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index 0000000..9864143
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index 0000000..a03a7bf
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index 0000000..c284518
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diff --git a/ConcordanceLibrary/WSA/WSA_Source2Root_WithLabelsV4.xlsx b/ConcordanceLibrary/WSA/WSA_Source2Root_WithLabelsV4.xlsx
new file mode 100644
index 0000000..e9c191e
Binary files /dev/null and b/ConcordanceLibrary/WSA/WSA_Source2Root_WithLabelsV4.xlsx differ
diff --git a/IEfeeds_code/Archive/Ind20Pro22v1_InitialEstimate.m b/IEfeeds_code/Archive/Ind20Pro22v1_InitialEstimate.m
new file mode 100644
index 0000000..4866435
--- /dev/null
+++ b/IEfeeds_code/Archive/Ind20Pro22v1_InitialEstimate.m
@@ -0,0 +1,49 @@
+function [RegMap,IndMap,ProdMap]=Ind20Pro22v1_InitialEstimate(handles)
+
+    disp('Launching initial estimate for the extended PIOT version 1');
+    
+    % Write handles variables into mat-file for R to read
+    % Working directory
+    filename = [handles.processeddatadir,'WorkingDirectory4R.mat'];
+    out = handles.motherALANGdir;
+    save(filename,'out');
+    
+    % Write region aggregator to file
+    filename = [handles.processeddatadir,'RegionAggFile4R.mat'];
+    out = handles.regionaggfile;
+    save(filename,'out');
+    
+    % Region aggregator
+    RegMap = csvread(handles.regionagg);
+    
+    if size(RegMap,1) > size(RegMap,2) % check orientation of aggregator
+        RegMap  = RegMap';
+    end
+    
+    reg_proxy = ones(size(RegMap,2),1);
+    RegMap = prorate(RegMap,'col_proxy',reg_proxy);
+    
+    % Product aggregator
+    ProdMap = csvread(handles.sectoraggprod);
+    
+    if size(ProdMap,1) > size(ProdMap,2) % check orientation of aggregator
+        ProdMap  = ProdMap';
+    end
+    
+    prod_proxy = ones(size(ProdMap,2),1);
+    ProdMap = prorate(ProdMap,'col_proxy',prod_proxy);
+    
+    % Industry aggregator 
+    IndMap = csvread(handles.sectoragg);
+    
+    if size(IndMap,1) > size(IndMap,2) % check orientation of aggregator
+        IndMap  = IndMap';
+    end
+    
+    ind_proxy = ones(size(IndMap,2),1);
+    IndMap = prorate(IndMap,'col_proxy',ind_proxy);
+    
+    command = 'Rscript /import/emily1/isa/IELab/Roots/PIOLab/Rscripts/IEfeeds_code/Ind20Pro22v1_InitialEstimate.R';
+    system(command,'-echo');
+        
+    end    
\ No newline at end of file
diff --git a/IEfeeds_code/Archive/Ind30Pro40v1_InitialEstimate.m b/IEfeeds_code/Archive/Ind30Pro40v1_InitialEstimate.m
new file mode 100644
index 0000000..7db8a82
--- /dev/null
+++ b/IEfeeds_code/Archive/Ind30Pro40v1_InitialEstimate.m
@@ -0,0 +1,49 @@
+function [RegMap,IndMap,ProdMap]=Ind30Pro40v1_InitialEstimate(handles)
+
+    disp('Launching initial estimate for the extended PIOT version 1');
+    
+    % Write handles variables into mat-file for R to read
+    % Working directory
+    filename = [handles.processeddatadir,'WorkingDirectory4R.mat'];
+    out = handles.motherALANGdir;
+    save(filename,'out');
+    
+    % Write region aggregator to file
+    filename = [handles.processeddatadir,'RegionAggFile4R.mat'];
+    out = handles.regionaggfile;
+    save(filename,'out');
+    
+    % Region aggregator
+    RegMap = csvread(handles.regionagg);
+    
+    if size(RegMap,1) > size(RegMap,2) % check orientation of aggregator
+        RegMap  = RegMap';
+    end
+    
+    reg_proxy = ones(size(RegMap,2),1);
+    RegMap = prorate(RegMap,'col_proxy',reg_proxy);
+    
+    % Product aggregator
+    ProdMap = csvread(handles.sectoraggprod);
+    
+    if size(ProdMap,1) > size(ProdMap,2) % check orientation of aggregator
+        ProdMap  = ProdMap';
+    end
+    
+    prod_proxy = ones(size(ProdMap,2),1);
+    ProdMap = prorate(ProdMap,'col_proxy',prod_proxy);
+    
+    % Industry aggregator 
+    IndMap = csvread(handles.sectoragg);
+    
+    if size(IndMap,1) > size(IndMap,2) % check orientation of aggregator
+        IndMap  = IndMap';
+    end
+    
+    ind_proxy = ones(size(IndMap,2),1);
+    IndMap = prorate(IndMap,'col_proxy',ind_proxy);
+    
+    command = 'Rscript /import/emily1/isa/IELab/Roots/PIOLab/Rscripts/IEfeeds_code/Ind30Pro40v1_InitialEstimate.R';
+    system(command,'-echo');
+        
+    end    
\ No newline at end of file
diff --git a/IEfeeds_code/IEsettings/IE_longnames.txt b/IEfeeds_code/IEsettings/IE_longnames.txt
index 31b7c0c..eba8d88 100644
--- a/IEfeeds_code/IEsettings/IE_longnames.txt
+++ b/IEfeeds_code/IEsettings/IE_longnames.txt
@@ -1,2 +1 @@
-IE feed from R for various regions with 20 industries and 22 products
 IE feed from R for various regions with 30 industries and 39 products
\ No newline at end of file
diff --git a/IEfeeds_code/IEsettings/IE_shortnames.txt b/IEfeeds_code/IEsettings/IE_shortnames.txt
index ae1da5f..fc19c2b 100644
--- a/IEfeeds_code/IEsettings/IE_shortnames.txt
+++ b/IEfeeds_code/IEsettings/IE_shortnames.txt
@@ -1,2 +1 @@
-Ind20Pro22v1
 Ind30Pro39v1
\ No newline at end of file
diff --git a/IEfeeds_code/datafeed_PIOLab_InitialEstimate.m b/IEfeeds_code/datafeed_PIOLab_InitialEstimate.m
index 08b4ba8..563952e 100644
--- a/IEfeeds_code/datafeed_PIOLab_InitialEstimate.m
+++ b/IEfeeds_code/datafeed_PIOLab_InitialEstimate.m
@@ -3,10 +3,6 @@
 % Master file for IE feeds
 
     if handles.nonsurvey == 1
-
-       [RegMap,IndMap,ProdMap]=Ind20Pro22v1_InitialEstimate(handles);
-       
-    elseif handles.nonsurvey == 2    
     
        [RegMap,IndMap,ProdMap]=Ind30Pro39v1_InitialEstimate(handles);
 
