, type, params = NULL) { + # nolint start if (is.null(params)) { params <- switch(type, s = c(100, 7), w = c(4.5, -0.025), h = c(1.1, 0.06), - t = c(16))} + t = c(16)) + } return(switch(type, s = {params[[1]] + params[[2]] * t}, w = {params[[1]] + params[[2]] * t}, h = {exp(params[[1]] + params[[2]] * t)}, t = {params[[1]]}, - error("No valid parameter type specified.") - )) + error("No valid parameter type specified.") #nolint + ) + ) + # nolint end } @@ -418,11 +438,11 @@ smooth <- function(r, weight) { r1D[[1]] <- 0 # two days indented - r2D <- r[[c(nlyr(r)-1, nlyr(r), 1:(nlyr(r) - 2))]] + r2D <- r[[c(nlyr(r) - 1, nlyr(r), 1:(nlyr(r) - 2))]] r2D[[1:2]] <- 0 # smooth - rSmooth <- (r + weight[["sig"]]*r1D + weight[["sig"]]**2 * r2D) / (1 + weight[["sig"]] + weight[["sig"]]**2) + rSmooth <- (r + weight[["sig"]] * r1D + weight[["sig"]]**2 * r2D) / (1 + weight[["sig"]] + weight[["sig"]]**2) return(rSmooth) } @@ -445,8 +465,9 @@ smooth <- function(r, weight) { blend <- function(bait, tas, weight) { print("blend") - bBar <- (tas - 0.5*(weight[["bUpper"]] + weight[["bLower"]])) * 10 / (weight[["bUpper"]] - weight[["bLower"]]) - b <- weight[["bMax"]] / (1 + exp(-bBar)) + + bBar <- (tas - 0.5 * (weight[["bUpper"]] + weight[["bLower"]])) * 10 / (weight[["bUpper"]] - weight[["bLower"]]) + b <- weight[["bMax"]] / (1 + exp(-bBar)) blend <- bait * (1 - b) + (tas * b) return(blend) @@ -473,35 +494,34 @@ blend <- function(bait, tas, weight) { #' #' @return raster object with BAIT values -calcBAIT <- function(baitInput, tasData, weight=NULL, params=NULL) { +calcBAIT <- function(baitInput, tasData, weight = NULL, params = NULL) { if (is.null(weight)) { warning("Please give appropriate weights for the calculation of BAIT.") - weight <- list("wRSDS" = wRSDS, - "wSFC" = wSFC, - "wHUSS" = wHUSS, - "sig" = sig, - "bLower" = bLower, - "bUpper" = bUpper, - "bMax" = bMax)} - - dates <- names(tasData) + weight <- list("wRSDS" = 0.012, + "wSFC" = -0.20, + "wHUSS" = 0.05, + "sig" = 0.5, + "bLower" = 15, + "bUpper" = 23, + "bMax" = 0.5) + } solar <- baitInput$rsds wind <- baitInput$sfc hum <- baitInput$huss print("calc s") - s <- solar - cfac(tasData, type="s", params = c(params[["aRSDS"]], params[["bRSDS"]])) + s <- solar - cfac(tasData, type = "s", params = c(params[["aRSDS"]], params[["bRSDS"]])) print("calc w") - w <- wind - cfac(tasData, type="w", params = c(params[["aSFC"]], params[["bSFC"]])) + w <- wind - cfac(tasData, type = "w", params = c(params[["aSFC"]], params[["bSFC"]])) print("print h") - h <- hum - cfac(tasData, type="h", params = c(params[["aHUSS"]], params[["bHUSS"]])) + h <- hum - cfac(tasData, type = "h", params = c(params[["aHUSS"]], params[["bHUSS"]])) print("calc t") - t <- tasData - cfac(tasData, type="t", params = NULL) + t <- tasData - cfac(tasData, type = "t", params = NULL) # calc bait print("calc bait") - bait <- tasData + weight[["wRSDS"]]*s + weight[["wSFC"]]*w + weight[["wHUSS"]]*h*t + bait <- tasData + weight[["wRSDS"]] * s + weight[["wSFC"]] * w + weight[["wHUSS"]] * h * t # smooth bait bait <- smooth(bait, weight) @@ -531,13 +551,13 @@ calcBAIT <- function(baitInput, tasData, weight=NULL, params=NULL) { #' To account for heterogenity in heating/cooling behavior, the ambient and limit #' temperature, \code{tamb} and \code{tlim}, are assumed to be normally distributed. #' This changes the calculation of a degree day to a double integration of -#' \code{T_limit - T_ambient_day} with integration boundaries set at 3 standard +#' \code{tLimit - T_ambient_day} with integration boundaries set at 3 standard #' deviations, \code{tambStd} and \code{tlimStd}, from \code{tamb} and \code{tlim} #' respectively. #' -#' As consequence, the ramp function of \code{HDD_day = max(0, T_limit - T_ambient_day)} +#' As consequence, the ramp function of \code{HDD_day = max(0, tLimit - T_ambient_day)} #' changes to a curved function that is above zero even if the mean of \code{T_ambient_day} -#' is above the mean of \code{T_limit}. +#' is above the mean of \code{tLimit}. #' #' @param tlow lower temperature boundary #' @param tup upper temperature boundary @@ -550,25 +570,25 @@ calcBAIT <- function(baitInput, tasData, weight=NULL, params=NULL) { #' @importFrom stats dnorm #' @importFrom pracma integral2 -calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd=5, tlimStd=5) { +calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd = 5, tlimStd = 5) { # t1 : ambient temperature variable # t2 : limit temperature variable # HDD heatingFactor <- function(t2, t1, tamb, tambStd, tlim, tlimStd) { - h <- dnorm(t2, mean=tlim, sd=tlimStd) * dnorm(t1, mean=tamb, sd=tambStd) * (t2 - t1) + h <- dnorm(t2, mean = tlim, sd = tlimStd) * dnorm(t1, mean = tamb, sd = tambStd) * (t2 - t1) return(h) } # CDD coolingFactor <- function(t2, t1, tamb, tambStd, tlim, tlimStd) { - h <- dnorm(t2, mean=tlim, sd=tlimStd) * dnorm(t1, mean=tamb, sd=tambStd) * (t1 - t2) + h <- dnorm(t2, mean = tlim, sd = tlimStd) * dnorm(t1, mean = tamb, sd = tambStd) * (t1 - t2) return(h) } # check if ambient/limit temperature interval is reasonable - # e.g. t_lim = 17C and t_amb = -50C wouldn't give reasonable CDD + # e.g. tLim = 17C and t_amb = -50C wouldn't give reasonable CDD checkTDif <- function(tamb, tlim, typeDD, tambStd, tlimStd) { check <- TRUE stdDif <- tambStd + tlimStd @@ -576,9 +596,8 @@ calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd=5, tlimStd=5) { if (tamb - tlim > stdDif) { check <- FALSE } - } - else if (typeDD == "CDD") { - if (tlim - tamb > 2*stdDif) { + } else if (typeDD == "CDD") { + if (tlim - tamb > 2 * stdDif) { check <- FALSE } } @@ -599,37 +618,34 @@ calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd=5, tlimStd=5) { if (!checkTDif(tamb, .tlim, typeDD, tambStd, tlimStd)) { tmp <- data.frame("T_amb" = tamb, "T_amb_K" = round(tamb + 273.15, 1), - "T_lim" = .tlim, + "tLim" = .tlim, "factor" = 0, "factor_err" = 0, "typeDD" = typeDD) - } - else { + } else { # integration boundaries - x1 <- .tlim - 4*tlimStd - x2 <- .tlim + 4*tlimStd - y1 <- min(.tlim - 3*tlimStd, tamb - 3*tlimStd) - y2 <- max(.tlim + 3*tlimStd, tamb + 3*tlimStd) + x1 <- .tlim - 4 * tlimStd + x2 <- .tlim + 4 * tlimStd + y1 <- min(.tlim - 3 * tlimStd, tamb - 3 * tlimStd) + y2 <- max(.tlim + 3 * tlimStd, tamb + 3 * tlimStd) if (typeDD == "HDD") { f <- integral2(heatingFactor, xmin = x1, xmax = x2, ymin = y1, - ymax = function(x){x}, + ymax = function(x) {x}, #nolint tamb = tamb, tambStd = tambStd, tlim = .tlim, tlimStd = tlimStd, - reltol = 1e-1 - ) - } - else { + reltol = 1e-1) + } else { f <- integral2(coolingFactor, xmin = x1, xmax = x2, - ymin = function(x){x}, + ymin = function(x) {x}, #nolint ymax = y2, tamb = tamb, tambStd = tambStd, @@ -639,11 +655,12 @@ calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd=5, tlimStd=5) { } tmp <- data.frame("T_amb" = tamb, "T_amb_K" = round(tamb + 273.15, 1), - "T_lim" = .tlim, + "tLim" = .tlim, "factor" = f$Q, "factor_err" = f$error, "typeDD" = typeDD) } + return(tmp) } ) ) @@ -669,7 +686,6 @@ calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd=5, tlimStd=5) { #' @importFrom terra classify tapp calcCellHDDCDD <- function(temp, typeDD, tlim, factors) { - browser() # extract years dates <- names(temp) @@ -677,7 +693,7 @@ calcCellHDDCDD <- function(temp, typeDD, tlim, factors) { factors <- factors[factors$typeDD == typeDD, ] factors <- factors %>% - filter(.data[["T_lim"]] == tlim) %>% + filter(.data[["tLim"]] == tlim) %>% dplyr::reframe(from = .data[["T_amb_K"]] - 0.049, to = .data[["T_amb_K"]] + 0.049, becomes = .data[["factor"]]) %>% @@ -767,13 +783,15 @@ aggCells <- function(data, weight, mask) { #' #' @importFrom raster writeRaster #' @importFrom stringr str_split +#' @importFrom terra writeCDF calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, frsds = NULL, fsfc = NULL, fhuss = NULL, wBAIT = NULL, - params = NULL) { + params = NULL, + rasDir = NULL) { # read cellular temperature temp <- readSource("ISIMIPbuildings", subtype = ftas, convert = TRUE) @@ -813,19 +831,19 @@ calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, do.call( "rbind", lapply( tlim[[typeDD]], function(t) { - browser() hddcddAgg <- calcCellHDDCDD(temp, typeDD, t, factors) # write raster files - y <- names(hddcddAgg) + if (!is.null(rasDir)) { + y <- names(hddcddAgg) - rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) + rname <- paste0(fSplit[[1]], "_", y, "_", fSplit[[4]], "_", typeDD, "_", t) + rname <- paste0(rname, if (bait) "_bait" else "", ".nc") - rname <- paste0(rname, if (bait) "_bait" else "", ".nc") - - terra::writeCDF(hddcddAgg, - file.path("/p/tmp/hagento/output/rasterdata", fSplit[[1]], rname), - overwrite = TRUE) + writeCDF(hddcddAgg, + file.path(rasDir, fSplit[[1]], rname), + overwrite = TRUE) + } hddcddAgg <- hddcddAgg %>% aggCells(pop, countries) %>% @@ -843,5 +861,3 @@ calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, return(hddcdd) } - - diff --git a/R/convertISIMIPbuildings.R b/R/convertISIMIPbuildings.R index 60816cc..ac4ba75 100644 --- a/R/convertISIMIPbuildings.R +++ b/R/convertISIMIPbuildings.R @@ -54,9 +54,8 @@ convertISIMIPbuildings <- function(x, subtype) { if (var == "population") { x <- fillDates(x, subtype, pop = TRUE) - } - # dates have specific length of n = 10 - else if (!all(nchar(names(x)) == 10)) { + } else if (!all(nchar(names(x)) == 10)) { + # dates have specific length of n = 10 x <- fillDates(x, subtype) } @@ -66,8 +65,8 @@ convertISIMIPbuildings <- function(x, subtype) { } } - return(list(x = x, - class = "SpatRaster", - unit = unitMapping[var], - cache = FALSE)) + return(list(x = x, + class = "SpatRaster", + unit = unitMapping[var], + cache = FALSE)) } diff --git a/R/readISIMIPbuildings.R b/R/readISIMIPbuildings.R index dedd4ce..62b4c57 100644 --- a/R/readISIMIPbuildings.R +++ b/R/readISIMIPbuildings.R @@ -13,7 +13,7 @@ #' @author Hagen Tockhorn #' #' @importFrom stringr str_split -#' @importFrom raster brick dropLayer res extent aggregate +#' @importFrom terra rast subset aggregate ext res #' #' @note #' folder structure in inputdata/sources/ISIMIPbuildings is expected to be: @@ -38,8 +38,9 @@ readISIMIPbuildings <- function(subtype) { splitSubtype <- function(subtype) { vars <- list() + # nolint start if (grepl("countrymask", subtype)) { - vars[["variable"]] = "countrymask" + vars[["variable"]] <- "countrymask" } else if (grepl("population", subtype)) { @@ -75,9 +76,10 @@ readISIMIPbuildings <- function(subtype) { vars[["subtype"]] <- sub("_(1[0-9]|\\d)\\.nc$", ".nc", subtype) } - } - - else {stop("Invalid subtype given.")