remove pcc and pco from 'calcCoolingSharesAll'

R/calcCoolingSharesAll.R

@@ -1,55 +1,58 @@
 #' Calculate Cooling Type Shares
-#' 
+#'
 #' This function merges the output of two other functions that calculate REMIND
 #' input data for the shares of cooling types per electricity technology and
 #' REMIND region, using as initial information the Davies (2013) data per
 #' electricity technology and GCAM region. The two other functions separately
 #' calculate data for the base year and for future time steps. The source data
 #' provide most required information but some assumptions on missing data are
 #' also made.
-#' 
-#' 
+#'
+#'
 #' @return MAgPIE object on cooling type shares per elecricity technology and
 #' REMIND region
 #' @author Ioanna Mouratiadou
 #' @seealso \code{\link{calcOutput}}, \code{\link{readDaviesCooling}},
 #' \code{\link{convertDaviesCooling}},
 #' \code{\link{calcCoolingSharesBase}},\code{\link{calcCoolingSharesFuture}}
 #' @examples
-#' 
-#' \dontrun{ 
-#' 
+#' \dontrun{
+#'
 #' a <- calcOutput("CoolingSharesAll")
-#' 
 #' }
-#' 
+#'
 calcCoolingSharesAll <- function() {
+  cooloutputBase <- calcOutput("CoolingSharesBase", aggregate = FALSE)
+  cooloutputFuture <- calcOutput("CoolingSharesFuture", aggregate = FALSE)
 
-  cooloutputBase   <- calcOutput("CoolingSharesBase", aggregate=FALSE)
-  cooloutputFuture <- calcOutput("CoolingSharesFuture",aggregate=FALSE)
-
   # merge two datasets
-  outputAll <- mbind(cooloutputBase,cooloutputFuture)
-
-  #assign aggregation weight
-  weight     <- dimSums(calcOutput("IO",subtype="output",aggregate=FALSE)[,2010,c("feelb","feeli")],dim=3) 
-
-  #set weights to zero for countries that were not contained in the GCAM2ISO mapping
-  weight["ALA",,] <- 0
-  weight["ATA",,] <- 0
-  weight["BES",,] <- 0
-  weight["BLM",,] <- 0
-  weight["CUW",,] <- 0
-  weight["GGY",,] <- 0
-  weight["IMN",,] <- 0
-  weight["JEY",,] <- 0
-  weight["MAF",,] <- 0
-  weight["PSE",,] <- 0
-  weight["SSD",,] <- 0
-  weight["SXM",,] <- 0
+  outputAll <- mbind(cooloutputBase, cooloutputFuture)
 
-  return(list(x=outputAll, weight=weight,
-              unit="% of cooling type technologies", 
-              description="Cooling shares for different cooling technologies based on Davies et al. (2013) publication and using electricity use weights (aggregated based on IEA World Energy Balances, 2014) for regional mapping"              
+  # assign aggregation weight
+  weight <- dimSums(calcOutput("IO", subtype = "output", aggregate = FALSE)[, 2010, c("feelb", "feeli")], dim = 3)
+
+  # set weights to zero for countries that were not contained in the GCAM2ISO mapping
+  weight["ALA", , ] <- 0
+  weight["ATA", , ] <- 0
+  weight["BES", , ] <- 0
+  weight["BLM", , ] <- 0
+  weight["CUW", , ] <- 0
+  weight["GGY", , ] <- 0
+  weight["IMN", , ] <- 0
+  weight["JEY", , ] <- 0
+  weight["MAF", , ] <- 0
+  weight["PSE", , ] <- 0
+  weight["SSD", , ] <- 0
+  weight["SXM", , ] <- 0
+
+  return(list(
+    x = outputAll,
+    weight = weight,
+    unit = "% of cooling type technologies",
+    description = c(
+      "Cooling shares for different cooling technologies based on ",
+      "Davies et al. (2013) publication and using electricity use weights (aggregated ",
+      "based on IEA World Energy Balances, 2014) for regional mapping"
+    )
   ))
 }