diff --git a/Rscripts/IEfeeds_code/Archive/Ind20Pro22v1_InitialEstimate.R b/Rscripts/IEfeeds_code/Archive/Ind20Pro22v1_InitialEstimate.R
new file mode 100644
index 0000000..80fa928
--- /dev/null
+++ b/Rscripts/IEfeeds_code/Archive/Ind20Pro22v1_InitialEstimate.R
@@ -0,0 +1,173 @@
+###############################################
+#                                             #
+#   This is the IE data feed for processing   #
+#     raw data of the iron and steel PIOT     #
+#     it covers 20 processes and 22 products  #
+#                                             #
+###############################################
+# IE feed for 20 industries/processes and 22 products base classifications
+# hanspeter.wieland@wu.ac.at (c)
+# 02.19.2020 
+
+# In case the code is executed not on the server (and the GUI) for debugging, 
+# the user can choose the desired region aggregator by setting the following variable
+# either to 5,35 or 49. If test_regagg is not defined it will be set automatically to 
+# 5 regions later on in the code.
+# test_regagg <- "049"
+
+################################################################################
+# 1. Set up environment for building the initial estimate
+
+IEdatafeed_name <- "Ind20Pro22v1" 
+print(paste0("Start of ",IEdatafeed_name," InitialEstimate."))
+
+# Set library path when running on suphys server
+if(Sys.info()[1] == "Linux"){
+  .libPaths("/suphys/hwie3321/R/x86_64-redhat-linux-gnu-library/3.5")
+  # Define location for root directory
+  root_folder <- "/import/emily1/isa/IELab/Roots/PIOLab/"}else{
+  root_folder <- "C:/Users/hwieland/Github workspace/PIOLab/"}
+
+# Initializing R script (load R packages and set paths to folders etc.)
+source(paste0(root_folder,"Rscripts/Subroutines/InitializationR.R"))
+
+# Read base regions, products and codes from mat-file if available
+source(paste0(path$root,"Rscripts/Subroutines/Read_BaseClassification.R"))
+
+# Read region aggregation from classification to set the right path for the IE data
+if(max(base$region$Code) < 10) {regagg <- paste0("00",max(base$region$Code))} else
+{regagg <- paste0("0",max(base$region$Code))}
+
+# Set additional paths that are specific to the present run
+path[["IE_Subroutines"]] <- paste0(path$root,"Rscripts/IEfeeds_code/IE_subroutines")
+path[["IE_Processed"]] <- paste0(path$root,"ProcessedData/",IEdatafeed_name,"/",regagg)
+path[["Agg_Processed"]] <- paste0(path$root,"ProcessedData/",IEdatafeed_name)
+
+remove(regagg)
+
+# Check whether output folder for processed data for the present initial estimate exists, if not then create it
+if(!dir.exists(path$Agg_Processed)) dir.create(path$Agg_Processed)
+
+# Check whether output folder for processed data for the specific aggregation exists, if yes, delete it
+if(dir.exists(path$IE_Processed)) unlink(path$IE_Processed,recursive = TRUE) 
+dir.create(path$IE_Processed)
+
+# Check if ALANG files of old initial estimate exist and delete
+source(paste0(path$Subroutines,"/DeleteALANGfilesOfOldIEfeeds.R"))
+
+################################################################################
+# 2. Commencing data feeds
+
+# Loading production values for semi- and finished steel + information on yields
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_WSA.R"))
+IEFeed_PIOLab_WSA(year,path)
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_SteelIndustryYields.R"))
+IEFeed_PIOLab_SteelIndustryYields(path)
+
+# Loading the trade data
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_BACI.R"))
+IEFeed_PIOLab_BACI(year,path)
+
+# The extraction and ore grade feed
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_IRP.R"))
+IEFeed_PIOLab_IRP(year,path)
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_Grades.R"))
+IEFeed_PIOLab_Grades(path)
+
+# Loading end-of-life steel scrap
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_EOL.R"))
+IEFeed_PIOLab_EOL(year,path)
+
+# Loading energy data
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_IEA.R"))
+IEFeed_PIOLab_IEA(year,path)
+
+# Loading and aggregating EXIOBASE Waste-MFA IO version
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_EXIOWasteMFAIO.R"))
+IEFeed_PIOLab_EXIOWasteMFAIO(year,path)
+
+# Loading fabrication yields taken from Cullen et al 2012
+source(paste0(path$IE_Subroutines,"/IEFeed_PIOLab_Cullen.R"))
+IEFeed_PIOLab_Cullen(path)
+
+################################################################################
+# 3. Commencing data processing
+
+# Aligning WSA and IEA data and filling gaps in WSA accounts. 
+# Moreover, remove BACI trade flows of iron ores for regions where IRP reports no extraction
+source(paste0(path$IE_Subroutines,"/IEDataProcessing_PIOLab_AligningData.R"))
+IEDataProcessing_PIOLab_AligningData(year,path)
+
+# Compile extension for the MFA-Waste IO Model and estimate fabrication scrap
+source(paste0(path$IE_Subroutines,"/IEDataProcessing_PIOLab_WasteMFAIOExtension.R"))
+IEDataProcessing_PIOLab_WasteMFAIOExtension(year,path)
+
+# Run Waste-IO Model calculation
+source(paste0(path$IE_Subroutines,"/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R"))
+IEDataProcessing_PIOLab_WasteMFAIOModelRun(year,path)
+
+# Compile domestic SUTs
+source(paste0(path$IE_Subroutines,"/IEDataProcessing_PIOLab_BuildingDomesticTables.R"))
+IEDataProcessing_PIOLab_BuildingDomesticTables(year,path)
+
+# Compiling trade blocks
+source(paste0(path$IE_Subroutines,"/IEDataProcessing_PIOLab_BuildingTradeBlocks.R"))
+IEDataProcessing_PIOLab_BuildingTradeBlocks(year,path)
+
+################################################################################
+# 4. Create S8 files (see AISHA manual annex for further information) for easy data import to AISHA 
+   
+source(paste0(path$IE_Subroutines,"/IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables.R"))
+IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables(year,path)
+
+################################################################################
+# 5. Write ALANG commands
+print("Start writing ALANG commands.")
+
+# Set up wrapper for adding rows to ALANG
+NewALANG <- function(name,SE,ALANG)
+{
+  file <- paste0("S8 ",path$mother,"Data/IE/",gsub("-","",Sys.Date()),
+                 "_PIOLab_AllCountriesS8File_",name,year,".csv")
+  
+  ALANG <- add_row(ALANG,'1' = name,Coef1 = file, S.E. = SE,
+                   Value = "I",Incl = "Y",Parts = "1",'Row parent' = "",'Row child' = "",
+                   'Row grandchild' = "",'Column parent' = "",'Column child' = "",
+                   'Column grandchild' = "",Years = "",Margin = "",'Pre-map' = "",'Post-map' = "",
+                   'Pre-Map' = "",'Post-Map' = "")
+  
+  return(ALANG)
+}
+
+n_reg <- nrow(base$region)
+# Create empty file with header
+source(paste0(path$Subroutines,"/makeALANGheadline.R"))
+
+# Write ALANG commands
+ALANG <- NewALANG("Supply","E MX1;MN10;",ALANG)
+ALANG <- NewALANG("Use","E MX1;MN10;CN1;",ALANG)
+ALANG <- NewALANG("FinalDemand","E MX1;MN10;",ALANG)
+ALANG <- NewALANG("Extraction","E MX1;MN10;",ALANG)
+ALANG <- NewALANG("EolScrap","E MX1;MN10;",ALANG)
+ALANG <- NewALANG("OtherInput","E MX1;MN10;",ALANG)
+ALANG <- NewALANG("Waste","E MX1;MN10;",ALANG)
+ALANG <- NewALANG("Zero","E CN1;",ALANG)
+
+ALANG$`#` <- as.character(1:nrow(ALANG))
+
+# Write data frame with ALANG commands as tab-delimited txt-file to root and working directory (mother)
+# Note HP: This is probably not the normal procedure, meaning no IE ALANG's in the root
+filename <-  paste0(path$root,"ALANGfiles/",gsub("-","",Sys.Date()),
+                    "_PIOLab_SUT_000_InitialEstimate-",year,"_000_S8filesForAllRegionsAndFlows.txt")
+
+write.table(ALANG,file = filename,row.names = FALSE, quote = F,sep = "\t")
+# Check if the mother directory really exists
+if(file.exists(path$mother))
+{ 
+  filename <-  paste0(path$mother,gsub("-","",Sys.Date()),
+                      "_PIOLab_SUT_000_InitialEstimate-",year,"_000_S8filesForAllRegionsAndFlows.txt")
+  
+  write.table(ALANG,file = filename,row.names = FALSE, quote = F,sep = "\t") 
+}
+  
+print(paste0("End of ",IEdatafeed_name," InitialEstimate."))
diff --git a/Rscripts/IEfeeds_code/Archive/Ind30Pro40v1_InitialEstimate.R b/Rscripts/IEfeeds_code/Archive/Ind30Pro40v1_InitialEstimate.R
new file mode 100644
index 0000000..8bf59d0
--- /dev/null
+++ b/Rscripts/IEfeeds_code/Archive/Ind30Pro40v1_InitialEstimate.R
@@ -0,0 +1,232 @@
+###############################################
+#                                             #
+#   This is the IE data feed for processing   #
+#     raw data of the iron and steel PIOT     #
+#     it covers 20 processes and 22 products  #
+#                                             #
+###############################################
+# IE feed for 20 industries/processes and 22 products base classifications
+# hanspeter.wieland@wu.ac.at (c)
+# 02.19.2020 
+
+# In case the code is executed not on the server (and the GUI) for debugging, 
+# the user can choose the desired region aggregator by setting the following variable
+# either to 5,35 or 49. If test_regagg is not defined it will be set automatically to 
+# 5 regions later on in the code.
+# test_regagg <- "049"
+
+################################################################################
+# 1. Set up environment for building the initial estimate
+
+IEdatafeed_name <- "Ind30Pro40v1" 
+
+print(paste0("Start of ",IEdatafeed_name," InitialEstimate."))
+
+# Set library path when running on suphys server
+if(Sys.info()[1] == "Linux"){
+  .libPaths("/suphys/hwie3321/R/x86_64-redhat-linux-gnu-library/3.5")
+  # Define location for root directory
+  root_folder <- "/import/emily1/isa/IELab/Roots/PIOLab/"}else{
+  root_folder <- "C:/Users/hwieland/Github workspace/PIOLab/"}
+
+# Initializing R script (load R packages and set paths to folders etc.)
+
+source(paste0(root_folder,"Rscripts/Subroutines/InitializationR.R"))
+
+# Read base regions, products and codes from mat-file if available
+
+source(paste0(path$root,"Rscripts/Subroutines/Read_BaseClassification.R"))
+
+# Read region aggregation from classification to set the right path for the IE data
+
+if(max(base$region$Code) < 10) 
+  {
+    regagg <- paste0("00",max(base$region$Code))
+  } else
+  {
+    regagg <- paste0("0",max(base$region$Code))
+  }
+
+# Set additional paths that are specific to the present run
+path[["IE_Subroutines"]] <- paste0(path$root,"Rscripts/IEfeeds_code/IE_subroutines")
+path[["IE_Processed"]] <- paste0(path$root,"ProcessedData/",IEdatafeed_name,"/",regagg)
+path[["Agg_Processed"]] <- paste0(path$root,"ProcessedData/",IEdatafeed_name)
+
+remove(regagg)
+
+# Check whether output folder for processed data for the present initial estimate exists, if not then create it
+
+if(!dir.exists(path$Agg_Processed)) dir.create(path$Agg_Processed)
+
+# Check whether output folder for processed data for the specific aggregation exists, if yes, delete it
+
+if(dir.exists(path$IE_Processed)) unlink(path$IE_Processed,recursive = TRUE) 
+
+dir.create(path$IE_Processed)
+
+# Check if ALANG files of old initial estimate exist and delete
+
+source(paste0(path$Subroutines,"/DeleteALANGfilesOfOldIEfeeds.R"))
+
+# Functions to process raw data for Initial Estimate
+
+IE_fun <- list("/IEFeed_PIOLab_WSA.R",
+               "/IEFeed_PIOLab_SteelIndustryYields.R",
+               "/IEFeed_PIOLab_BACI.R",
+               "/IEFeed_PIOLab_IRP.R",
+               "/IEFeed_PIOLab_Grades.R",
+               "/IEFeed_PIOLab_EOL.R",
+               "/IEFeed_PIOLab_IEA.R",
+               "/IEFeed_PIOLab_EXIOWasteMFAIOV2.R",
+               "/IEFeed_PIOLab_Cullen.R",
+               "/IEDataProcessing_PIOLab_AligningDataV2.R",
+               "/IEDataProcessing_PIOLab_WasteMFAIOExtensionV2.R",
+               "/IEDataProcessing_PIOLab_WasteMFAIOModelRunV2.R",
+               "/IEDataProcessing_PIOLab_BuildingDomesticTablesV2.R",
+               "/IEDataProcessing_PIOLab_BuildingTradeBlocks.R",
+               "/IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables.R")
+
+IE_fun <- paste0(path$IE_Subroutines,IE_fun)  # Add path to functions
+
+lapply(IE_fun,source)  # Load functions into workspace
+
+
+# 2. Commencing data pre-processing (from source to root classification)
+
+IEFeed_PIOLab_WSA(year,path)  # WSA production numbers
+
+IEFeed_PIOLab_SteelIndustryYields(path)  # Yield information
+
+IEFeed_PIOLab_BACI(year,path)  # Trade data