} + } else { + stop("Invalid subtype given.") + } + # nolint end return(vars) } @@ -106,15 +108,16 @@ readISIMIPbuildings <- function(subtype) { # PROCESS DATA---------------------------------------------------------------- vars <- splitSubtype(subtype) + # nolint start # region mask - if (vars[["variable"]] == "countrymask"){ + if (vars[["variable"]] == "countrymask") { fpath <- file.path("countrymasks", subtype) varNames <- names(ncdf4::nc_open(fpath)[["var"]]) countries <- list() for (var in varNames) { - countries[[var]] <- suppressWarnings(terra::rast(fpath, subds = var)) + countries[[var]] <- suppressWarnings(rast(fpath, subds = var)) } - r <- terra::rast(countries) + r <- rast(countries) names(r) <- gsub("m_", "", varNames) x <- list(x = r, class = "SpatRaster") @@ -126,10 +129,10 @@ readISIMIPbuildings <- function(subtype) { fpath <- file.path(vars[["variable"]], vars[["scenario"]], subtype) if (vars[["scenario"]] == "picontrol") { - r <- suppressWarnings(terra::rast(fpath)) + r <- suppressWarnings(rast(fpath)) } else { - r <- suppressWarnings(terra::rast(fpath, subds = "total-population")) + r <- suppressWarnings(rast(fpath, subds = "total-population")) } subtype <- gsub(".nc", "", subtype) @@ -139,14 +142,13 @@ readISIMIPbuildings <- function(subtype) { names(r) <- years[1]:years[2] # filter relevant years - r <- terra::subset(r, as.numeric(names(r)) >= firstHistYear) + r <- subset(r, as.numeric(names(r)) >= firstHistYear) # aggregate to common resolution of 0.5 deg - if (any(raster::res(r) != 0.5)) { - r <- terra::aggregate(r, fun = "sum", - fact = round(0.5 / terra::res(r), 3)) - terra::res(r) <- 0.5 - terra::ext(r) <- round(terra::ext(r)) + if (any(res(r) != 0.5)) { + r <- aggregate(r, fun = "sum", fact = round(0.5 / res(r), 3)) + res(r) <- 0.5 + ext(r) <- round(ext(r)) } x <- list(x = r, class = "SpatRaster", cache = FALSE) @@ -160,20 +162,21 @@ readISIMIPbuildings <- function(subtype) { fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], vars[["subtype"]]) ranges <- getRanges(vars) - r <- suppressWarnings(terra::rast(fpath, lyrs = ranges[["idxRange"]])) + r <- suppressWarnings(rast(fpath, lyrs = ranges[["idxRange"]])) names(r) <- ranges[["yRange"]] } # full data set else { fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], subtype) - r <- suppressWarnings(terra::rast(fpath)) + r <- suppressWarnings(rast(fpath)) } x <- list(x = r, class = "SpatRaster", cache = FALSE) } else {stop("Subtype was incorrectly split or invalid subtype given.")} + # nolint end return(x) } diff --git a/inst/extdata/sectoral/filemappings/start_GFDL-ESM4_picontrol.R b/inst/extdata/sectoral/filemappings/start_GFDL-ESM4_picontrol.R deleted file mode 100644 index 022178c..0000000 --- a/inst/extdata/sectoral/filemappings/start_GFDL-ESM4_picontrol.R +++ /dev/null @@ -1,46 +0,0 @@ -library(mrremind) -library(mrcommons) - - -# load libraries -devtools::load_all("dev/mredgebuildings") -#library(mredgebuildings) - -setConfig(outputfolder = "/p/tmp/hagento/output", - mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", - ignorecache = c("ISIMIPbuildings")) - -#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") - -# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. -# Each list element is a named vector with: -# - the first element "regionmapping": region mapping used for the aggregation of the input data -# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping - -# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" - -# Current input data revision (.) #### -revision <- "0.1" # should be a number with two decimal places for production -cachetype <- "def" -dev <- "" -puc <- identical(dev, '') -renv <- FALSE - -sessionInfo() - - -# HISTORICAL - -f <- "GFDL-ESM4_picontrol.csv" - -# EDGE mapping -# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, -# dev = dev, cachetype = cachetype, puc = puc, renv = renv) - -dev <- paste0("_", gsub(".csv", "", f)) - -# ISO -retrieveData(model = "hddcdd", file = f, rev = revision, - dev = dev, cachetype = cachetype, puc = puc, renv = renv) - - diff --git a/inst/extdata/sectoral/filemappings/start_IPSL-CM6A-LR_picontrol.R b/inst/extdata/sectoral/filemappings/start_IPSL-CM6A-LR_picontrol.R deleted file mode 100644 index b2e57f5..0000000 --- a/inst/extdata/sectoral/filemappings/start_IPSL-CM6A-LR_picontrol.R +++ /dev/null @@ -1,46 +0,0 @@ -library(mrremind) -library(mrcommons) - - -# load libraries -devtools::load_all("dev/mredgebuildings") -#library(mredgebuildings) - -setConfig(outputfolder = "/p/tmp/hagento/output", - mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", - ignorecache = c("ISIMIPbuildings")) - -#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") - -# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. -# Each list element is a named vector with: -# - the first element "regionmapping": region mapping used for the aggregation of the input data -# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping - -# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" - -# Current input data revision (.) #### -revision <- "0.1" # should be a number with two decimal places for production -cachetype <- "def" -dev <- "" -puc <- identical(dev, '') -renv <- FALSE - -sessionInfo() - - -# HISTORICAL - -f <- "IPSL-CM6A-LR_picontrol.csv" - -# EDGE mapping -# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, -# dev = dev, cachetype = cachetype, puc = puc, renv = renv) - -dev <- paste0("_", gsub(".csv", "", f)) - -# ISO -retrieveData(model = "hddcdd", file = f, rev = revision, - dev = dev, cachetype = cachetype, puc = puc, renv = renv) - - diff --git a/inst/extdata/sectoral/filemappings/start_MPI-ESM1-2-HR_picontrol.R b/inst/extdata/sectoral/filemappings/start_MPI-ESM1-2-HR_picontrol.R deleted file mode 100644 index 91232d7..0000000 --- a/inst/extdata/sectoral/filemappings/start_MPI-ESM1-2-HR_picontrol.R +++ /dev/null @@ -1,46 +0,0 @@ -library(mrremind) -library(mrcommons) - - -# load