R/calcCoolingSharesBase.R

@@ -1,118 +1,124 @@
 #' Calculate Cooling Type Shares for the Base Year
-#' 
+#'
 #' This function calculates REMIND input data for the shares of cooling types
 #' per electricity technology and REMIND region in 2005, using as initial
 #' information the Davies (2013) data per electricity technology and GCAM
 #' region. The source data provide most required information but some
 #' assumptions on missing data are also made.
-#' 
-#' 
+#'
+#'
 #' @return MAgPIE object on cooling type shares per elecricity technology and
 #' REMIND region
 #' @author Lavinia Baumstark, Ioanna Mouratiadou
 #' @seealso \code{\link{calcOutput}}, \code{\link{readDaviesCooling}},
 #' \code{\link{convertDaviesCooling}},
 #' \code{\link{calcCoolingSharesAll}},\code{\link{calcCoolingSharesFuture}}
 #' @examples
-#' 
-#' \dontrun{ 
+#' \dontrun{
 #' a <- calcOutput("CoolingSharesBase")
 #' }
-#' @importFrom readxl read_excel
-
-
-
 calcCoolingSharesBase <- function() {
-
   # read in data
-  data <- readSource("DaviesCooling", subtype="dataBase")
-  getNames(data)[grepl("^Sea",getNames(data))] <- "Sea.NA"
-  
+  data <- readSource("DaviesCooling", subtype = "dataBase")
+  getNames(data)[grepl("^Sea", getNames(data))] <- "Sea.NA"
+
   # seperate data for Sea water
-  Sea <- data[,,"Sea"]
-  data <- data[,,-which(getNames(data)=="Sea.NA")]
-  
-  id <- getNames(data,dim=1)
-  
+  Sea <- data[, , "Sea"]
+  data <- data[, , -which(getNames(data) == "Sea.NA")]
+
+  id <- getNames(data, dim = 1)
+
   # calculate pond
-  pond <- new.magpie(getRegions(data),getYears(data),id)
+  pond <- new.magpie(getRegions(data), getYears(data), id)
   for (i in id) {
-    pond[,,i] <- 100 - dimSums(data[,,i],dim=3.2)
+    pond[, , i] <- 100 - dimSums(data[, , i], dim = 3.2)
   }
-  getNames(pond) <- paste(getNames(pond),"Pond",sep=".")
-  
+  getNames(pond) <- paste(getNames(pond), "Pond", sep = ".")
+
   # add pond to data
-  data <- mbind(data,pond)
-  
+  data <- mbind(data, pond)
+
   # calculate sea water
-  sea_new <- new.magpie(getRegions(data),getYears(data),id)
+  sea_new <- new.magpie(getRegions(data), getYears(data), id)
   for (i in id) {
-    sea_new[,,i] <- data[,,paste(i,"1-thru",sep=".")] * Sea/100
+    sea_new[, , i] <- data[, , paste(i, "1-thru", sep = ".")] * Sea / 100
   }
-  getNames(sea_new) <- paste(getNames(sea_new),"Sea",sep=".")
-  
+  getNames(sea_new) <- paste(getNames(sea_new), "Sea", sep = ".")
+
   # correct 1-thru-data
-  data[,,"1-thru"]  <- data[,,"1-thru"] * (1 - Sea/100)
+  data[, , "1-thru"] <- data[, , "1-thru"] * (1 - Sea / 100)
   # add sea data to data
-  data <- mbind(data,sea_new)
-  
+  data <- mbind(data, sea_new)
+
   # check if all categories sum up to 100%
-  check <- new.magpie(getRegions(data),getYears(data),id)
+  check <- new.magpie(getRegions(data), getYears(data), id)
   for (i in id) {
-    check[,,i] <- dimSums(data[,,i],dim=3.2)  
+    check[, , i] <- dimSums(data[, , i], dim = 3.2)
   }
-  if(!all(check==100)) { stop("sum of categorie XXX is not 100%")}
-
+  if (!all(check == 100)) {
+    stop("sum of categorie XXX is not 100%")
+  }
+
   # read in mapping to REMIND technologies
-  map_table <- read_excel(toolGetMapping(type = "sectoral",
-                                         name = "TechnologyMappingDavies2REMIND.xlsx",
-                                         returnPathOnly = TRUE, where = "mappingfolder"))
+  map_table <- toolGetMapping(
+    type = "sectoral",
+    name = "techmappingDaviesToREMIND.csv",
+    where = "mrremind"
+  )
   map <- list()