+
+IEFeed_PIOLab_IRP(year,path)  # Extraction data
+
+IEFeed_PIOLab_Grades(path)  # Ore grades
+
+IEFeed_PIOLab_EOL(year,path)  # end-of-life i.e. old scrap
+
+IEFeed_PIOLab_IEA(year,path) # IEA energy data
+
+IEFeed_PIOLab_EXIOWasteMFAIOV2(year,path) # Aggregate EXIOBASE Waste-MFA IO model (WIO)
+
+IEFeed_PIOLab_Cullen(path)  # Fabrication yields from Cullen et al. 2012
+
+
+# 3. Commencing data processing
+
+IEDataProcessing_PIOLab_AligningDataV2(year,path) # Align WSA, IRP extraction
+
+IEDataProcessing_PIOLab_WasteMFAIOExtensionV2(year,path) # Compile extension for WIO  
+
+IEDataProcessing_PIOLab_WasteMFAIOModelRunV2(year,path) # Run WIO Model calculation
+
+
+
+# Compile domestic SUTs
+
+IEDataProcessing_PIOLab_BuildingDomesticTablesV2(year,path)
+
+# Compiling trade blocks
+
+IEDataProcessing_PIOLab_BuildingTradeBlocks(year,path)
+
+################################################################################
+# 4. Create S8 files (see AISHA manual annex for further information) for easy data import to AISHA 
+   
+IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables(year,path)
+
+################################################################################
+# 5. Write ALANG commands
+print("Start writing ALANG commands.")
+
+# Set up wrapper for adding rows to ALANG
+NewALANG <- function(name,SE,ALANG)
+{
+  file <- paste0("S8 ",path$mother,"Data/IE/",gsub("-","",Sys.Date()),
+                 "_PIOLab_AllCountriesS8File_",name,year,".csv")
+  
+  ALANG <- add_row(ALANG,'1' = name,Coef1 = file, S.E. = SE,
+                   Value = "I",Incl = "Y",Parts = "1",'Row parent' = "",'Row child' = "",
+                   'Row grandchild' = "",'Column parent' = "",'Column child' = "",
+                   'Column grandchild' = "",Years = "",Margin = "",'Pre-map' = "",'Post-map' = "",
+                   'Pre-Map' = "",'Post-Map' = "")
+  
+  return(ALANG)
+}
+
+# n_reg <- nrow(base$region)
+# Create empty file with header
+source(paste0(path$Subroutines,"/makeALANGheadline.R"))
+
+RSE <- read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/IE_RSE_settings.xlsx"))
+
+# Write ALANG commands
+
+name <- "Supply"
+RSE_sel <- RSE[RSE$item == name,]
+ALANG <- NewALANG(name,
+                  paste0("E MX",RSE_sel$MX,";MN",RSE_sel$MN,";"),
+                  ALANG)
+
+name <- "Use"
+RSE_sel <- RSE[RSE$item == name,]
+ALANG <- NewALANG(name,
+                  paste0("E MX",RSE_sel$MX,";MN",RSE_sel$MN,";CN",RSE_sel$CN,";"),
+                  ALANG)
+
+name <- "FinalOutput"
+RSE_sel <- RSE[RSE$item == name,]
+ALANG <- NewALANG(name,
+                  paste0("E MX",RSE_sel$MX,";MN",RSE_sel$MN,";"),
+                  ALANG)
+
+name <- "Extraction"
+RSE_sel <- RSE[RSE$item == name,]
+ALANG <- NewALANG(name,
+                  paste0("E MX",RSE_sel$MX,";MN",RSE_sel$MN,";"),
+                  ALANG)
+
+name <- "EolScrap"
+RSE_sel <- RSE[RSE$item == name,]
+ALANG <- NewALANG(name,
+                  paste0("E MX",RSE_sel$MX,";MN",RSE_sel$MN,";"),
+                  ALANG)
+
+name <- "OtherInput"
+RSE_sel <- RSE[RSE$item == name,]
+ALANG <- NewALANG(name,
+                  paste0("E MX",RSE_sel$MX,";MN",RSE_sel$MN,";"),
+                  ALANG)
+
+name <- "Waste"
+RSE_sel <- RSE[RSE$item == name,]
+ALANG <- NewALANG(name,
+                  paste0("E MX",RSE_sel$MX,";MN",RSE_sel$MN,";"),
+                  ALANG)
+
+name <- "Zero"
+RSE_sel <- RSE[RSE$item == name,]
+ALANG <- NewALANG(name,
+                  paste0("E CN",RSE_sel$CN,";"),
+                  ALANG)
+
+ALANG$`#` <- as.character(1:nrow(ALANG))
+
+# Write data frame with ALANG commands as tab-delimited txt-file to root and working directory (mother)
+# Note HP: This is probably not the normal procedure, meaning no IE ALANG's in the root
+filename <-  paste0(path$root,"ALANGfiles/",gsub("-","",Sys.Date()),
+                    "_PIOLab_SUT_000_InitialEstimate-",year,"_000_S8filesForAllRegionsAndFlows.txt")
+
+write.table(ALANG,file = filename,row.names = FALSE, quote = F,sep = "\t")
+
+# Check if the mother directory really exists
+
+if(file.exists(path$mother))
+{ 
+  filename <-  paste0(path$mother,gsub("-","",Sys.Date()),
+                      "_PIOLab_SUT_000_InitialEstimate-",year,"_000_S8filesForAllRegionsAndFlows.txt")
+  
+  write.table(ALANG,file = filename,row.names = FALSE, quote = F,sep = "\t") 
+}
+  
+print(paste0("End of ",IEdatafeed_name," InitialEstimate."))
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_AligningData.R b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_AligningData.R
new file mode 100644
index 0000000..faa4c52
--- /dev/null
+++ b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_AligningData.R
@@ -0,0 +1,148 @@
+###########################################
+#                                         #
+#   This function fills the gap of the    #
+#   of the WorldSteels (WSA) production   #
+#   numbers using the IEA energy data.    #
+#   Moreover, BACI trade data is aligned  #
+#   with IRP ore extraction data          #
+#                                         #  
+###########################################
+
+IEDataProcessing_PIOLab_AligningData <- function(year,path)
+{
+  print("IEDataProcessing_PIOLab_AligningData initiated.")
+  
+  ##############################################################################
+  
+  # 1. Loading data
+  
+  # Loading processed WSA production numbers
+  
+  Pig <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_PigIron.csv"))
+  
+  Flat <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_FlatRolledProducts.csv"))
+  
+  colnames(Flat)[2] <- "Flat"
+  
+  Long <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_LongRolledProducts.csv"))
+  
+  colnames(Long)[2] <- "Long"
+  
+  BOF_steel <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOxygenBlownConverters.csv"))
+  
+  EAF_steel <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelElectricFurnaces.csv"))
+  
+  colnames(EAF_steel)[2] <- "EAF"
+  
+  
+  # Loading IE_Processed IEA energy data
+  BF_gas <- read.csv(paste0(path$IE_Processed,"/IEA/IEA_",year,"_BlastFurnaceGas.csv"))
+  colnames(BF_gas)[2] <- "Energy"
+  BOF_gas <- read.csv(paste0(path$IE_Processed,"/IEA/IEA_",year,"_BlastOxygenFurnaceGas.csv"))
+  colnames(BOF_gas)[2] <- "Energy"
+  Energy_con <- read.csv(paste0(path$IE_Processed,"/IEA/IEA_",year,"_SteelIndustryEnergyConsumption.csv"))
+  
+  # Loading BACI trade data on iron ores and IRP extraction accounts
+  BACI <- read.csv(file = paste0(path$IE_Processed,"/BACI/BACI_",year,".csv"))
+  IronOre_Extraction <- read.csv(file = paste0(path$IE_Processed,"/IRP/IRP_",year,".csv"))
+  
+  ##############################################################################
+  # 2. Filling gaps in WSA production accounts by checking IEA energy data
+  # 2.1 WSA pig iron production vs. production of (IEA) blast furnace gas
+  
+  # Following the WSA publication "Energy use in the steel industry" (p98) between 
+  # 4.3 and 7.2 GJ of blast furnace gas is produced per ton of pig iron.
+  # In the following we fill the gaps assuming an average intensity of 5.7 GJ/t
+  
+  # Merge the two data sets and filter for missing production values
+  Pig_vs_BF <- full_join(Pig,BF_gas,c("base"),copy = FALSE) %>% 
+    mutate(Intensity = Energy/Quantity)
+  Pig_vs_BF_gap <-  Pig_vs_BF %>% filter(Quantity == 0 & Energy != 0)
+  
+  # Fill in values and save new pig iron production data
+  if(length(Pig_vs_BF_gap) > 0) {
+    Pig_vs_BF$Quantity[Pig_vs_BF$base %in% Pig_vs_BF_gap$base] <- Pig_vs_BF_gap$Energy / 5.7 
+    Pig <- Pig_vs_BF %>% select(base,Quantity) 
+    write.csv(Pig, file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_PigIron.csv"),row.names = FALSE)}
+  
+  remove(Pig_vs_BF,Pig_vs_BF_gap,Pig,BF_gas)
+  
+  # 2.2 BOF steel production vs. BOF gas production
+  
+  # Again according to the WSA report on energy, the blown oxygen furnace steelmaking
+  # process produces gases of approx. 0.234 GJ per crude steel (p101). Please note that the 
+  # WSA case studies (p104) showed values betwene 0.4 and 0.8 GJ per ton
+  
+  Steel_vs_BOF <- full_join(BOF_steel,BOF_gas,c("base"),copy = FALSE) %>% 
+    mutate(Intensity = Energy/Quantity)
+  Steel_vs_BOF_gap <- Steel_vs_BOF %>% filter(Quantity == 0 & Energy != 0)
+  
+  # Fill in values and save new BOF steel production data
+  if(nrow(Steel_vs_BOF_gap) > 0) {
+    Steel_vs_BOF$Quantity[Steel_vs_BOF$base %in% Steel_vs_BOF_gap$base] <- Steel_vs_BOF_gap$Energy / 0.234 
+    BOF_steel <- Steel_vs_BOF %>% select(base,Quantity) 
+    write.csv(BOF_steel, file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOxygenBlownConverters.csv"),row.names = FALSE)}
+  
+  remove(Steel_vs_BOF,Steel_vs_BOF_gap,BOF_gas)
+  colnames(BOF_steel)[2] <- "BOF"
+  
+  # 2.3 Filling gaps for rolled steel products
+  Steel_vs_energy <- full_join(Energy_con,Flat,c("base"),copy = FALSE)
+  Steel_vs_energy <- full_join(Steel_vs_energy,Long,c("base"),copy = FALSE) 
+  Steel_vs_energy <- full_join(Steel_vs_energy,BOF_steel,c("base"),copy = FALSE)
+  Steel_vs_energy <- full_join(Steel_vs_energy,EAF_steel,c("base"),copy = FALSE)
+  Steel_vs_energy[is.na(Steel_vs_energy)] <- 0
+  Steel_vs_energy <- mutate(Steel_vs_energy,Rolled = Flat+Long,Intensity = Value/Rolled)
+  Steel_vs_energy$Intensity[Steel_vs_energy$Intensity == Inf] <- 0
+  
+  # The reference plant, of the WSA report, has an average energy intensity for
+  # long rolled products of approx. 20 GJ per ton and 9.5 GJ per ton for scrap based long products 
+  # WSA referenced flat rolling plate mill has an energy intensity of 24 GJ per ton of product (p120-122)
+  # Please note that these numbers refer to the total energy requirement including 
+  # upstream energy use for steel and iron making. Direct energy use in rolling mills is much smaller at around 2-7 GJ/t
+  
+  # 2.3.1 Filter for countries with inconsistent data
+  # No flat rolling reported but BOF steel is produced 
+  Gap <- Steel_vs_energy %>% filter(Flat == 0 & BOF != 0)
+  if(nrow(Gap) > 0) Steel_vs_energy$Flat[Steel_vs_energy$base %in% Gap$base] <- Gap$BOF
+  
+  # No long rolling reported but EAF steel is porduced
+  Gap <- Steel_vs_energy %>% filter(Long == 0 & EAF != 0)
+  if(nrow(Gap) > 0) Steel_vs_energy$Long[Steel_vs_energy$base %in% Gap$base] <- Gap$EAF
+  
+  # BOF steel reported but flat rolled products + energy intensity is below the threshold
+  Gap <- Steel_vs_energy %>% filter(BOF == 0 & Flat != 0 & Intensity < 20)
+  if(nrow(Gap) > 0) Steel_vs_energy$BOF[Steel_vs_energy$base %in% Gap$base] <- Gap$Flat
+  
+  # No flat and long rolling reported but energy use data available
+  Gap <- Steel_vs_energy %>% filter(Flat == 0 & Long == 0 & Value != 0)
+  if(nrow(Gap) > 0) {
+    # Assuming 18 GJ/ton
+    filling <- Gap$Value/18
+    Steel_vs_energy[Steel_vs_energy$base %in% Gap$base,c("Flat","Long","BOF","EAF")] <- filling/2 }
+  
+  ##############################################################################
+  # 3. Delete BACI trade flows of iron ores where IRP reports no extraction
+  colnames(IronOre_Extraction)[2] <- "Extraction"
+  # Code of countries with no extraction
+  sel <- setdiff(base$region$Code,IronOre_Extraction$base)
+  # Set zero
+  BACI$Quantity[BACI$From %in% sel & BACI$Product == 1] <- 0
+  BACI <- BACI[BACI$Quantity != 0,]
+  ##############################################################################
+  # 4. Writing manipulated data to folder
+  Flat <- select(Steel_vs_energy,base,Flat) 
+  Long <- select(Steel_vs_energy,base,Long)
+  BOF <- select(Steel_vs_energy,base,BOF)
+  EAF <- select(Steel_vs_energy,base,EAF)
+  colnames(Flat)[2] <- colnames(Long)[2] <- colnames(BOF)[2] <- colnames(EAF)[2] <- "Quantity"
+  
+  write.csv(Flat,file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_FlatRolledProducts.csv"),row.names = FALSE)
+  write.csv(Long,file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_LongRolledProducts.csv"),row.names = FALSE)
+  write.csv(BOF,file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOxygenBlownConverters.csv"),row.names = FALSE)
+  write.csv(EAF,file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelElectricFurnaces.csv"),row.names = FALSE)
+  write.csv(BACI,file = paste0(path$IE_Processed,"/BACI/BACI_",year,".csv"),row.names = FALSE)
+  
+  print("IEDataProcessing_PIOLab_AligningData finished.")