libraries -devtools::load_all("dev/mredgebuildings") -#library(mredgebuildings) - -setConfig(outputfolder = "/p/tmp/hagento/output", - mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", - ignorecache = c("ISIMIPbuildings")) - -#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") - -# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. -# Each list element is a named vector with: -# - the first element "regionmapping": region mapping used for the aggregation of the input data -# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping - -# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" - -# Current input data revision (.) #### -revision <- "0.1" # should be a number with two decimal places for production -cachetype <- "def" -dev <- "" -puc <- identical(dev, '') -renv <- FALSE - -sessionInfo() - - -# HISTORICAL - -f <- "MPI-ESM1-2-HR_historical.csv" - -# EDGE mapping -# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, -# dev = dev, cachetype = cachetype, puc = puc, renv = renv) - -dev <- paste0("_", gsub(".csv", "", f)) - -# ISO -retrieveData(model = "hddcdd", file = f, rev = revision, - dev = dev, cachetype = cachetype, puc = puc, renv = renv) - - diff --git a/inst/extdata/sectoral/filemappings/start_MRI-ESM2-0_picontrol.R b/inst/extdata/sectoral/filemappings/start_MRI-ESM2-0_picontrol.R deleted file mode 100644 index c525d21..0000000 --- a/inst/extdata/sectoral/filemappings/start_MRI-ESM2-0_picontrol.R +++ /dev/null @@ -1,46 +0,0 @@ -library(mrremind) -library(mrcommons) - - -# load libraries -devtools::load_all("dev/mredgebuildings") -#library(mredgebuildings) - -setConfig(outputfolder = "/p/tmp/hagento/output", - mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", - ignorecache = c("ISIMIPbuildings")) - -#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") - -# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. -# Each list element is a named vector with: -# - the first element "regionmapping": region mapping used for the aggregation of the input data -# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping - -# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" - -# Current input data revision (.) #### -revision <- "0.1" # should be a number with two decimal places for production -cachetype <- "def" -dev <- "" -puc <- identical(dev, '') -renv <- FALSE - -sessionInfo() - - -# HISTORICAL - -f <- "MRI-ESM2-0_historical.csv" - -# EDGE mapping -# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, -# dev = dev, cachetype = cachetype, puc = puc, renv = renv) - -dev <- paste0("_", gsub(".csv", "", f)) - -# ISO -retrieveData(model = "hddcdd", file = f, rev = revision, - dev = dev, cachetype = cachetype, puc = puc, renv = renv) - - diff --git a/inst/extdata/sectoral/filemappings/start_UKESM1-0-LL_picontrol.R b/inst/extdata/sectoral/filemappings/start_UKESM1-0-LL_picontrol.R deleted file mode 100644 index b32d630..0000000 --- a/inst/extdata/sectoral/filemappings/start_UKESM1-0-LL_picontrol.R +++ /dev/null @@ -1,46 +0,0 @@ -library(mrremind) -library(mrcommons) - - -# load libraries -devtools::load_all("dev/mredgebuildings") -#library(mredgebuildings) - -setConfig(outputfolder = "/p/tmp/hagento/output", - mappingfolder = "/p/tmp/hagento/dev/mredgebuildings/inst", - ignorecache = c("ISIMIPbuildings")) - -#raster::rasterOptions(tmpdir = "/p/tmp/hagento/.Rtmp") - -# List with the names of the files containing the ISO-to-region mapping to which the data should be aggregated. -# Each list element is a named vector with: -# - the first element "regionmapping": region mapping used for the aggregation of the input data -# - the second element "extramappings_historic": mapping with regions to which only the historic data are aggregated in addition to the normal region mapping - -# regionmapping <- "regionmappingISO-EDGE_EUR_ETP.csv" - -# Current input data revision (.) #### -revision <- "0.1" # should be a number with two decimal places for production -cachetype <- "def" -dev <- "" -puc <- identical(dev, '') -renv <- FALSE - -sessionInfo() - - -# HISTORICAL - -f <- "UKESM1-0-LL_historical.csv" - -# EDGE mapping -# retrieveData(model = "HDDCDD", regionmapping = regionmapping, rev = revision, -# dev = dev, cachetype = cachetype, puc = puc, renv = renv) - -dev <- paste0("_", gsub(".csv", "", f)) - -# ISO -retrieveData(model = "hddcdd", file = f, rev = revision, - dev = dev, cachetype = cachetype, puc = puc, renv = renv) - - From 1aa5db7d6286f494be2be4b1198f70935520421b Mon Sep 17 00:00:00 2001 From: hagento Date: Fri, 15 Mar 2024 15:06:48 +0000 Subject: [PATCH 07/10] more lucode fixes --- DESCRIPTION | 6 ++++++ NAMESPACE | 4 +--- R/calcBAITpars.R | 6 +++++- R/calcHDDCDD.R | 20 ++++++++------------ R/readISIMIPbuildings.R | 3 ++- man/calcHDDCDD.Rd | 29 ++++++++++++++++++++++++----- 6 files changed, 46 insertions(+), 22 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index d76b7fb..ac07a7e 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -23,8 +23,14 @@ Imports: brick (>= 0.1.3), dplyr, GDPuc, + ncdf4, openxlsx, plyr, + pracma, + raster, + rlang, + stringr, + terra, quadprog, quitte, readxl, diff --git a/NAMESPACE b/NAMESPACE index d73b747..c5976cd 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -34,7 +34,6 @@ export(convertGDL) export(convertHDDCDD) export(convertOdyssee) export(convertUNHouseholds) -export(convertWEO) export(fullEDGEBUILDINGS) export(getFEbyEUEC) export(readCensusHub) @@ -53,7 +52,6 @@ export(readHotmaps) export(readIEAfloorspace) export(readOdyssee) export(readUNHouseholds) -export(readWEO) export(toolCountryFillAvg) export(toolDisaggregate) export(toolUnitConversion) @@ -116,11 +114,11 @@ importFrom(magclass,mbind) importFrom(magclass,mselect) importFrom(magclass,setNames) importFrom(magclass,time_interpolate) +importFrom(ncdf4,nc_open) importFrom(openxlsx,read.xlsx) importFrom(plyr,revalue) importFrom(pracma,integral2) importFrom(quadprog,solve.QP) -importFrom(quitte,aggregate_map) importFrom(quitte,as.quitte) importFrom(quitte,calc_addVariable) importFrom(quitte,getPeriods) diff --git a/R/calcBAITpars.R b/R/calcBAITpars.R index 8e613da..383aaaf 100644 --- a/R/calcBAITpars.R +++ b/R/calcBAITpars.R @@ -25,11 +25,12 @@ calcBAITpars <- function(model = "GFDL-ESM4") { # READ-IN DATA---------------------------------------------------------------- - files <- toolGetMapping("baitregression-files.csv", type = "sectoral") %>% + files <- toolGetMapping("baitregression-files_test.csv", type = "sectoral") %>% filter(.data[["gcm"]] == model) vars <- unique(files$variable) + # nolint start data <- sapply(vars, function(v) { tmp <- sapply(files[files$variable == v, ]$file, function(f) { @@ -41,6 +42,7 @@ calcBAITpars <- function(model = "GFDL-ESM4") { return(tmp) }, USE.NAMES = TRUE) + # nolint end print("Reading completed") @@ -55,6 +57,7 @@ calcBAITpars <- function(model = "GFDL-ESM4") { data$tas <- data$tas - 273.15 + # nolint start regPars <- sapply(vars[vars != "tas"], function(v) { x <- data[["tas"]] y <- data[[v]] @@ -66,6 +69,7 @@ calcBAITpars <- function(model = "GFDL-ESM4") { }, USE.NAMES = FALSE) %>% rast() + # nolint end diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index c1328b7..7ba63d6 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -14,10 +14,6 @@ #' #' @author Robin Hasse, Hagen Tockhorn #' -#' \dontrun{ -#' calcHDDCDD() -#' } -#' #' @importFrom madrat toolGetMapping readSource calcOutput toolCountryFill #' @importFrom tidyr %>% #' @importFrom dplyr mutate @@ -277,7 +273,7 @@ calcHDDCDD <- function(mappingFile, checkDates <- function(baitInput, tasData) { datesT <- names(tasData) - baitInput <- sapply(names(baitInput), function(var) { + baitInput <- sapply(names(baitInput), function(var) { #nolint # fill missing data with means from previous years # NOTE: "temp" and "baitInput" have the same global temporal lower # boundary, since "temp" is the constraining dataset, only @@ -299,7 +295,7 @@ checkDates <- function(baitInput, tasData) { } if (length(daysFill) > 0) { - baitInputMean <- prepBaitInput(mean = TRUE, baitInput = baitInput) + baitInputMean <- prepBaitInput(fillWithMean = TRUE, baitInput = baitInput) # fill up missing dates with yearly-average value for specific day/cell baitInputFill <- rast( @@ -360,7 +356,7 @@ prepBaitInput <- function(frsds = NULL, if (isTRUE(fillWithMean)) { # optional: calculate daily means over years to fill missing data - baitInputMean <- sapply( + baitInputMean <- sapply( #nolint names(baitInput), function(var) { meanData <- tapp(baitInput[[var]], @@ -372,11 +368,11 @@ prepBaitInput <- function(frsds = NULL, ) return(baitInputMean) } else { - input <- list( + input <- list( #nolint start "rsds" = readSource("ISIMIPbuildings", subtype = frsds, convert = TRUE), "sfc" = readSource("ISIMIPbuildings", subtype = fsfc, convert = TRUE), "huss" = readSource("ISIMIPbuildings", subtype = fhuss, convert = TRUE)) - return(input) + return(input) # nolint end } } @@ -389,8 +385,7 @@ prepBaitInput <- function(frsds = NULL, #' calcBAITpars where the respective variable is correlated with the near-surface #' atmospherical temperature. #' If no cell-resoluted parameters are given, the globally-meaned parameters from -#' Staffel et. all 2023 are taken -#' (see \link{https://doi.org/10.1038/s41560-023-01341-5}) +#' Staffel et. all 2023 are taken (see https://doi.org/10.1038/s41560-023-01341-5). #' #' @param t raster data on near-surface atmospherical temperature #' @param type considered climate variable @@ -488,7 +483,7 @@ blend <- function(bait, tas, weight) { #' near-surface temperature (see \code{\link{blend}}). #' #' @param baitInput named list containing rsds, sfcwind, huss climate data -#' @param tas raster data on near-surface atmospherical temperature +#' @param tasData raster data on near-surface atmospherical temperature #' @param weight named list with weights #' @param params optional named list with regression parameters from calcBAITpars #' @@ -780,6 +775,7 @@ aggCells <- function(data, weight, mask) { #' @param fhuss file name of data on near-surface specific humidity (optional) #' @param wBAIT named list containing BAIT weights (optional) #' @param params raster object containing regression parameters from \code{calcBAITpars} (optional) +#' @param rasDir absolute path to directory for saving raster files #' #' @importFrom raster writeRaster #' @importFrom stringr str_split diff --git a/R/readISIMIPbuildings.R b/R/readISIMIPbuildings.R index 62b4c57..70ed997 100644 --- a/R/readISIMIPbuildings.R +++ b/R/readISIMIPbuildings.R @@ -14,6 +14,7 @@ #' #' @importFrom stringr str_split #' @importFrom terra rast subset aggregate ext res +#' @importFrom ncdf4 nc_open #' #' @note #' folder structure in inputdata/sources/ISIMIPbuildings is expected to be: @@ -112,7 +113,7 @@ readISIMIPbuildings <- function(subtype) { # region mask if (vars[["variable"]] == "countrymask") { fpath <- file.path("countrymasks", subtype) - varNames <- names(ncdf4::nc_open(fpath)[["var"]]) + varNames <- names(nc_open(fpath)[["var"]]) countries <- list() for (var in varNames) { countries[[var]] <- suppressWarnings(rast(fpath, subds = var)) diff --git a/man/calcHDDCDD.Rd b/man/calcHDDCDD.Rd index 063563b..cf10b42 100644 --- a/man/calcHDDCDD.Rd +++ b/man/calcHDDCDD.Rd @@ -2,16 +2,35 @@ % Please edit documentation in R/calcHDDCDD.R \name{calcHDDCDD} \alias{calcHDDCDD} -\title{HDD and CDD scenario and historic data} +\title{Calculate HDD and CDD based on outdoor/indoor temperature difference} \usage{ -calcHDDCDD(tlimit = 18) +calcHDDCDD( + mappingFile, + bait = FALSE, + multiscen = FALSE, + rasDir = NULL, + cacheDir = NULL +) } \arguments{ -\item{tlimit}{Temperature threshold for computing HDD and CDD} +\item{mappingFile}{file name of sectoral mapping containing input data file names and directories} + +\item{bait}{boolean, use BAIT instead of raw temperature} + +\item{multiscen}{boolean, does \code{mappingFile} cover more than one scenario?} + +\item{rasDir}{absolute path to directory for saving raster files} + +\item{cacheDir}{absolute path to directory for pre-calculated BAIT regression parameters} +} +\value{ +magpie object of heating and cooling degree days } \description{ -smoothes the HDD and CDD scenario data +heating and cooling degree days based on raw outside temperature +or bias-adjusted internal temperature (BAIT), driver for space heating and +cooling demand in buildings } \author{ -Antoine Levesque +Robin Hasse, Hagen Tockhorn } From d4d6bfc8334b42d05db5ad5be41bb28147dea9a2 Mon Sep 17 00:00:00 2001 From: hagento Date: Mon, 18 Mar 2024 15:20:01 +0000 Subject: [PATCH 08/10] minor lucode adaptions --- NAMESPACE | 1 + R/calcHDDCDD.R | 43 ++++++++++++++++++++++++----------------- R/readISIMIPbuildings.R | 25 +++++++++++------------- 3 files changed, 37 insertions(+), 32 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index c5976cd..1d8815d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -119,6 +119,7 @@ importFrom(openxlsx,read.xlsx) importFrom(plyr,revalue) importFrom(pracma,integral2) importFrom(quadprog,solve.QP) +importFrom(quitte,aggregate_map) importFrom(quitte,as.quitte) importFrom(quitte,calc_addVariable) importFrom(quitte,getPeriods) diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index 7ba63d6..bd63566 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -67,10 +67,10 @@ calcHDDCDD <- function(mappingFile, pop, hddcddFactor, bait, - frsds = frsds, - fsfc = fsfc, - fhuss = fhuss, - wBAIT = wBAIT, + frsds = frsds, + fsfc = fsfc, + fhuss = fhuss, + wBAIT = wBAIT, params = params, rasDir = rasDir) } else { @@ -148,29 +148,27 @@ calcHDDCDD <- function(mappingFile, # READ-IN DATA---------------------------------------------------------------- # list of files that are processed - files <- toolGetMapping(mappingFile, type = "sectoral", where = "mredgebuildings") %>% + files <- toolGetMapping(name = mappingFile, + type = "sectoral", + where = "mredgebuildings") %>% filter(.data[["variable"]] != "") # cells -> country mask fCM <- file.path(files[files$variable == "CountryMask", "file"]) countries <- readSource("ISIMIPbuildings", subtype = fCM, convert = FALSE) + # population + fpop <- files %>% filter(.data[["variable"]] == "pop") + pop <- readSource("ISIMIPbuildings", subtype = fpop$file, + convert = FALSE) - - # PROCESS DATA---------------------------------------------------------------- - - # calculate HDD/CDD-factors - hddcddFactor <- calcHDDCDDFactors(tlow = tlow, tup = tup, tLim, tambStd, tlimStd) - + # extract run-specific variables ssp <- unique(files$ssp[files$variable == "tas"]) rcp <- unique(files$rcp[files$variable == "tas"]) model <- unique(files$gcm[files$variable == "tas"]) - # read population data - fpop <- files %>% filter(.data[["variable"]] == "pop") - pop <- readSource("ISIMIPbuildings", subtype = fpop$file, - convert = FALSE) + # bait regression parameters if (bait) { baitPars <- calcOutput("BAITpars", aggregate = FALSE, @@ -180,6 +178,15 @@ calcHDDCDD <- function(mappingFile, names(baitPars) <- parNames } + + + # PROCESS DATA---------------------------------------------------------------- + + # calculate HDD/CDD-factors + hddcddFactor <- calcHDDCDDFactors(tlow = tlow, tup = tup, tLim, tambStd, tlimStd) + + + # full calculation of degree days hddcdd <- do.call( # file iteration "rbind", lapply( @@ -394,21 +401,21 @@ prepBaitInput <- function(frsds = NULL, #' @return counterfactuals for respective climate variable cfac <- function(t, type, params = NULL) { - # nolint start if (is.null(params)) { params <- switch(type, s = c(100, 7), w = c(4.5, -0.025), h = c(1.1, 0.06), t = c(16)) - } + } + # nolint start return(switch(type, s = {params[[1]] + params[[2]] * t}, w = {params[[1]] + params[[2]] * t}, h = {exp(params[[1]] + params[[2]] * t)}, t = {params[[1]]}, - error("No valid parameter type specified.") #nolint + warning("No valid parameter type specified.") ) ) # nolint end diff --git a/R/readISIMIPbuildings.R b/R/readISIMIPbuildings.R index 70ed997..df08b78 100644 --- a/R/readISIMIPbuildings.R +++ b/R/readISIMIPbuildings.R @@ -109,16 +109,17 @@ readISIMIPbuildings <- function(subtype) { # PROCESS DATA---------------------------------------------------------------- vars <- splitSubtype(subtype) - # nolint start # region mask if (vars[["variable"]] == "countrymask") { - fpath <- file.path("countrymasks", subtype) - varNames <- names(nc_open(fpath)[["var"]]) + fpath <- file.path("countrymasks", subtype) + varNames <- names(nc_open(fpath)[["var"]]) countries <- list() + for (var in varNames) { countries[[var]] <- suppressWarnings(rast(fpath, subds = var)) } - r <- rast(countries) + + r <- rast(countries) names(r) <- gsub("m_", "", varNames) x <- list(x = r, class = "SpatRaster") @@ -126,20 +127,19 @@ readISIMIPbuildings <- function(subtype) { # population - else if (vars[["variable"]] == "population") { + else if (vars[["variable"]] == "population") { #nolint fpath <- file.path(vars[["variable"]], vars[["scenario"]], subtype) if (vars[["scenario"]] == "picontrol") { r <- suppressWarnings(rast(fpath)) - } - else { + } else { r <- suppressWarnings(rast(fpath, subds = "total-population")) } subtype <- gsub(".nc", "", subtype) # rename years - years <- tail(strsplit(subtype, "_")[[1]], 2) + years <- tail(strsplit(subtype, "_")[[1]], 2) names(r) <- years[1]:years[2] # filter relevant years @@ -148,8 +148,6 @@ readISIMIPbuildings <- function(subtype) { # aggregate to common resolution of 0.5 deg if (any(res(r) != 0.5)) { r <- aggregate(r, fun = "sum", fact = round(0.5 / res(r), 3)) - res(r) <- 0.5 - ext(r) <- round(ext(r)) } x <- list(x = r, class = "SpatRaster", cache = FALSE) @@ -157,7 +155,7 @@ readISIMIPbuildings <- function(subtype) { # climate data - else if (any(vars[["variable"]] %in% baitVars)) { + else if (any(vars[["variable"]] %in% baitVars)) { #nolint # slice single years if (!is.null(vars[["yStart"]])) { fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], vars[["subtype"]]) @@ -168,7 +166,7 @@ readISIMIPbuildings <- function(subtype) { } # full data set - else { + else { #nolint fpath <- file.path(vars[["variable"]], vars[["scenario"]], vars[["model"]], subtype) r <- suppressWarnings(rast(fpath)) } @@ -176,8 +174,7 @@ readISIMIPbuildings <- function(subtype) { x <- list(x = r, class = "SpatRaster", cache = FALSE) } - else {stop("Subtype was incorrectly split or invalid subtype given.")} - # nolint end + else {stop("Subtype was incorrectly split or invalid subtype given.")} #nolint return(x) } From 97fd54e509209a90752814a0cd0786bc3f8c840b Mon Sep 17 00:00:00 2001 From: hagento Date: Wed, 27 Mar 2024 16:23:28 +0100 Subject: [PATCH 09/10] final lucode adaptations and version update --- .buildlibrary | 3 ++- .github/workflows/check.yaml | 5 +++++ .gitignore | 27 +++++++++++++++++++++++++++ .pre-commit-config.yaml | 2 +- CITATION.cff | 4 ++-- DESCRIPTION | 4 ++-- README.md | 10 +++++----- 7 files changed, 44 insertions(+), 11 deletions(-) diff --git a/.buildlibrary b/.buildlibrary index aad36a0..8abc44c 100644 --- a/.buildlibrary +++ b/.buildlibrary @@ -1,7 +1,8 @@ -ValidationKey: '984200' +ValidationKey: '1188540' AutocreateReadme: yes AcceptedWarnings: - 'Warning: package ''.*'' was built under R version' - 'Warning: namespace ''.*'' is not available and has been replaced' AcceptedNotes: 'Unexported object imported by a .:::. call: .GDPuc:::wb_wdi.' allowLinterWarnings: no +enforceVersionUpdate: no diff --git a/.github/workflows/check.yaml b/.github/workflows/check.yaml index 870f216..46f518a 100644 --- a/.github/workflows/check.yaml +++ b/.github/workflows/check.yaml @@ -49,6 +49,11 @@ jobs: shell: Rscript {0} run: lucode2:::validkey(stopIfInvalid = TRUE) + - name: Verify that lucode2::buildLibrary was successful + if: github.event_name == 'pull_request' + shell: Rscript {0} + run: lucode2:::isVersionUpdated() + - name: Checks shell: Rscript {0} run: | diff --git a/.gitignore b/.gitignore index 5b6a065..cda71e7 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,30 @@ .Rhistory .RData .Ruserdata +.DS_Store +R/convertWEO.R +R/readWEO.R +inst/extdata/.DS_Store +inst/extdata/regional/.DS_Store +inst/extdata/regional/regionmappingEUshares.csv +inst/extdata/regional/regionmappingIEA_ETP-Shares.csv +inst/extdata/regional/regionmappingISO-EDGE_EUR_ETP.csv +inst/extdata/regional/regionmappingWEO.csv +inst/extdata/sectoral/baitregression-files_test.csv +inst/regional/ +inst/sectoral/ +man/aggCells.Rd +man/blend.Rd +man/calcBAIT.Rd +man/calcBAITpars.Rd +man/calcCellHDDCDD.Rd +man/calcHDDCDDFactors.Rd +man/calcStackHDDCDD.Rd +man/cfac.Rd +man/checkDates.Rd +man/convertISIMIPbuildings.Rd +man/convertWEO.Rd +man/prepBaitInput.Rd +man/readISIMIPbuildings.Rd +man/readWEO.Rd +man/smooth.Rd diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 2f13466..243f46a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,7 +15,7 @@ repos: - id: mixed-line-ending - repo: https://github.com/lorenzwalthert/precommit - rev: v0.3.2.9025 + rev: v0.4.0 hooks: - id: parsable-R - id: deps-in-desc diff --git a/CITATION.cff b/CITATION.cff index 88ce6a5..e25b69d 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -2,8 +2,8 @@ cff-version: 1.2.0 message: If you use this software, please cite it using the metadata from this file. type: software title: 'mredgebuildings: Prepare data to be used by the EDGE-Buildings model' -version: 0.5.0 -date-released: '2023-11-23' +version: 0.6.0 +date-released: '2024-03-27' abstract: Prepare data to be used by the EDGE-Buildings model. authors: - family-names: Hasse diff --git a/DESCRIPTION b/DESCRIPTION index ac07a7e..6d20c33 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Type: Package Package: mredgebuildings Title: Prepare data to be used by the EDGE-Buildings model -Version: 0.5.0 -Date: 2023-11-23 +Version: 0.6.0 +Date: 2024-03-27 Authors@R: c( person("Robin", "Hasse", , "robin.hasse@pik-potsdam.de", role = c("aut", "cre"), comment = c(ORCID = "0000-0003-1818-3186")), diff --git a/README.md b/README.md index cec8b70..f705081 