Merge branch 'master' of https://github.com/pik-piam/mrmagpie into ma…

…ster_mppalves

merge

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '27381835'
+ValidationKey: '27487080'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'

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: 'mrmagpie: madrat based MAgPIE Input Data Library'
-version: 1.39.1
-date-released: '2023-11-24'
+version: 1.39.5
+date-released: '2023-12-13'
 abstract: Provides functions for MAgPIE country and cellular input data generation.
 authors:
 - family-names: Karstens
@@ -18,11 +18,12 @@ authors:
   given-names: Michael
 - family-names: Alves
   given-names: Marcos
-- family-names: Köberle
-  given-names: Alexandre
 - family-names: Beier
   given-names: Felicitas
   email: [email protected]
+- family-names: Köberle
+  given-names: Alexandre
+  email: [email protected]
 - family-names: v. Jeetze
   given-names: Patrick
   email: [email protected]

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: mrmagpie
 Title: madrat based MAgPIE Input Data Library
-Version: 1.39.1
-Date: 2023-11-24
+Version: 1.39.5
+Date: 2023-12-13
 Authors@R: c(
     person("Kristine", "Karstens", , "[email protected]", role = c("aut", "cre")),
     person("Jan Philipp", "Dietrich", , "[email protected]", role = "aut"),

R/calcFoodDemandGridded.R

@@ -4,6 +4,7 @@
 #' @param attribute dm or calories ("ge") or other massbalance attribute
 #' @param feed whether to include feed demand in the gridded demand
 #' @param prod for memory reasons
+#' @param cells magpiecell or lpjcell (default)
 #' @author David M Chen
 #' @importFrom withr local_options
 #' @importFrom magpiesets findset
@@ -12,7 +13,7 @@
 #' calcOutput("FoodDemandGridded")
 #' }
 #'
-calcFoodDemandGridded <- function(attribute = "dm", prod = "k", feed = TRUE) {
+calcFoodDemandGridded <- function(attribute = "dm", prod = "k", feed = TRUE, cells = "lpjcell") {
 
   # Country-level food demand
   foodDemand <- calcOutput("FAOmassbalance", aggregate = FALSE)
@@ -26,7 +27,7 @@ calcFoodDemandGridded <- function(attribute = "dm", prod = "k", feed = TRUE) {
 
   # Gridded population
   gridPop <- collapseNames(calcOutput("GridPop", aggregate = FALSE,
-                                      cellular = TRUE, cells = "lpjcell",
+                                      cellular = TRUE, cells = cells,
                                       source = "Gao", urban = TRUE)[, hist, "SSP2"])
   # Country-level population
   popAgg    <- dimSums(gridPop, dim = c("x", "y"))
@@ -49,15 +50,15 @@ calcFoodDemandGridded <- function(attribute = "dm", prod = "k", feed = TRUE) {
   local_options(magclass_sizeLimit = 1e+12)
   prods         <- findset(prod)
   foodDisaggUrb <- foodDisaggUrb[, , prods]
-  
+
   # Sum up demand dimension, this uses a lot of memory so split again
-  pr1 <- prods[c(1:(length(prods)/2))]
-  pr2 <- prods[c((length(prods)/2 + 1):(length(prods) + 0.5))] #0.5 to ensure if odd numbers
+  pr1 <- prods[c(1:(length(prods) / 2))]
+  pr2 <- prods[c((length(prods) / 2 + 1):(length(prods) + 0.5))] #0.5 to ensure if odd numbers
 
   foodDisaggUrb1 <- dimSums(foodDisaggUrb[, , pr1], dim = 3.2)
   foodDisaggUrb2 <- dimSums(foodDisaggUrb[, , pr2], dim = 3.2)
   foodDisaggUrb <- mbind(foodDisaggUrb1, foodDisaggUrb2)
- 
+
   return(list(x = foodDisaggUrb,
               weight = NULL,
               unit = "Mt",

R/calcNonLocalProduction.R

@@ -3,26 +3,28 @@
 #' assuming that food produced in the grid cell is first consumed by local population
 #' i.e. amount of food greater than local rural demand, split into that which feeds the local
 #' urban population, and that which exceeds total local demand and is available to export
+#' @param cells magpiecell or lpjcell (default)
 #' @author David M Chen
 #' @examples
 #' \dontrun{
 #' calcOutput("NonLocalTransport")
 #' }
-calcNonLocalProduction <- function() {
+calcNonLocalProduction <- function(cells = "lpjcell") {
 
   productionPri  <- calcOutput("Production",  products = "kcr",
-                               cellular = TRUE, cells = "lpjcell",
+                               cellular = TRUE, cells = cells,
                                aggregate = FALSE)
   productionLi   <- calcOutput("Production", products = "kli",
-                               cellular = TRUE, cells = "lpjcell",
+                               cellular = TRUE, cells = cells,
                                aggregate = FALSE)
   productionPast <- calcOutput("Production", products = "pasture", cellular = TRUE,
-                               cells = "lpjcell", aggregate = FALSE)
+                               cells = cells, aggregate = FALSE)
   productionPast <- add_dimension(productionPast,   dim = 3.1, add = "Item", nm = "pasture")
   production <- collapseNames(mbind(productionPri, productionLi)[, , "dm"])
   production <- mbind(production, collapseNames(productionPast[, , "dm"], collapsedim  = 2))
 