+  
+}
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_BuildingDomesticTables.R b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_BuildingDomesticTables.R
new file mode 100644
index 0000000..5084b04
--- /dev/null
+++ b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_BuildingDomesticTables.R
@@ -0,0 +1,396 @@
+#####################################
+#                                   #
+#   This code writes the data       #
+#   into the domestic SUTs that     # 
+#   can be used as an input for     # 
+#               AISHA               #
+#                                   #  
+#####################################
+
+
+IEDataProcessing_PIOLab_BuildingDomesticTables <- function(year,path)
+{
+  print("IEDataProcessing_PIOLab_BuildingDomesticTables initiated.")
+  # Load region aggregator
+  source(paste0(path$Subroutines,"/Root2Base_RegionAggregator.R"))
+  reg_agg <- Root2Base_RegionAggregator(RegionAggregator)
+  # Load function to compile end use map
+  source(paste0(path$Subroutines,"/makeEndUseMap.R"))
+  # Load slag rate of blast furnace and coefficient per ton of pig iron output
+  WSA_yield <- read.csv(paste0(path$IE_Processed,"/WSA/SteelIndustryYields.csv")) %>%
+    select(Process,Average)
+  # Load extension of the WasteMFAIO model and IO codes
+  load(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Q.RData"))
+  load(paste0(path$IE_Processed,"/EXIOWasteMFAIO/IO.codes.RData"))
+  # Load trade data
+  BACI <- read.csv(paste0(path$IE_Processed,"/BACI/BACI_",year,".csv"))
+  # Load allocation function
+  source(paste0(path$Subroutines,"/AllocateSupply2Use.R"))
+  
+  # Define general variables
+  n_pro <- nrow(base$product)
+  n_ind <- nrow(base$industry)
+  n_reg <- nrow(base$region)
+  n_va <- 5
+  n_fd <- 3
+  
+  for(i in 1:n_reg)
+  {
+    print(i)
+    # Create empty SUT
+    SUT <- data.frame(matrix(0,(n_pro+n_ind+n_va),(n_pro+n_ind+n_fd)))
+    colnames(SUT) <- c(base$industry$Name,base$product$Name,
+                       "FinalConsumption","Landfill","Atmosphere")
+    rownames(SUT) <- c(base$industry$Name,base$product$Name,
+                       "FerrousMinerals","EOLScrap","Limestone","Coke","Air")
+    SUT <- as.matrix(SUT)
+    ############################################################################
+    # 1. Writing boundary inputs of iron ore extraction and end-of-life scrap to table
+    
+    # 1.1 EoL scrap 
+    data <- read.csv(paste0(path$IE_Processed,"/EOL/EOL_",year,".csv"))
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["EOLScrap","Scrap preparation"] <- value }
+    
+    # 1.2 IRP iron ore extraction i.e. inputs from nature
+    data <- read.csv(paste0(path$IE_Processed,"/IRP/IRP_",year,".csv"))
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["FerrousMinerals","Mining"] <- value }
+    
+    ############################################################################
+    # 2. Writing production values into supply table
+    
+    # 2.1 Waste (gangue) output of mining
+    # Read iron ore grades and assume beneficiation to 60% of ore grade
+    data <- read.csv(paste0(path$IE_Processed,"/Grades/IronOreGrades.csv"))  
+    
+    # if no ore grades are available for a region, assume 0.6
+    if(i %in% data$base) 
+    {concen <- data$Concentration[data$base == i]}else  
+    {concen <- 0.6}
+    
+    if(SUT["FerrousMinerals","Mining"] > 0) {
+    iron <- SUT["FerrousMinerals","Mining"]*concen
+    # iron ore weight with 60% grade, when concentration is higher than 0.6, leave it like it is
+    if(concen <= 0.6) {value_new <- iron/0.6} else
+    {value_new <- iron/concen}
+    # Calculate waste flow to landfill 
+    waste <- value-value_new
+    # Allocate output of iron ore (now 60% grade) to mining
+    SUT["Mining","Iron ore"] <- value_new
+    # Allocate boundary outputs of waste to the supply-side of mining, that is landfill
+    SUT["Mining","Landfill"] <- waste }
+    
+    # 2.2 Write pig iron production and use yields to estimate waste/slag flows
+    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_PigIron.csv"))
+    
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["Blast furnace","Pig iron"] <- value
+      # Take slag rate from WSA
+      yield <- WSA_yield$Average[WSA_yield$Process == 'BF slag rate']/1000
+      # Estimate slag output and allocate to landfill (final use)
+      SUT["Blast furnace","Landfill"] <- value * yield
+      # Estimate output of top gas (1.7 tons per ton oig iron)
+      SUT["Blast furnace","Atmosphere"] <- value *1.7}
+    
+    # 2.3 Sponge iron
+    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SpongeIron.csv"))
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["Direct reduction","Sponge iron"] <- value
+      # Allocate slag output assuming losses of 5% of sponge iron output
+      #yield <- 0.05
+      #waste <- value * yield
+      # Alternatively allocate losses including gangue by assuming waste of 600 kg per ton of useful output
+      SUT["Direct reduction","Landfill"] <- value * 0.6 }
+      
+    # 2.4 Write steel production of BOF & OHF
+    data_1 <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOxygenBlownConverters.csv"))
+    data_2 <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOpenHearthFurnaces.csv"))
+    data <- rbind(data_1,data_2) %>% group_by(base) %>% summarise(Quantity = sum(Quantity))
+    remove(data_1,data_2)
+    
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["Oxygen blown & open hearth furnace","Liquid steel (OBF/OHF)"] <- value
+      # Calculate waste
+      yield <- WSA_yield$Average[WSA_yield$Process == 'BOF yield']
+      waste <- (value/yield)-value
+      SUT["Oxygen blown & open hearth furnace","Landfill"] <- waste }
+    
+    # 2.5 Write steel production of EAF
+    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelElectricFurnaces.csv"))
+    
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["Electric arc furnace","Liquid steel (EAF)"] <- value
+      # Calculate waste
+      yield <- WSA_yield$Average[WSA_yield$Process == 'EAF yield']
+      waste <- (value/yield)-value
+      SUT["Electric arc furnace","Landfill"] <- waste }
+    
+    # 2.6 Write flat rolling production and forming scrap
+    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_FlatRolledProducts.csv"))
+    
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["Flat rolling","Flat rolled products"] <- value
+      # Calculate waste
+      yield <- WSA_yield$Average[WSA_yield$Process == 'Slab caster yield']
+      waste <- (value/yield)-value
+      SUT["Flat rolling","Forming & fabrication scrap"] <- waste }
+    
+    # 2.7 Write long rolling production and forming scrap
+    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_LongRolledProducts.csv"))
+    
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["Long rolling","Long rolled products"] <- value
+      # Calculate waste
+      yield <- WSA_yield$Average[WSA_yield$Process == 'Bloom caster yield']
+      waste <- (value/yield)-value
+      SUT["Long rolling","Forming & fabrication scrap"] <- waste }
+    
+    # 2.8 Write production of ingots and subsequently fill the gap of the casting
+    # process with slabs (because all feeds into flat rolling)
+    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_Ingots.csv"))
+    
+    if(i %in% data$base) {
+      value <- data$Quantity[data$base == i]
+      SUT["Casting of ingots & slabs","Ingots"] <- value
+      value <- SUT["Flat rolling","Flat rolled products"] - SUT["Casting of ingots & slabs","Ingots"]
+      SUT["Casting of ingots & slabs","Slabs"] <- value
+      yield <- WSA_yield$Average[WSA_yield$Process == 'Slab caster yield']
+      value <- SUT["Flat rolling","Flat rolled products"]
+      waste <- (value/yield)-value
+      SUT["Casting of ingots & slabs","Forming & fabrication scrap"] <- waste }
+    
+    # 2.9 For the production value of billets and blooms use the value of long rolling
+      value <- sum(SUT["Long rolling",c("Long rolled products","Forming & fabrication scrap")])
+      SUT["Casting of billets & blooms","Billets & blooms"] <- value
+      # Estimate scrap flows using yield data
+      yield <- WSA_yield$Average[WSA_yield$Process == 'Bloom caster yield']
+      waste <- (value/yield)-value
+      SUT["Casting of billets & blooms","Forming & fabrication scrap"] <- waste 
+      
+    # 2.10 Fabrication i.e. manufacturing output
+    data <- read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_FabricationToFinalDemand.csv")) %>%
+      filter(From.Region == i) %>% select(From.Product,Quantity) %>% group_by(From.Product) %>%
+      summarise(Quantity = sum(Quantity))
+    # Set up index to write values directly into cells
+    index <- cbind(base$industry$Name[11:20],base$product$Name[13:22])
+    SUT[index] <- data$Quantity
+      
+    # 2.11 Fabrication scarp
+    data <- read.csv(paste0(path$IE_Processed,"/FabricationScrap/FabricationScrap_",year,".csv")) %>%
+      filter(base == i)
+    data <- data.frame("commodity" = as.character(data$commodity),
+                       "Quantity" = data$Quantity,
+                       stringsAsFactors = FALSE)
+    index <- as.data.frame(index,stringsAsFactors = FALSE)
+    colnames(index) <- c("Industry","Product")
+    data <- left_join(index,data,c("Product" = "commodity"),copy = FALSE)
+    
+    SUT[data$Industry,"Forming & fabrication scrap"] <- data$Quantity
+
+    # 2.12 Estimate production of scrap steel by summing over "forming and fabrication scrap"
+    # and EoL scrap supply
+    
+    value <- sum(SUT[,"Forming & fabrication scrap"]) + SUT["EOLScrap","Scrap preparation"]
+    SUT["Scrap preparation","Scrap steel"] <- value  
+    
+    ############################################################################
+    # 3. Filling use table
+    # IMPORTANT: The allocation follows the logic to source data on exports from the
+    # BACI data set and subtract this value from the domestic production of the upstream process 
+    # to get an estimate for the domestic inputs of each process. Where this results in negative values
+    # for domestic use, we assume that inputs are 50% from foreign source
+    
+    # 3.1 Steel in final demand
+    data <- read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_SteelInFinalDemand.csv")) %>%
+      filter(From.Region == i & To.Region == i)
+    index <- base$product$Name[base$product$Code %in% data$Product]
+    SUT[index,"FinalConsumption"] <- data$Quantity
+    
+    # 3.2.1 Flat Rolled products used in fabrication
+    item <- "Flat rolled products"
+    # Load map to allocate to end use (fabrication)
+    map <- makeEndUseMap(i,"Flat")
+    map <- map[order(map$index),]
+    share <- map$Share
+    ############################################
+    # The following command may cause problems #
+    ############################################
+    users <- base$industry$Code[11:20][map$index]
+    # Load trade data 
+    data <- filter(BACI,Product == base$product$Code[base$product$Name == item]) %>% select(From,To,Quantity)
+    # Read domestic production
+    pro <- SUT["Flat rolling","Flat rolled products"]
+    # Read domestic use
+    use <- sum(Q["Flat",IO.codes$index[IO.codes$base == i]])
+    
+    # Execute function for estimation script to write values
+    SUT <- AllocateSupply2Use(SUT,item,share,users,data,pro,use,i)
+    
+    # 3.3.2 Long Rolled products used in fabrication
+    item <- "Long rolled products"
+    # Load map to allocate to end use (fabrication)
+    map <- makeEndUseMap(i,"Long")
+    map <- map[order(map$index),]
+    share <- map$Share
+    users <- base$industry$Code[11:20][map$index]
+    # Load trade data 
+    data <- filter(BACI,Product == base$product$Code[base$product$Name == item]) %>% select(From,To,Quantity)
+    # Read domestic production
+    pro <- SUT["Long rolling",item]
+    # Read domestic use
+    use <- sum(Q["Long",IO.codes$index[IO.codes$base == i]])
+    
+    # Execute function for estimation script to write values
+    SUT <- AllocateSupply2Use(SUT,item,share,users,data,pro,use,i)
+    
+    # 3.4 Use of forming and fabrication scrap by scrap preparation
+    SUT["Forming & fabrication scrap","Scrap preparation"] <- sum(SUT[,"Forming & fabrication scrap"])
+    
+    # 3.5 Use of billets and blooms by long rolling
+    # Load number for exports of billets and blooms 