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # Prepare data to be used by the EDGE-Buildings model -R package **mredgebuildings**, version **0.5.0** +R package **mredgebuildings**, version **0.6.0** -[![CRAN status](https://www.r-pkg.org/badges/version/mredgebuildings)](https://cran.r-project.org/package=mredgebuildings) [![R build status](https://github.com/pik-piam/mredgebuildings/workflows/check/badge.svg)](https://github.com/pik-piam/mredgebuildings/actions) [![codecov](https://codecov.io/gh/pik-piam/mredgebuildings/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mredgebuildings) [![r-universe](https://pik-piam.r-universe.dev/badges/mredgebuildings)](https://pik-piam.r-universe.dev/builds) +[![CRAN status](https://www.r-pkg.org/badges/version/mredgebuildings)](https://cran.r-project.org/package=mredgebuildings) [![R build status](https://github.com/pik-piam/mredgebuildings/workflows/check/badge.svg)](https://github.com/pik-piam/mredgebuildings/actions) [![codecov](https://codecov.io/gh/pik-piam/mredgebuildings/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mredgebuildings) ## Purpose and Functionality @@ -38,7 +38,7 @@ In case of questions / problems please contact Robin Hasse . +Hasse R, Führlich P, Levesque A, Tockhorn H (2024). _mredgebuildings: Prepare data to be used by the EDGE-Buildings model_. R package version 0.6.0, . A BibTeX entry for LaTeX users is @@ -46,8 +46,8 @@ A BibTeX entry for LaTeX users is @Manual{, title = {mredgebuildings: Prepare data to be used by the EDGE-Buildings model}, author = {Robin Hasse and Pascal Führlich and Antoine Levesque and Hagen Tockhorn}, - year = {2023}, - note = {R package version 0.5.0}, + year = {2024}, + note = {R package version 0.6.0}, url = {https://github.com/pik-piam/mredgebuildings}, } ``` From 68dc604499f507b0706457e0968e34a86a652227 Mon Sep 17 00:00:00 2001 From: hagento Date: Wed, 3 Apr 2024 10:29:50 +0200 Subject: [PATCH 10/10] changed function names --- R/calcHDDCDD.R | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/R/calcHDDCDD.R b/R/calcHDDCDD.R index bd63566..df5be0d 100644 --- a/R/calcHDDCDD.R +++ b/R/calcHDDCDD.R @@ -1,8 +1,8 @@ #' Calculate HDD and CDD based on outdoor/indoor temperature difference #' -#' @description heating and cooling degree days based on raw outside temperature -#' or bias-adjusted internal temperature (BAIT), driver for space heating and -#' cooling demand in buildings +#' Heating and cooling degree days based on raw outside temperature +#' or bias-adjusted internal temperature (BAIT), driver for space heating and +#' cooling demand in buildings. #' #' @param mappingFile file name of sectoral mapping containing input data file names and directories #' @param bait boolean, use BAIT instead of raw temperature @@ -61,7 +61,7 @@ calcHDDCDD <- function(mappingFile, print(paste("Processing temperature file:", ftas)) if (bait) { - hddcddCell <- calcStackHDDCDD(ftas, + hddcddCell <- compStackHDDCDD(ftas, tLim, countries, pop, @@ -74,7 +74,7 @@ calcHDDCDD <- function(mappingFile, params = params, rasDir = rasDir) } else { - hddcddCell <- calcStackHDDCDD(ftas, + hddcddCell <- compStackHDDCDD(ftas, tLim, countries, pop, @@ -183,7 +183,7 @@ calcHDDCDD <- function(mappingFile, # PROCESS DATA---------------------------------------------------------------- # calculate HDD/CDD-factors - hddcddFactor <- calcHDDCDDFactors(tlow = tlow, tup = tup, tLim, tambStd, tlimStd) + hddcddFactor <- compHDDCDDFactors(tlow = tlow, tup = tup, tLim, tambStd, tlimStd) # full calculation of degree days @@ -496,7 +496,7 @@ blend <- function(bait, tas, weight) { #' #' @return raster object with BAIT values -calcBAIT <- function(baitInput, tasData, weight = NULL, params = NULL) { +compBAIT <- function(baitInput, tasData, weight = NULL, params = NULL) { if (is.null(weight)) { warning("Please give appropriate weights for the calculation of BAIT.") weight <- list("wRSDS" = 0.012, @@ -572,7 +572,7 @@ calcBAIT <- function(baitInput, tasData, weight = NULL, params = NULL) { #' @importFrom stats dnorm #' @importFrom pracma integral2 -calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd = 5, tlimStd = 5) { +compHDDCDDFactors <- function(tlow, tup, tlim, tambStd = 5, tlimStd = 5) { # t1 : ambient temperature variable # t2 : limit temperature variable @@ -687,7 +687,7 @@ calcHDDCDDFactors <- function(tlow, tup, tlim, tambStd = 5, tlimStd = 5) { #' #' @importFrom terra classify tapp -calcCellHDDCDD <- function(temp, typeDD, tlim, factors) { +compCellHDDCDD <- function(temp, typeDD, tlim, factors) { # extract years dates <- names(temp) @@ -788,7 +788,7 @@ aggCells <- function(data, weight, mask) { #' @importFrom stringr str_split #' @importFrom terra writeCDF -calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, +compStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, frsds = NULL, fsfc = NULL, fhuss = NULL, @@ -811,7 +811,7 @@ calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, checkDates(tasData) # calculate bait - temp <- calcBAIT(baitInput, tasData, weight = wBAIT, params = params) + temp <- compBAIT(baitInput, tasData, weight = wBAIT, params = params) # convert back to [K] temp <- temp + 273.15 # [K] @@ -834,7 +834,7 @@ calcStackHDDCDD <- function(ftas, tlim, countries, pop, factors, bait, do.call( "rbind", lapply( tlim[[typeDD]], function(t) { - hddcddAgg <- calcCellHDDCDD(temp, typeDD, t, factors) + hddcddAgg <- compCellHDDCDD(temp, typeDD, t, factors) # write raster files if (!is.null(rasDir)) {