-  map$davies <- paste(map_table$'Davies Source/Technology',map_table$'Davies Cooling',sep=".")
-  map$remind <- paste(map_table$'REMIND Technology',map_table$'REMIND Cooling',sep=".")
-  
+  map$davies <- paste(map_table$Davies.Source.Technology, map_table$Davies.Cooling, sep = ".")
+  map$remind <- paste(map_table$REMIND.Technology, map_table$REMIND.Cooling, sep = ".")
+
   # calculate REMIND input in REMIND categories
-  output <- new.magpie(getRegions(data),getYears(data),map$remind)
-  output[,,] <- 0
-  for(d in 1:length(map$davies)){
-    if( !map$davies[d] == "-.-"){
-      output[,,map$remind[d]] <- data[,,map$davies[d]]
+  output <- new.magpie(getRegions(data), getYears(data), map$remind)
+  output[, , ] <- 0
+  for (d in 1:length(map$davies)) {
+    if (!map$davies[d] == "-.-") {
+      output[, , map$remind[d]] <- data[, , map$davies[d]]
     }
   }
-
+
+  # remove no longer used technologies pcc and pco
+  output <- output[, , c("pcc", "pco"), invert = TRUE]
+
   # add assumed data
-  output[,,"geohdr.tower"]  <- 70
-  output[,,"geohdr.dry"]    <- 20
-  output[,,"geohdr.hybrid"] <- 10
-  output[,,"hydro.default"] <- 100
-  output[,,"wind.default"]  <- 100
-  output[,,"spv.default"]   <- 100
-  output[,,"csp.tower"]     <- 70
-  output[,,"csp.dry"]       <- 20
-  output[,,"csp.hybrid"]    <- 10
-
-  outputBase <- new.magpie(getRegions(output),c(2005),getNames(output))
-  outputBase[,,] <- output[,,]
-
-  #assign aggregation weight
-  weight     <- dimSums(calcOutput("IO",subtype="output",aggregate=FALSE)[,2010,c("feelb","feeli")],dim=3) 
-
-  #set weights to zero for countries that were not contained in the GCAM2ISO mapping
-  weight["ALA",,] <- 0
-  weight["ATA",,] <- 0
-  weight["BES",,] <- 0
-  weight["BLM",,] <- 0
-  weight["CUW",,] <- 0
-  weight["GGY",,] <- 0
-  weight["IMN",,] <- 0
-  weight["JEY",,] <- 0
-  weight["MAF",,] <- 0
-  weight["PSE",,] <- 0
-  weight["SSD",,] <- 0
-  weight["SXM",,] <- 0
-
-  return(list(x=outputBase,weight=weight,
-              unit="% of cooling type technologies", 
-              description="Cooling shares for different cooling technologies based on Davies et al. (2013) publication and using electricity use weights (aggregated based on IEA World Energy Balances, 2014) for regional mapping"
+  output[, , "geohdr.tower"] <- 70
+  output[, , "geohdr.dry"] <- 20
+  output[, , "geohdr.hybrid"] <- 10
+  output[, , "hydro.default"] <- 100
+  output[, , "wind.default"] <- 100
+  output[, , "spv.default"] <- 100
+  output[, , "csp.tower"] <- 70
+  output[, , "csp.dry"] <- 20
+  output[, , "csp.hybrid"] <- 10
+
+  outputBase <- new.magpie(getRegions(output), c(2005), getNames(output))
+  outputBase[, , ] <- output[, , ]
+
+  # assign aggregation weight
+  weight <- dimSums(calcOutput("IO", subtype = "output", aggregate = FALSE)[, 2010, c("feelb", "feeli")], dim = 3)
+
+  # set weights to zero for countries that were not contained in the GCAM2ISO mapping
+  weight["ALA", , ] <- 0
+  weight["ATA", , ] <- 0
+  weight["BES", , ] <- 0
+  weight["BLM", , ] <- 0
+  weight["CUW", , ] <- 0
+  weight["GGY", , ] <- 0
+  weight["IMN", , ] <- 0
+  weight["JEY", , ] <- 0
+  weight["MAF", , ] <- 0
+  weight["PSE", , ] <- 0
+  weight["SSD", , ] <- 0
+  weight["SXM", , ] <- 0
+
+  return(list(
+    x = outputBase, weight = weight,
+    unit = "% of cooling type technologies",
+    description = c(
+      "Cooling shares for different cooling technologies based on Davies et al. (2013) ",
+      "publication and using electricity use weights (aggregated based on IEA World ",
+      "Energy Balances, 2014) for regional mapping"
+    )
   ))
 }