+    item <- "Billets & blooms"
+    users <- "Long rolling"
+    share <- 1
+    data <- filter(BACI,Product == base$product$Code[base$product$Name == item]) %>% select(From,To,Quantity)
+    pro <- SUT["Casting of billets & blooms",item]
+    use <- sum(SUT[users,])
+    
+    # Execute function for estimation script to write values
+    SUT <- AllocateSupply2Use(SUT,item,share,users,data,pro,use,i)
+    
+    # 3.6 Use of ingots and slabs by flat rolling
+    # 3.6.1 Ingots
+    export <- filter(BACI,Product == base$product$Code[base$product$Name == "Ingots"]) %>% 
+      select(From,To,Quantity) %>% filter(From == i) 
+    value <- SUT["Casting of ingots & slabs","Ingots"] - sum(export$Quantity)
+    # In case value is negative, assume that 5% of inputs is coming from domestic ingots
+    if(value > 0) {SUT["Ingots","Flat rolling"] <- value} else
+    {SUT["Ingots","Flat rolling"] <- sum(SUT["Flat rolling",]) * 0.05}
+    
+    # 3.6.2 Slabs
+    export <-  filter(BACI,Product == base$product$Code[base$product$Name == "Slabs"]) %>% 
+      select(From,To,Quantity) %>% filter(From == i) 
+    # Domestic production of slabs minus exports = domestic use
+    value <- SUT["Casting of ingots & slabs","Slabs"] - sum(export$Quantity)
+    if(value > 0) {SUT["Slabs","Flat rolling"] <- value} else
+    {SUT["Slabs","Flat rolling"] <-  sum(SUT["Flat rolling",]) * 0.45}
+    
+    # 3.7 Casting of billets and blooms and casting of ingots and slabs
+    # The only input to casting is liquid steel which is not a traded commodity, 
+    # The inputs to casting are therefor estimated by using the production/output value
+    # of the respective furnace
+    
+    # Steel from Electric arc furnace
+    SUT["Liquid steel (EAF)","Casting of billets & blooms"] <- SUT["Electric arc furnace","Liquid steel (EAF)"]
+    # Fill remaining gap with OBF steel   
+    gap <- sum(SUT["Casting of billets & blooms",]) - SUT["Liquid steel (EAF)","Casting of billets & blooms"]
+    if(gap > 0) SUT["Liquid steel (OBF/OHF)","Casting of billets & blooms"] <- gap
+    
+    # Steel from oxygen blown converter  
+    SUT["Liquid steel (OBF/OHF)","Casting of ingots & slabs"] <- sum(SUT["Casting of ingots & slabs",])
+    
+    # 3.8 Sponge iron used by EAF
+    export <- filter(BACI,Product == base$product$Code[base$product$Name == "Sponge iron"],From == i)
+    
+    if(SUT["Direct reduction","Sponge iron"] != 0) 
+    {
+      value <- SUT["Direct reduction","Sponge iron"] - sum(export$Quantity)
+      if(value > 0) {SUT["Sponge iron","Electric arc furnace"] <- value} else
+      {SUT["Sponge iron","Electric arc furnace"] <- sum(SUT["Direct reduction","Sponge iron"])/2}
+    }
+    
+    # 3.9 Allocating iron ores used by direct reduction
+    SUT["Iron ore","Direct reduction"] <- sum(SUT["Direct reduction",])
+    
+    # 3.10 Allocate iron ores used by Blast furnace 
+    export <- filter(BACI,Product == base$product$Code[base$product$Name == "Iron ore"],From == i)
+    value <- SUT["Mining","Iron ore"] - sum(export$Quantity)
+    if(value > 0) {SUT["Iron ore","Blast furnace"] <- value} else
+    {SUT["Iron ore","Blast furnace"] <- sum(SUT["Blast furnace",])/2}
+    
+    # 3.10.1
+    # Allocate other inputs used by blast furnace
+    if(SUT["Blast furnace","Pig iron"] > 0)
+    {
+      pro <- SUT["Blast furnace","Pig iron"]
+      # Limestone and fluxes assuming 300 kg per ton of pig iron
+      SUT["Limestone","Blast furnace"] <- pro *0.3
+      # 600 kg coke or coal per ton pig iron
+      SUT["Coke","Blast furnace"] <- pro * 0.6
+      # (Hot) air of approx. 800 kg per ton
+      SUT["Air","Blast furnace"] <- pro * 0.8
+    }
+    
+    # 3.11 Allocating pig iron to BOF
+    export <- filter(BACI,Product == base$product$Code[base$product$Name == "Pig iron"],From == i)
+    value <- SUT["Blast furnace","Pig iron"] - sum(export$Quantity)
+    if(value > 0) {SUT["Pig iron","Oxygen blown & open hearth furnace"] <- value} else
+    {SUT["Pig iron","Oxygen blown & open hearth furnace"] <- sum(SUT["Oxygen blown & open hearth furnace",])*0.4}
+    
+    # 3.12 Allocate Scrap steel to BOF and EAF
+    SUT["Scrap steel","Oxygen blown & open hearth furnace"] <- sum(SUT["Oxygen blown & open hearth furnace",])*0.15
+    SUT["Scrap steel","Electric arc furnace"] <- sum(SUT["Electric arc furnace",]) - SUT["Direct reduction","Electric arc furnace"]
+    
+    # 4. Save SUT
+    # Check if subfolder in processed data exists and if not create it
+    path_set <- paste0(path$IE_Processed,"/SUT")
+    if(!dir.exists(path_set)) dir.create(path_set)
+    # Setting decimals to two digits
+    SUT <- round(SUT,2)
+    print(min(SUT))
+    # Decompose SUT into single elements, that is supply, use, final demand, inputs from nature and eol scrap
+    Use <- SUT[base$product$Name,base$industry$Name]
+    Supply <- SUT[base$industry$Name,base$product$Name]
+    # What is final demand in monetary IO is called hereafter boundary output
+    BoundaryOutput <- SUT[c(base$industry$Name,base$product$Name),c("FinalConsumption","Landfill","Atmosphere")]
+    
+    # What is value added in monetary IO is called hereafter boundary input
+    pi <- c("FerrousMinerals","EOLScrap","Air","Limestone","Coke")
+    BoundaryInput <- matrix(SUT[pi,base$industry$Name],length(pi),nrow(base$industry))
+    
+    # Remove all column and row names
+    colnames(Use) <- NULL
+    rownames(Use) <- NULL
+    colnames(Supply) <- NULL
+    rownames(Supply) <- NULL
+    colnames(BoundaryOutput) <- NULL
+    rownames(BoundaryOutput) <- NULL
+    colnames(BoundaryInput) <- NULL
+    rownames(BoundaryInput) <- NULL
+    
+    # Write Supply, Use, In- and Output to root folder, note that because write.csv will always export colnames
+    # had to use the write.tabel function.
+    
+    write.table(Supply,file = paste0(path_set,"/",year,"_DomesticSupply_Region",i,".csv"),
+                col.names = FALSE,
+                row.names = FALSE,
+                sep = ",")
+    
+    write.table(Use,file = paste0(path_set,"/",year,"_DomesticUse_Region",i,".csv"),
+                col.names = FALSE,
+                row.names = FALSE,
+                sep = ",")
+    
+    write.table(BoundaryOutput,file = paste0(path_set,"/",year,"_BoundaryOutput_Region",i,".csv"),
+                col.names = FALSE,
+                row.names = FALSE,
+                sep = ",")
+    
+    write.table(BoundaryInput,file = paste0(path_set,"/",year,"_BoundaryInput_Region",i,".csv"),
+                col.names = FALSE,
+                row.names = FALSE,
+                sep = ",")
+    
+    print(paste0("Minimum value: ",min(SUT)))
+  }
+  print("IEDataProcessing_PIOLab_BuildingDomesticTables finished.")
+}
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_WasteMFAIOExtension.R b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_WasteMFAIOExtension.R
new file mode 100644
index 0000000..de6c7c4
--- /dev/null
+++ b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_WasteMFAIOExtension.R
@@ -0,0 +1,149 @@
+###############################################
+#                                             #
+#   This code uses the data on fabrication    #
+#   yields and products flows to end-use to   # 
+#     build the IO extension and estimate     # 
+#     the amount of fabrication scrap         #
+#                                             #
+###############################################
+
+
+IEDataProcessing_PIOLab_WasteMFAIOExtension <- function(year,path)
+{
+  print("IEDataProcessing_PIOLab_WasteMFAIOExtension initiated.")
+  #############################################################################
+  
+  # Load IO codes of aggregated EXIOBASE data
+  load(paste0(path$IE_Processed,"/EXIOWasteMFAIO/IO.codes.RData"))
+  
+  # Load function to create allocation map
+  source(paste0(path$Subroutines,"/makeEndUseMap.R"))
+  # 1. Defining functions
+  
+  # aggregation function
+  Agg <- function(x)
+  {
+    x <- as.matrix(x) %*% sapply(unique(colnames(x)),"==",colnames(x))
+    return(x)
+  }
+  
+  # Writing values of rolled products into arrays (extension and fabrication scrap)
+  WriteValues2Array <- function(data,name,Q,Scrap)
+  {
+    for(i in 1:nrow(data))
+    {
+      #print(i)
+      reg <- data$base[i]
+      
+      # call function to create specific map 
+      map <- makeEndUseMap(reg,name)
+      
+      map <- map %>% mutate(Value = Share * data$Quantity[data$base == reg]) %>% 
+        select(index,IO,Yield.Sector,Value) 
+      
+      map <- left_join(map,Yields,c("Yield.Sector"),copy = FALSE) %>% mutate(EndUse = Value*Yield.Factor,Scrap = Value - EndUse) %>%
+        select(index,IO,EndUse,Scrap)
+      
+      index <- map$index+((reg-1)*10)
+      
+      if(name == "Flat")
+      {
+        Q[1,index] <<- map$EndUse
+        Scrap[1,index] <<- map$Scrap
+      }else
+      {
+        Q[2,index] <<- map$EndUse
+        Scrap[2,index] <<- map$Scrap
+      }
+    }
+    
+  }
+  
+  # Load processed data from Cullen et al 2012
+  Yields <- read.csv(paste0(path$IE_Processed,"/Cullen/FabricationYields.csv"))
+
+  ##############################################################################
+  # 1. The first section calculates the net use of rolled steel products in regions
+  # by adding imports and subtracting exports from the WSA production values
+  
+  BACI <- read.csv(paste0(path$IE_Processed,"/BACI/BACI_",year,".csv")) # Load trade data
+  
+  # Load finished steel production
+  Flat <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_FlatRolledProducts.csv"))
+  Long <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_LongRolledProducts.csv"))
+   
+  
+  
+  Flat_trade <- BACI %>% filter(Product == base$product$Code[base$product$Name == "Flat rolled products"]) %>% 
+    select(From,To,Quantity)
+  
+  Long_trade <- BACI %>% filter(Product == base$product$Code[base$product$Name == "Long rolled products"]) %>% 
+    select(From,To,Quantity)
+  
+  # Estimate the net-use of products in countries
+  Flat_import <- Flat_trade %>% group_by(To) %>% summarise(Quantity = sum(Quantity))
+  Flat_export <- Flat_trade %>% group_by(From) %>% summarise(Quantity = sum(Quantity))
+  Flat_trade <- full_join(Flat_import,Flat_export,c("To" = "From"),copy = FALSE)
+  colnames(Flat_trade) <- c("base","import","export")
+  # Regional production + imports - exports = net-use
+  Flat <- full_join(Flat,Flat_trade,c("base"),copy = FALSE) 
+  Flat[is.na(Flat)] <- 0
+  
+  Flat <- Flat %>% mutate(NetUse = Quantity + import - export) 
+  
+  #####################################
+  # When negatives: set net use to import value
+  Flat$NetUse[Flat$NetUse < 0] <- Flat$import[Flat$NetUse < 0] 
+  Flat <- Flat %>% select(base,NetUse)
+  colnames(Flat)[2] <- "Quantity"
+  #####################################
+  
+  Long_import <- Long_trade %>% group_by(To) %>% summarise(Quantity = sum(Quantity))
+  Long_export <- Long_trade %>% group_by(From) %>% summarise(Quantity = sum(Quantity))
+  Long_trade <- full_join(Long_import,Long_export,c("To" = "From"),copy = FALSE)
+  colnames(Long_trade) <- c("base","import","export")
+  # Regional production + imports - exports = net-use
+  Long <- full_join(Long,Long_trade,c("base"),copy = FALSE) 
+  Long[is.na(Long)] <- 0
+  
+  Long <- Long %>% mutate(NetUse = Quantity + import - export) 
+  #####################################
+  # When negatives: set net use to import value
+  Long$NetUse[Long$NetUse < 0] <- Long$import[Long$NetUse < 0] 
+  #####################################
+  Long <- Long %>% select(base,NetUse)
+  colnames(Long)[2] <- "Quantity"
+  
+  ##############################################################################
+  # 3. Start processing
+  
+  # Create two empty arrays for steel for the extension (Q) and for the fabrication scrap
+  Scrap <- Q <- as.data.frame(matrix(0,2,nrow(IO.codes)))
+  rownames(Scrap) <- rownames(Q) <- c("Flat","Long")
+  
+  # Execute functions
+  # Warnings are turned of for the run.
+  options(warn = -1)
+  WriteValues2Array(Flat,"Flat",Q,Scrap)
+  WriteValues2Array(Long,"Long",Q,Scrap)
+  options(warn = 0)
+
+  # Save extension of the MFA-Waste-IO Model
+  save(Q,file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Q.RData"))
+  
+  # Prepate fabrication scrap data for export
+  Scrap <- colSums(Scrap)
