Generate global and regional mif

R/calcHistorical.R

@@ -1,3 +1,4 @@
+#' Gather reference data from various sources.
 #' @importFrom magclass setNames getNames getSets add_columns
 #' @importFrom luscale rename_dimnames
 
@@ -177,8 +178,6 @@ calcHistorical <- function() {
   ARIADNE_ReferenceScenarioPop <- add_dimension(ARIADNE_ReferenceScenarioPop, 
                                                 dim = 3.1, add = "model", nm = "ARIADNE")
 
-  IEA_ETP <- calcOutput("IEA_ETP", aggregate = F)
-
   IEA_EVOutlook <- calcOutput("IEA_EVOutlook", aggregate = F)
 
   # Calculate Emission Reference Values
@@ -235,8 +234,6 @@ calcHistorical <- function() {
   BP <- calcOutput("BP", aggregate = FALSE)
   BP <- add_dimension(BP, dim = 3.1, add = "model", nm = "BP")
 
-  WEO_2021 <- calcOutput("IEA_WEO_2021", subtype = "regional", aggregate = F)
-
   # Steel Production ----
   worldsteel <- readSource('worldsteel', convert = FALSE) %>% 
     madrat_mule() %>% 
@@ -294,7 +291,7 @@ calcHistorical <- function() {
                   LU_EDGAR_LU, LU_CEDS, LU_FAO_EmisLUC, LU_FAO_EmisAg, LU_PRIMAPhist, IRENAcap, eurostat, #emiMktES, emiMktETS, emiMktESOthers, 
                   EU_ReferenceScenario, emiEurostat, ARIADNE_ReferenceScenarioGdp, ARIADNE_ReferenceScenarioGdpCorona,
                   ARIADNE_ReferenceScenarioPop, EEA_GHGSectoral, EEA_GHGTotal, EEA_GHGProjections, Emi_Reference, #, EEA_GHGES
-                  IEA_ETP, IEA_EVOutlook, INNOPATHS, JRC_Industry, JRC_Transport, JRC_ResCom, AGEB_FE, UBA_emi, UNFCCC, BP, worldsteel, WEO_2021)
+                  IEA_EVOutlook, INNOPATHS, JRC_Industry, JRC_Transport, JRC_ResCom, AGEB_FE, UBA_emi, UNFCCC, BP, worldsteel)
 
   y <- Reduce(union,lapply(varlist,getYears))
   n <- Reduce(c,lapply(varlist,getNames))

R/calcHistoricalGlobal.R

@@ -0,0 +1,41 @@
+#' Gather global reference data from various sources. Call this function with aggregate = "global".
+#' @importFrom magclass setNames getNames getSets add_columns
+#' @importFrom luscale rename_dimnames
+#' @importFrom madrat getISOlist
+#' 
+
+calcHistoricalGlobal <- function() {
+
+  IEA_ETP <- calcOutput("IEA_ETP", aggregate = F)
+
+  WEO_2021 <- calcOutput("IEA_WEO_2021", subtype = "GLO", aggregate = F)
+
+  # ====== start: blow up to union of years ===================
+  # find all existing years (y) and variable names (n)
+
+  varlist <- list(IEA_ETP, WEO_2021)
+
+  y <- Reduce(union, lapply(varlist, getYears))
+  n <- Reduce(c, lapply(varlist, getNames))
+  y <- sort(y)
+
+  # create empty object with full temporal, regional and data dimensionality
+  data <- new.magpie(getISOlist(), y, n, fill = NA)
+
+  getSets(data)[3] <- "model"
+  getSets(data)[4] <- "variable"
+
+  # transfer data of existing years
+  for (i in varlist) {
+    data[, getYears(i), getNames(i)] <- i
+  }
+  # ====== end: blow up to union of years ===================
+
+  # add scenario dimension
+  data <- add_dimension(data, dim = 3.1, add = "scenario", nm = "historical")
+
+  # rename dimension "data" into "variable"
+  getSets(data)[5] <- "variable"
+
+  return(list(x = data, weight = NULL, unit = "Various", description = "Historical Data"))
+}

R/calcHistoricalRegion.R

@@ -0,0 +1,47 @@
+#' Gather global reegional data from various sources. Call this function with aggregate = "region".
+#' @importFrom magclass setNames getNames getSets add_columns
+#' @importFrom luscale rename_dimnames
+#' @importFrom madrat getISOlist
+#'
+
+calcHistoricalRegion <- function() {
+
+  IEA_ETP <- calcOutput("IEA_ETP", aggregate = F)
+
+  # filter regional cement and steel production data from IEA ETP
+  IEA_ETP <- IEA_ETP[, , c(
+    "Production|Industry|Cement (Mt/yr)",
+    "Production|Industry|Steel (Mt/yr"
+  ), pmatch = T, invert = T]
+
+  WEO_2021 <- calcOutput("IEA_WEO_2021", subtype = "regional", aggregate = F)
+
+  # ====== start: blow up to union of years ===================
+  # find all existing years (y) and variable names (n)
+
+  varlist <- list(IEA_ETP, WEO_2021)
+
+  y <- Reduce(union, lapply(varlist, getYears))
+  n <- Reduce(c, lapply(varlist, getNames))
+  y <- sort(y)
+
+  # create empty object with full temporal, regional and data dimensionality
+  data <- new.magpie(getISOlist(), y, n, fill = NA)
+
+  getSets(data)[3] <- "model"
+  getSets(data)[4] <- "variable"
+
+  # transfer data of existing years
+  for (i in varlist) {
+    data[, getYears(i), getNames(i)] <- i
+  }
+  # ====== end: blow up to union of years ===================
+
+  # add scenario dimension
+  data <- add_dimension(data, dim = 3.1, add = "scenario", nm = "historical")
+
+  # rename dimension "data" into "variable"
+  getSets(data)[5] <- "variable"
+
+  return(list(x = data, weight = NULL, unit = "Various", description = "Historical Data"))
+}

R/fullVALIDATIONREMIND.R

@@ -15,6 +15,11 @@
 fullVALIDATIONREMIND <- function(rev = 0) {
 
   #-------------- historical data ---------------------------------------------------------------------
+
   calcOutput("Historical", round = 5,  file = "historical.mif", aggregate = "region+global+missingH12")
+
+  calcOutput("HistoricalGlobal", round = 5,  file = "historical_global.mif", aggregate = "global")
+
+  calcOutput("HistoricalRegion", round = 5,  file = "historical_region.mif", aggregate = "region")
 
-}
+  }