Column names for the details-timeStep.txt and details-res-timeStep.txt (

#230)

* Add contributor and change version

* Changelog

* Add unit test for the column nmaes in output table

* Add .get_value_columns_details_file() to compute the column names of the output table

* Remove special characters to avoid warning

* Add control on the thematic-trimming parameter

* Factorization code

* Add some documentation for the function .get_value_columns_details_file()

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: antaresRead
 Type: Package
 Title: Import, Manipulate and Explore the Results of an 'Antares' Simulation
-Version: 2.6.1
+Version: 2.6.2
 Authors@R: c(
     person("Tatiana", "Vargas", email = "[email protected]", role = c("aut", "cre")),
     person("Jalal-Edine", "ZAWAM", role = "aut"),
@@ -13,6 +13,7 @@ Authors@R: c(
     person("Etienne", "Sanchez", role = "ctb"),
     person("Assil", "Mansouri", role = "ctb"),
     person("Clement", "Berthet", role = "ctb"),
+    person("Kamel", "Kemiha", role = "ctb"),
     person("RTE", role = "cph")
     )
 Description: Import, manipulate and explore results generated by 'Antares', a 

NEWS.md

@@ -1,5 +1,13 @@
 > Copyright © 2016 RTE Réseau de transport d’électricité
 
+# antaresRead 2.6.2 (devlopment)
+
+BUGFIXES :  
+
+* `readAntares()` :  
+  - returns the right column names for details-timeStep.txt and details-res-timeStep.txt 
+
+
 # antaresRead 2.6.1 (devlopment)
 
 BUGFIXES :  

R/importOutput.R

@@ -46,6 +46,7 @@
 #' is determined by the arguments "folder" and "file"
 #' - "areas", "values"  => areas
 #' - "areas", "details" => clusters
+#' - "areas", "details-res" => renewables clusters
 #' - "links", "values"  => links
 #'
 #' @return
@@ -277,6 +278,62 @@
   )
 }
 
+
+#' .get_value_columns_details_file
+#'
+#' Private function used to get the column names for the details-timeStep.txt or details-res-timeStep.txt.
+#' Used in .importOutputForClusters() and .importOutputForResClusters()
+#' From the opts, we detect which outputs the user decides to take 
+#'
+#' @return
+#' a vector
+#'
+#' @noRd
+#'
+.get_value_columns_details_file <- function(opts, type) {
+
+  if(type == "details") {
+    # Order is important. There is a correspondance between elements.
+    all_thematic_variables <- c("DTG by plant", "NP Cost by plant", "NODU by plant")
+    colNames <- c("production", "NP Cost", "NODU")
+    if (opts$antaresVersion >= 830){
+      all_thematic_variables <- c(all_thematic_variables, "Profit by plant")
+      colNames <- c(colNames, "profit")
+    }
+  } else if(type == "details-res") {
+    # Order is important. There is a correspondance between elements.
+    all_thematic_variables <- c("RES generation by plant")
+    colNames <- c("production")
+  }
+  # With thematic-trimming enabled
+  if (opts$parameters$general$`thematic-trimming`) {
+    if ("variables selection" %in% names(opts$parameters)) {
+      var_selection <- opts$parameters$`variables selection`
+      selection_type <- unique(names(var_selection))
+      allowed_selection_type <- c("select_var -", "select_var +")
+      # Filter the vector to avoid other properties (for example : selected_vars_reset)
+      selection_type <- intersect(selection_type, allowed_selection_type)
+      # List with a repeated name
+      var_selection <- var_selection[which(names(var_selection) == selection_type)]
+      selected_variables <- unlist(var_selection, use.names = FALSE)
+      # Index of the variables found in the section "variables selection"
+      idx_vars <- which(all_thematic_variables %in% selected_variables)
+      if (length(idx_vars) > 0) {
+        if (selection_type == "select_var -") {
+          # vars to remove
+          colNames <- colNames[-idx_vars]
+        } else if (selection_type == "select_var +") {
+          # vars to keep
+          colNames <- colNames[idx_vars]
+        }
+      }
+    }
+  }
+
+  return(colNames)
+}
+
+
 #' .importOutputForClusters
 #'
 #' Private function used to import the output for the thermal clusters of one area
@@ -295,6 +352,7 @@
   # To improve greatly the performance we use our knowledge of the position of 
   # the columns instead of using more general functions like dcast.
   reshapeFun <- function(x) {
+
     # Get cluster names
     n <- names(x)
     idx <- ! n %in% pkgEnv$idVars
@@ -304,28 +362,19 @@
     idVarsId <- which(!idx)
     idVarsNames <- n[idVarsId]
 