+  Scrap <- data.frame(IO.codes,
+                     "Quantity" = Scrap,
+                     stringsAsFactors = FALSE)
+  
+  # Check if subfolder in processed data exists and if not create it
+  path_set <- paste0(path$IE_Processed,"/FabricationScrap")
+  if(!dir.exists(path_set)) dir.create(path_set)
+  
+  # Write to folder
+  write.csv(Scrap,file = paste0(path_set,"/FabricationScrap_",year,".csv"),row.names = FALSE)
+  
+  print("IEDataProcessing_PIOLab_WasteMFAIOExtension finished.")
+  
+}
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R
new file mode 100644
index 0000000..85ec43c
--- /dev/null
+++ b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R
@@ -0,0 +1,141 @@
+#########################################
+#                                       #
+#   This code runs the Waste-IO model   #
+#       and saves the data in the       #
+#         appropriate folders           #
+#                                       #
+#########################################
+
+IEDataProcessing_PIOLab_WasteMFAIOModelRunV2 <- function(year,path)
+{
+  
+  print("IEDataProcessing_PIOLab_WasteMFAIOModelRunV2 initiated.")
+  
+  
+  # Load codes and indices of model:
+  
+  IO.codes <- read.csv( paste0(path$IE_Processed,"/EXIOWasteMFAIO/IO_codes.csv"))
+  
+  IO.codes["Key"] <- paste0(IO.codes$base,"-",IO.codes$sector)
+  
+  Y.codes <- read.csv( paste0(path$IE_Processed,"/EXIOWasteMFAIO/Y_codes.csv"))
+  
+  Y.codes["Key"] <- paste0(Y.codes$base,"-",Y.codes$sector)
+  
+  
+  # Load model variables:
+  
+  Q <- as.matrix( read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Q.csv"),
+                           header = FALSE) )
+  
+  L <- as.matrix( read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_L.csv"),
+                           header = FALSE) )
+  
+  Y <- as.matrix( read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Y.csv"),
+                           header = FALSE) )
+  
+  x <- rowSums( L %*% Y )  # Calculate gross output
+  
+  # Calculate multipliers
+  
+  E <- colSums(Q)/x
+  
+  E[is.na(E)] <- 0
+  
+  MP <- L*E
+  
+  # 1. Estimate flows from fabrication to final product
+  
+  FP <- MP %*% diag( rowSums(Y) )
+  
+  colnames(FP) <- rownames(FP) <- IO.codes$Key
+  
+  FP <- melt(FP)  # Transfrom from wide to long format (pivot-like list) 
+  
+  colnames(FP) <- c("From","To","Quantity")
+  
+  FP[,c("From","To")] <- apply(FP[,c("From","To")], c(2), as.character)
+  
+  
+  # Differentiate sector and region codes (from and to):
+  
+  FP <- left_join( x = FP, y = select(IO.codes,base,sector,Key), 
+                   by = c("From" = "Key"), 
+                   copy = FALSE, 
+                   suffix = c(".from",".to") ) 
+  
+  FP <- left_join( x = FP, y = select(IO.codes,base,sector,Key), 
+                   by = c("To" = "Key"), 
+                   copy = FALSE, 
+                   suffix = c(".from",".to") ) 
+  
+  Fabrication2Final <- select(FP,-From,-To) # Remove key (not needed anymore)
+  
+  
+  # 2. Estimate steel in final demand
+  
+  
+  FP <- Y # Create object to store data
+  
+  # Set col and row names:
+  
+  colnames(FP) <- paste0(Y.codes$base,"-",Y.codes$demand)
+  
+  rownames(FP) <- paste0(IO.codes$base,"-",IO.codes$sector)
+  
+  FP[1:nrow(Y),1:ncol(Y)] <- 0  # Set values to zero
+  
+  
+  # Calculate flows:
+  
+  for( i in 1:ncol(Y) ) FP[,i] <- rowSums( t(MP)*Y[,i] )
+  
+  
+  FP <- melt(FP)  # Transfrom from wide to long format (pivot-like list) 
+  
+  colnames(FP) <- c("From","To","Quantity")
+  
+  FP[,c("From","To")] <- apply(FP[,c("From","To")], c(2), as.character)
+  
+  
+  # Differentiate sector and region codes (from and to):
+  
+  FP <- left_join( x = FP, y = select(IO.codes,base,sector,Key), 
+                   by = c("From" = "Key"), 
+                   copy = FALSE, 
+                   suffix = c(".from",".to") ) 
+  
+  FP <- left_join( x = FP, y = select(IO.codes,base,sector,Key), 
+                   by = c("To" = "Key"), 
+                   copy = FALSE, 
+                   suffix = c(".from",".to") ) 
+  
+  Fabrication2Final <- select(FP,-From,-To) # Remove key (not needed anymore)
+  
+  
+  
+  
+  colSums(SteelInFinalDemand)/1000000
+  
+  
+  sum(SteelInFinalDemand)
+  
+  SteelInFinalDemand <- tibble::rownames_to_column(SteelInFinalDemand, "Key")
+  SteelInFinalDemand <- melt(SteelInFinalDemand,id.vars = "Key")
+  colnames(SteelInFinalDemand) <- c("Key","To.Region","Quantity")
+  # Warning messages are turned off for the following join
+  options(warn = -1)
+  SteelInFinalDemand <- left_join(SteelInFinalDemand,IO.codes,c("Key"),copy = FALSE) %>%
+    select(base,To.Region,commodity,Quantity)
+  options(warn = 0)
+  colnames(SteelInFinalDemand)[c(1,3)] <- c("From.Region","Commodity")
+  SteelInFinalDemand <- left_join(SteelInFinalDemand,base$product,c("Commodity" = "Name"),copy = FALSE) %>%
+    select(From.Region,To.Region,Code,Quantity)
+  colnames(SteelInFinalDemand)[3] <- "Product"
+  
+  # 4. Save results to folder 
+  write.csv(FabricationToFinal,file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_FabricationToFinalDemand.csv"),row.names = FALSE)
+  write.csv(SteelInFinalDemand,file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_SteelInFinalDemand.csv"),row.names = FALSE)
+  
+  print("DataProcessing_PIOLab_WasteMFAIOModelRun finished.")
+}
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEFeed_PIOLab_EXIOWasteMFAIO.R b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEFeed_PIOLab_EXIOWasteMFAIO.R
new file mode 100644
index 0000000..baf9954
--- /dev/null
+++ b/Rscripts/IEfeeds_code/IE_subroutines/Archive/IEFeed_PIOLab_EXIOWasteMFAIO.R
@@ -0,0 +1,146 @@
+#################################################
+#                                               #
+# This Feed takes EXIOBASE 3.6 and aggregating  #
+#     the database in order to apply the        #
+#           MFA-Waste-IO approach               #
+#                                               #
+#################################################
+
+
+IEFeed_PIOLab_EXIOWasteMFAIO <- function(year,path)
+{
+  print("IEFeed_PIOLab_EXIOWasteMFAIO initiated.")
+  # Function for aggregating the IOT
+  Agg <- function(x)
+  {
+    x <- as.matrix(x) %*% sapply(unique(colnames(x)),"==",colnames(x))
+    return(x)
+  }
+  
+  # 1. Loading the original EXIOBASE 3.6 variables
+  # Transaction matrix:
+  load(paste0(path$Raw,"/EXIOWasteMFAIO/",year,"_Z.RData"))
+  # Final demand:
+  load(paste0(path$Raw,"/EXIOWasteMFAIO/",year,"_Y.RData"))
+  # IO sector codes:
+  load(paste0(path$Raw,"/EXIOWasteMFAIO/","IO.codes.RData")) 
+  IO.codes <- IO.codes %>% select(Index,Product.Name,Country.Code)
+  regions <- unique(IO.codes$Country.Code)
+  # Final demand codes
+  load(paste0(path$Raw,"/EXIOWasteMFAIO/","Y.codes.RData"))
+  
+  # Load the root classifciation
+  root_class <- root$region %>% select(Code,ISO2digitCode)
+  
+  # Load region aggregator
+  source(paste0(path$Subroutines,"/Root2Base_RegionAggregator.R"))
+  
+  # Load source-to-root and calculate Source2Base concordances
+  Source2Root <- read.csv(paste0(path$Concordance,"/EXIOBASE/Source2Root_Region_EXIOBASE.csv"),
+                          header = FALSE)
+  
+  Root2Base <- as.matrix(RegionAggregator)
+    
+  Source2Root <- as.matrix(Source2Root)
+  # Create map from concordance
+  Source2Root <- Source2Root/rowSums(Source2Root)
+  Root2Base <- as.matrix(Root2Base)
+  # Create Source-to-base matrix
+  Source2Base <- Source2Root %*% Root2Base
+  
+  # Create concordance
+  Source2Base <- t(Source2Base)*(1:ncol(Source2Base))
+  rownames(Source2Base) <- NULL
+  Source2Base <- melt(Source2Base)
+  Source2Base <- filter(Source2Base,value > 0) %>% select(-value)
+  colnames(Source2Base) <- c("base","root")
+  
+  IO.codes <- data.frame(IO.codes,"NewProducts" = "NEC",stringsAsFactors = FALSE)
+  IO.codes["base"] <- rep(Source2Base$base,each = 200)
+  
+  # select steel containing commodities
+  steel.id <- c(117:125,150)
+  IO.codes$NewProducts[IO.codes$Product.Name %in% IO.codes$Product.Name[steel.id]] <- IO.codes$Product.Name[IO.codes$Product.Name %in% IO.codes$Product.Name[steel.id]] 
+
+  # Create key for aggregation
+  IO.codes <- data.frame(IO.codes,
+                         "Key" = paste0(as.character(IO.codes$base),"§",IO.codes$NewProducts),
+                         stringsAsFactors = FALSE)
+  
+  # Aggregate transaction matrix
+  colnames(Z) <- IO.codes$Key
+  Z <- Agg(Z)
+  Z <- t(Z)
+  colnames(Z) <- IO.codes$Key
+  Z <- Agg(Z)
+  Z <- t(Z)
+
+  # Aggregate final demand
+  Y.codes["base"] <- rep(Source2Base$base,each = 7) 
+  colnames(Y) <- Y.codes$base
+  Y <- Agg(Y)
+  Y <- t(Y)
+  colnames(Y) <- IO.codes$Key
+  Y <- Agg(Y)
+  Y <- t(Y)
+  # Rearrange columns so that the region code of the base table is in increasing order 
+  Y <- Y[,as.character(1:max(Source2Base$base))]
+  
+  # Create new IO.codes to rearrange the variables so that base region codes increases
+  IO.codes <- colnames(Z)
+  IO.codes <- as.data.frame(str_split_fixed(IO.codes, "§", 2),stringsAsFactors = FALSE)
+  colnames(IO.codes) <- c("base","commodity")
+  storage.mode(IO.codes$base) <- "numeric"
+  IO.codes <- IO.codes[order(IO.codes$base),]
+  IO.codes$index <- 1:nrow(IO.codes)
+  IO.codes$Key <- paste0(as.character(IO.codes$base),"§",IO.codes$commodity)
+  IO.codes <- IO.codes[,c("index","base","commodity","Key")]
+  
+  # New Y.codes 
+  Y.codes <- data.frame("base" = 1:max(Source2Base$base),
+                        "Region.Name" = base$region$Name, 
+                        stringsAsFactors = FALSE)
+  
+  # Rearrange Z & Y (if necessary)
+  Z <- Z[,IO.codes$Key]
+  Z <- t(Z)
+  Z <- Z[,IO.codes$Key]
+  Y <- t(Y)
+  Y <- Y[,IO.codes$Key]
+  Y <- t(Y)
+
+  # Compute new variables of the aggreagted table
+  x <- rowSums(Z) + rowSums(Y)
+  A <- t(t(Z)/x)
+  A[is.na(A)] <- 0
+  
+  # Delete input/flows not containing steel
+  A[IO.codes$index[IO.codes$commodity == "NEC"],] <- 0
+  A[,IO.codes$index[IO.codes$commodity == "NEC"]] <- 0
+  L <- solve(diag(nrow(A))-A)
+  
+  # Construct new final demand (now only steel-containing products) 
+  Y[IO.codes$index[IO.codes$commodity == "NEC"],] <- 0
+
+  # Remove NEC product from WIO model
+  L <- L[IO.codes$index[IO.codes$commodity != "NEC"],IO.codes$index[IO.codes$commodity != "NEC"]]
+  Y <- Y[IO.codes$index[IO.codes$commodity != "NEC"],]
+  x <- rowSums(L%*%Y)
+  IO.codes <- IO.codes[IO.codes$commodity != "NEC",]
+  IO.codes$index <- 1:nrow(IO.codes)
+  IO.codes$WIOcode <- 1:length(unique(IO.codes$commodity))
+  IO.codes$base.product <- base$product$Code[base$product$Name  %in% IO.codes$commodity]
+  
+  # Check if subfolder in processed data exists and if not create it
+  path_set <- paste0(path$IE_Processed,"/EXIOWasteMFAIO")
+  if(!dir.exists(path_set)) dir.create(path_set)
+  
+  write.table(L,file = paste0(path_set,"/",year,"_L.csv"),row.names = FALSE,col.names = FALSE,sep = ",")
+  write.table(Y,file = paste0(path_set,"/",year,"_Y.csv"),row.names = FALSE,col.names = FALSE,sep = ",")
+  write.table(x,file = paste0(path_set,"/",year,"_x.csv"),row.names = FALSE,col.names = FALSE,sep = ",")
+  save(IO.codes,file = paste0(path_set,"/IO.codes.RData"))
+  save(Y.codes,file = paste0(path_set,"/Y.codes.RData"))
+  
+  print("IEFeed_PIOLab_EXIOWasteMFAIO finished.")
+  
+}
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_AligningData.R b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_AligningData.R
index faa4c52..2237f78 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_AligningData.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_AligningData.R
@@ -1,148 +1,89 @@
-###########################################
-#                                         #
-#   This function fills the gap of the    #
-#   of the WorldSteels (WSA) production   #
-#   numbers using the IEA energy data.    #
-#   Moreover, BACI trade data is aligned  #
-#   with IRP ore extraction data          #
-#                                         #  
-###########################################
+################################################################################
+# This function uses WSA data to estimate forging output and 
+# aligns trade data with IRP extraction
+# It differs from version 1 in not filling the gaps in WSA by comparing with IEA
+# Author: hanspeter.wieland@wu.ac.at
+# Date: 26.03.2020
 