-  foodDisaggUrb <- calcOutput("FoodDemandGridded", feed = TRUE, aggregate = FALSE)
+  foodDisaggUrb <- calcOutput("FoodDemandGridded", feed = TRUE,
+                              cells = cells, aggregate = FALSE)
   # note this also includes feed demand!
 
   foodDemPrim <- collapseNames(foodDisaggUrb[, , getItems(production, dim = 3)])

R/calcTransportCosts.R

@@ -3,39 +3,55 @@
 #' transport costs, cellular production, and cellular travel time
 #' @param transport "all" or "nonlocal". "all" means all production incurs transport costs,
 #' while "nonlocal" sees only production greater than local rural consumption with transport costs
+#' @param gtapVersion "9" or "81"
 #' @return List of magpie objects with results on country level, weight on country level, unit and description.
 #' @author David M Chen
 #' @seealso
 #' [calcTransportTime()],
 #' [calcGTAPTotalTransportCosts()]
 #' @examples
 #' \dontrun{
-#' calcOutput("TransportCosts")
+#' calcOutput("TransportCosts_new")
 #' }
 #'
-calcTransportCosts <- function(transport = "all") { # nolint
+calcTransportCosts <- function(transport = "all", gtapVersion = "9") { # nolint
 
   # load distance (travel time), production, and gtap transport costs
-  distance <- calcOutput("TransportTime", subtype = "cities50", cells = "lpjcell",  aggregate = FALSE)
+  distance <- calcOutput("TransportTime", subtype = "cities50", aggregate = FALSE)
+
+  if (gtapVersion == "9") {
+    yr <- 2011
+  } else {
+    yr <- 2004
+  }
 
   if (transport == "all") {
 
-    production <- calcOutput("Production", cellular = TRUE, cells = "lpjcell",
-                             irrigation = FALSE, aggregate = FALSE)[, 2005, "dm"] * 10^6
-    productionLi <- calcOutput("Production", cellular = TRUE, cells = "lpjcell",
+    production <- calcOutput("Production", cellular = TRUE,
+                             irrigation = FALSE, aggregate = FALSE)[, , "dm"] * 10^6
+    productionLi <- calcOutput("Production", cellular = TRUE,
                                irrigation = FALSE, aggregate = FALSE,
-                               products = "kli")[, 2005, "dm"] * 10^6
+                               products = "kli")[, , "dm"] * 10^6
     production <- mbind(production, productionLi)
-
+    production <- time_interpolate(production, interpolated_year = c(2004:2011),
+                                   integrate_interpolated_years = TRUE)
+    production[production < 0] <- 0
+    production <- production[, yr, ]
   } else if (transport == "nonlocal") {
-    production <- calcOutput("NonLocalProduction", aggregate = FALSE)[, 2005, ] * 10^6
+    production <- calcOutput("NonLocalProduction",
+                             aggregate = FALSE) * 10^6
+    production <- time_interpolate(production, interpolated_year = c(2004:2011),
+                                   integrate_interpolated_years = TRUE)
+    production[production < 0] <- 0
+    production <- production[, yr, ]
+
   } else {
     stop("only all or nonlocal production available for subtype")
   }
 
-  transportGtap <- calcOutput("GTAPTotalTransportCosts", aggregate = FALSE)[, 2004, ] * 10^6
+  transportGtap <- calcOutput("GTAPTotalTransportCosts", version = gtapVersion,
+                              aggregate = FALSE)[, yr, ] * 10^6
   # transform 10^6 USD -> USD
-  # this is  in 2004 and 2007 current USD, and we don't have same year for travel time, and pretend GTAP is 2005
   # some processed products available in GTAP are neglected for the moment neglected here, as we don't have cellular
   # production data for them. These are  "pfb" (fibres) "sgr" ("sugar") "vol" ("oils") "b_t" ("alcohol")  "fst" "wood"
 
@@ -65,17 +81,14 @@ calcTransportCosts <- function(transport = "all") { # nolint
   # calculate transport power (amount * distance) &
   # create average transport costs per ton per distance dummy
   # calculate transport power (amount * distance)
-  tmpPower <- new.magpie(cells_and_regions = getItems(distance, dim = 1),
-                         years = "y2005",
-                         names = names(cftRel),
-                         fill = 0,
-                         sets = c("x", "y", "iso", "year", "data"))
-
-  transportPower <- new.magpie(cells_and_regions = getItems(distance, dim = "iso"),
-                               years = "y2005",
-                               names = names(cftRel),
-                               fill = 0,
-                               sets = c("iso", "year", "data"))
+
+  tmpPower <- new.magpie(0, cells_and_regions = getItems(distance, dim = 1),
+                         years = yr,
+                         names = names(cftRel))
+
+  transportPower <- new.magpie(0, cells_and_regions = unique(getItems(tmpPower, dim = 1.3)),
+                               years = yr,
+                               names = names(cftRel))
 