-    # Get final value columns
-    if (sum(idx) / length(clusterNames) == 4) {
-      colNames <- c("production", "NP Cost", "NODU", "profit")
-    } else if (sum(idx) / length(clusterNames) == 3) {
-      colNames <- c("production", "NP Cost", "NODU")
-    } else if (sum(idx) / length(clusterNames) == 2) {
-      colNames <- c("production", "NP Cost")
-    } else {
-      colNames <- c("production")
-    }
+    # Column names of the output table
+    colNames <- .get_value_columns_details_file(opts, "details")    
 
     # Loop over clusters
     nclusters <- length(clusterNames)
-    ncols <- length(colNames)
 
     res <- llply(1:nclusters, function(i) {
-      dt <- x[, c(nclusters * 0:(ncols - 1) + i, idVarsId), with = FALSE]
+      dt <- x[, c(nclusters * 0:(length(colNames) - 1) + i, idVarsId), with = FALSE]
       setnames(dt, c(colNames, idVarsNames))
       dt[, cluster := as.factor(clusterNames[i])]
       dt
     })
-    
+
     rbindlist(res)
   }
 
@@ -436,9 +485,9 @@
 
     res
   }
-
 }
 
+
 #' .importOutputForResClusters
 #'
 #' Private function used to import the output for the renewable clusters of one area
@@ -457,6 +506,7 @@
   # To improve greatly the performance we use our knowledge of the position of 
   # the columns instead of using more general functions like dcast.
   reshapeFun <- function(x) {
+
     # Get cluster names
     n <- names(x)
     idx <- ! n %in% pkgEnv$idVars
@@ -466,23 +516,14 @@
     idVarsId <- which(!idx)
     idVarsNames <- n[idVarsId]
 
-    # Get final value columns
-    # Get final value columns
-    # colNames <- c("resProduction")
-    if (sum(idx) / length(clusterNames) == 3) {
-      colNames <- c("production", "NP Cost", "NODU")
-    } else if (sum(idx) / length(clusterNames) == 2) {
-      colNames <- c("production", "NP Cost")
-    } else {
-      colNames <- c("production")
-    }
+    # Column names of the output table
+    colNames <- .get_value_columns_details_file(opts, "details-res")  
 
     # Loop over clusters
     nclusters <- length(clusterNames)
-    ncols <- length(colNames)
 
     res <- llply(1:nclusters, function(i) {
-      dt <- x[, c(nclusters * 0:(ncols - 1) + i, idVarsId), with = FALSE]
+      dt <- x[, c(nclusters * 0:(length(colNames) - 1) + i, idVarsId), with = FALSE]
       setnames(dt, c(colNames, idVarsNames))
       dt[, cluster := as.factor(clusterNames[i])]
       dt
@@ -496,10 +537,9 @@
                   mcYears, showProgress, opts, reshapeFun, sameNames = FALSE,
                   objectDisplayName = "clustersRe", parallel = parallel)
   )
-
-
 }
 
+
 #' .importOutputForBindingConstraints
 #'
 #' Private function used to import the output for binding constraints.

tests/testthat/test-readAntares.R

@@ -28,6 +28,16 @@ sapply(studyPathS, function(studyPath){
     expect_equal(nrow(clusters), 24 * 7 * nweeks * nrow(readClusterDesc()))
   })
 
+  test_that("Clusters importation column names are ok", {
+    clusters <- readAntares(clusters = opts$areasWithClusters,
+                            timeStep = "hourly",
+                            mcYears = "all",
+                            opts = opts,
+                            showProgress = FALSE)
+    expect_is(clusters, "data.table")
+    expect_equal(setdiff(colnames(clusters),pkgEnv$idVars), c("production", "NP Cost", "NODU"))
+  })
+
   test_that("importation of different objects works", {
     out <- readAntares(areas = opts$areaList, links=opts$linkList,
                        clusters=opts$areasWithClusters, showProgress= FALSE, timeStep = "annual")