 IEDataProcessing_PIOLab_AligningData <- function(year,path)
 {
+  
   print("IEDataProcessing_PIOLab_AligningData initiated.")
   
-  ##############################################################################
+  # Loading BACI trade data and IRP extraction accounts:
+  
+  BACI <- read.csv(file = paste0(path$IE_Processed,"/BACI/BACI_",year,".csv"))
   
-  # 1. Loading data
+  IronOre <- read.csv(file = paste0(path$IE_Processed,"/IRP/IRP_",year,".csv"))
   
-  # Loading processed WSA production numbers
+  colnames(IronOre)[2] <- "Extraction"
   
-  Pig <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_PigIron.csv"))
+  sel <- setdiff(base$region$Code,IronOre$base) # Countries with no extraction
   
-  Flat <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_FlatRolledProducts.csv"))
+  BACI$Quantity[BACI$From %in% sel & BACI$Product == 1] <- 0  # Set zero
   
-  colnames(Flat)[2] <- "Flat"
+  BACI <- BACI[BACI$Quantity != 0,] # Filter regions with non-zero values
   
-  Long <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_LongRolledProducts.csv"))
+  # Write to folder:
   
-  colnames(Long)[2] <- "Long"
+  write.csv(BACI,file = paste0(path$IE_Processed,"/BACI/BACI_",year,".csv"),
+            row.names = FALSE)
   