   # create average transport costs per ton per distance dummy
   transportPerTonPerDistance <- NULL
@@ -85,8 +98,7 @@ calcTransportCosts <- function(transport = "all") { # nolint
     # should also include rural consumers, and farms
     tmpPower[, , names(cftRel)[i]] <- dimSums(production[, , cftRel[[i]]], dim = 3) * distance
 
-    transportPower[, , names(cftRel)[i]] <- dimSums(x = tmpPower[, , names(cftRel)[i]],
-                                                    dim = c("x", "y"))
+    transportPower[, , names(cftRel)[i]] <- dimSums(tmpPower[, , names(cftRel)[i]], dim = c("region", "region1"))
 
     tpFilled <- toolCountryFill(transportPower, fill = 0) # need to fill first to divide
     transportPerTonPerDistance <- mbind(transportPerTonPerDistance,
@@ -99,10 +111,10 @@ calcTransportCosts <- function(transport = "all") { # nolint
   # Rename GTAP to MAgPIE commodities
   magpieComms <- unlist(cftRel)
 
-  transportMagpie <- transportPowerMagpie <- new.magpie(fill = 0,
+  transportMagpie <- transportPowerMagpie <- new.magpie(0,
                                                         cells_and_regions = getItems(transportPerTonPerDistance,
                                                                                      dim = 1),
-                                                        years = "y2005",
+                                                        years = yr,
                                                         names = magpieComms)
 
   for (i in getNames(transportMagpie)) {
@@ -120,7 +132,6 @@ calcTransportCosts <- function(transport = "all") { # nolint
                                  dim = 3.1, fill = 0)
   transportMagpie[, , c("wood", "woodfuel")] <- transportMagpie[, , "foddr"]
 
-  # what weight to add for these?
   transportPowerMagpie <- add_columns(transportPowerMagpie, addnm = c("pasture", "wood", "woodfuel"),
                                       dim = 3.1, fill = 0)
   transportPowerMagpie[, , c("wood", "woodfuel")] <- transportPowerMagpie[, , "foddr"]

README.md

@@ -1,6 +1,6 @@
 # madrat based MAgPIE Input Data Library
 
-R package **mrmagpie**, version **1.39.1**
+R package **mrmagpie**, version **1.39.5**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/mrmagpie)](https://cran.r-project.org/package=mrmagpie) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4319612.svg)](https://doi.org/10.5281/zenodo.4319612) [![R build status](https://github.com/pik-piam/mrmagpie/workflows/check/badge.svg)](https://github.com/pik-piam/mrmagpie/actions) [![codecov](https://codecov.io/gh/pik-piam/mrmagpie/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mrmagpie) [![r-universe](https://pik-piam.r-universe.dev/badges/mrmagpie)](https://pik-piam.r-universe.dev/builds)
 
@@ -39,16 +39,16 @@ In case of questions / problems please contact Kristine Karstens <karstens@pik-p
 
 To cite package **mrmagpie** in publications use:
 
-Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, Koeberle A, v. Jeetze P, Mishra A, Humpenoeder F, Sauer P (2023). _mrmagpie: madrat based MAgPIE Input Data Library_. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 1.39.1, <URL: https://github.com/pik-piam/mrmagpie>.
+Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, Köberle A, v. Jeetze P, Mishra A, Humpenoeder F, Sauer P (2023). _mrmagpie: madrat based MAgPIE Input Data Library_. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 1.39.5, <URL: https://github.com/pik-piam/mrmagpie>.
 
 A BibTeX entry for LaTeX users is
 
  ```latex
 @Manual{,
   title = {mrmagpie: madrat based MAgPIE Input Data Library},
-  author = {Kristine Karstens and Jan Philipp Dietrich and David Chen and Michael Windisch and Marcos Alves and Felicitas Beier and Alexandre Koeberle and Patrick {v. Jeetze} and Abhijeet Mishra and Florian Humpenoeder and Pascal Sauer},
+  author = {Kristine Karstens and Jan Philipp Dietrich and David Chen and Michael Windisch and Marcos Alves and Felicitas Beier and Alexandre Köberle and Patrick {v. Jeetze} and Abhijeet Mishra and Florian Humpenoeder and Pascal Sauer},
   year = {2023},
-  note = {R package version 1.39.1},
+  note = {R package version 1.39.5},
   doi = {10.5281/zenodo.4319612},
   url = {https://github.com/pik-piam/mrmagpie},
 }