-  BOF_steel <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOxygenBlownConverters.csv"))
+  remove(IronOre,sel) # Delete objects not needed anymore
   
-  EAF_steel <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelElectricFurnaces.csv"))
   
-  colnames(EAF_steel)[2] <- "EAF"
+  # 2. Estimate forgings
   
   
-  # Loading IE_Processed IEA energy data
-  BF_gas <- read.csv(paste0(path$IE_Processed,"/IEA/IEA_",year,"_BlastFurnaceGas.csv"))
-  colnames(BF_gas)[2] <- "Energy"
-  BOF_gas <- read.csv(paste0(path$IE_Processed,"/IEA/IEA_",year,"_BlastOxygenFurnaceGas.csv"))
-  colnames(BOF_gas)[2] <- "Energy"
-  Energy_con <- read.csv(paste0(path$IE_Processed,"/IEA/IEA_",year,"_SteelIndustryEnergyConsumption.csv"))
+  # Read settings list for WSA data feeds:
+  
+  Settings <- read.xlsx(paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"))
+  
+  # Read production, import and export of ingots from WSA and BACI: 
+  
+  Ingots <- list("Production" = read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_Ingots.csv")))
+  
+  Ingots[["Code"]] <- filter(base$flow, Name == "Ingots") %>% pull(Code)
+  
+  Ingots[["Export"]] <- filter(BACI, Product == Ingots$Code) %>% select(-Product) %>%
+    group_by(From) %>% summarise(Quantity = sum(Quantity)) %>% ungroup(From)
+  
+  Ingots[["Import"]] <- filter(BACI, Product == Ingots$Code) %>% select(-Product) %>%
+    group_by(To) %>% summarise(Quantity = sum(Quantity)) %>% ungroup(To)
+  
+  # Estimate forging output:
+  
+  Forgings <- data.frame("base" = 1:nrow(base$region),
+                         "Quantity" = 0,
+                         "In_pro" = 0,
+                         "In_im" = 0,
+                         "In_ex" = 0)
+  
+  Forgings$In_pro[Ingots$Production$base] <- Ingots$Production$Quantity
+  
+  Forgings$In_im[Ingots$Import$To] <- Ingots$Import$Quantity
+  
+  Forgings$In_ex[Ingots$Export$From] <- Ingots$Export$Quantity
+  
+  Forgings$Quantity <- Forgings$In_pro - Forgings$In_ex + Forgings$In_im
+  
+  Forgings$Quantity[Forgings$Quantity < 0] <- Forgings$In_im[Forgings$Quantity < 0]
+  
+  Forgings <- select(Forgings, base, Quantity)
+  
+  
+  # Check if folder with processed data exists and in case delete:
+  
+  df_Processed <- paste0(path$IE_Processed,"/AligningData")
+  
+  if(dir.exists(df_Processed)) unlink(df_Processed,recursive = TRUE) 
+  
+  dir.create(df_Processed) # Create new (empty) folder
+  
+  # Write data to folder
+  
+  write.csv(Forgings,file = paste0(df_Processed,"/Forgings_",year,".csv"),
+            row.names = FALSE)
   
-  # Loading BACI trade data on iron ores and IRP extraction accounts
-  BACI <- read.csv(file = paste0(path$IE_Processed,"/BACI/BACI_",year,".csv"))
-  IronOre_Extraction <- read.csv(file = paste0(path$IE_Processed,"/IRP/IRP_",year,".csv"))
-  
-  ##############################################################################
-  # 2. Filling gaps in WSA production accounts by checking IEA energy data
-  # 2.1 WSA pig iron production vs. production of (IEA) blast furnace gas
-  
-  # Following the WSA publication "Energy use in the steel industry" (p98) between 
-  # 4.3 and 7.2 GJ of blast furnace gas is produced per ton of pig iron.
-  # In the following we fill the gaps assuming an average intensity of 5.7 GJ/t
-  
-  # Merge the two data sets and filter for missing production values
-  Pig_vs_BF <- full_join(Pig,BF_gas,c("base"),copy = FALSE) %>% 
-    mutate(Intensity = Energy/Quantity)
-  Pig_vs_BF_gap <-  Pig_vs_BF %>% filter(Quantity == 0 & Energy != 0)
-  
-  # Fill in values and save new pig iron production data
-  if(length(Pig_vs_BF_gap) > 0) {
-    Pig_vs_BF$Quantity[Pig_vs_BF$base %in% Pig_vs_BF_gap$base] <- Pig_vs_BF_gap$Energy / 5.7 
-    Pig <- Pig_vs_BF %>% select(base,Quantity) 
-    write.csv(Pig, file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_PigIron.csv"),row.names = FALSE)}
-  
-  remove(Pig_vs_BF,Pig_vs_BF_gap,Pig,BF_gas)
-  
-  # 2.2 BOF steel production vs. BOF gas production
-  
-  # Again according to the WSA report on energy, the blown oxygen furnace steelmaking
-  # process produces gases of approx. 0.234 GJ per crude steel (p101). Please note that the 
-  # WSA case studies (p104) showed values betwene 0.4 and 0.8 GJ per ton
-  
-  Steel_vs_BOF <- full_join(BOF_steel,BOF_gas,c("base"),copy = FALSE) %>% 
-    mutate(Intensity = Energy/Quantity)
-  Steel_vs_BOF_gap <- Steel_vs_BOF %>% filter(Quantity == 0 & Energy != 0)
-  
-  # Fill in values and save new BOF steel production data
-  if(nrow(Steel_vs_BOF_gap) > 0) {
-    Steel_vs_BOF$Quantity[Steel_vs_BOF$base %in% Steel_vs_BOF_gap$base] <- Steel_vs_BOF_gap$Energy / 0.234 
-    BOF_steel <- Steel_vs_BOF %>% select(base,Quantity) 
-    write.csv(BOF_steel, file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOxygenBlownConverters.csv"),row.names = FALSE)}
-  
-  remove(Steel_vs_BOF,Steel_vs_BOF_gap,BOF_gas)
-  colnames(BOF_steel)[2] <- "BOF"
-  
-  # 2.3 Filling gaps for rolled steel products
-  Steel_vs_energy <- full_join(Energy_con,Flat,c("base"),copy = FALSE)
-  Steel_vs_energy <- full_join(Steel_vs_energy,Long,c("base"),copy = FALSE) 
-  Steel_vs_energy <- full_join(Steel_vs_energy,BOF_steel,c("base"),copy = FALSE)
-  Steel_vs_energy <- full_join(Steel_vs_energy,EAF_steel,c("base"),copy = FALSE)
-  Steel_vs_energy[is.na(Steel_vs_energy)] <- 0
-  Steel_vs_energy <- mutate(Steel_vs_energy,Rolled = Flat+Long,Intensity = Value/Rolled)
-  Steel_vs_energy$Intensity[Steel_vs_energy$Intensity == Inf] <- 0
-  
-  # The reference plant, of the WSA report, has an average energy intensity for
-  # long rolled products of approx. 20 GJ per ton and 9.5 GJ per ton for scrap based long products 
-  # WSA referenced flat rolling plate mill has an energy intensity of 24 GJ per ton of product (p120-122)
-  # Please note that these numbers refer to the total energy requirement including 
-  # upstream energy use for steel and iron making. Direct energy use in rolling mills is much smaller at around 2-7 GJ/t
-  
-  # 2.3.1 Filter for countries with inconsistent data
-  # No flat rolling reported but BOF steel is produced 
-  Gap <- Steel_vs_energy %>% filter(Flat == 0 & BOF != 0)
-  if(nrow(Gap) > 0) Steel_vs_energy$Flat[Steel_vs_energy$base %in% Gap$base] <- Gap$BOF
-  
-  # No long rolling reported but EAF steel is porduced
-  Gap <- Steel_vs_energy %>% filter(Long == 0 & EAF != 0)
-  if(nrow(Gap) > 0) Steel_vs_energy$Long[Steel_vs_energy$base %in% Gap$base] <- Gap$EAF
-  
-  # BOF steel reported but flat rolled products + energy intensity is below the threshold
-  Gap <- Steel_vs_energy %>% filter(BOF == 0 & Flat != 0 & Intensity < 20)
-  if(nrow(Gap) > 0) Steel_vs_energy$BOF[Steel_vs_energy$base %in% Gap$base] <- Gap$Flat
-  
-  # No flat and long rolling reported but energy use data available
-  Gap <- Steel_vs_energy %>% filter(Flat == 0 & Long == 0 & Value != 0)
-  if(nrow(Gap) > 0) {
-    # Assuming 18 GJ/ton
-    filling <- Gap$Value/18
-    Steel_vs_energy[Steel_vs_energy$base %in% Gap$base,c("Flat","Long","BOF","EAF")] <- filling/2 }
-  
-  ##############################################################################
-  # 3. Delete BACI trade flows of iron ores where IRP reports no extraction
-  colnames(IronOre_Extraction)[2] <- "Extraction"
-  # Code of countries with no extraction
-  sel <- setdiff(base$region$Code,IronOre_Extraction$base)
-  # Set zero
-  BACI$Quantity[BACI$From %in% sel & BACI$Product == 1] <- 0
-  BACI <- BACI[BACI$Quantity != 0,]
-  ##############################################################################
-  # 4. Writing manipulated data to folder
-  Flat <- select(Steel_vs_energy,base,Flat) 
-  Long <- select(Steel_vs_energy,base,Long)
-  BOF <- select(Steel_vs_energy,base,BOF)
-  EAF <- select(Steel_vs_energy,base,EAF)
-  colnames(Flat)[2] <- colnames(Long)[2] <- colnames(BOF)[2] <- colnames(EAF)[2] <- "Quantity"
-  
-  write.csv(Flat,file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_FlatRolledProducts.csv"),row.names = FALSE)
-  write.csv(Long,file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_LongRolledProducts.csv"),row.names = FALSE)
-  write.csv(BOF,file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOxygenBlownConverters.csv"),row.names = FALSE)
-  write.csv(EAF,file = paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelElectricFurnaces.csv"),row.names = FALSE)
-  write.csv(BACI,file = paste0(path$IE_Processed,"/BACI/BACI_",year,".csv"),row.names = FALSE)
-  
-  print("IEDataProcessing_PIOLab_AligningData finished.")
   
 }
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables.R b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables.R
index 73b93c1..1241381 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables.R
@@ -171,8 +171,6 @@ IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables <- function(year,path)
   write_file(zero,"Zero")
   write_file(S8,"AllInOne")
   
-  print(paste0("IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables finished"))
-  
 }
 
 
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildingDomesticTables.R b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildingDomesticTables.R
index 5084b04..a060e52 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildingDomesticTables.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildingDomesticTables.R
@@ -10,387 +10,712 @@
 
 IEDataProcessing_PIOLab_BuildingDomesticTables <- function(year,path)
 {
+  # !diagnostics off
+  
   print("IEDataProcessing_PIOLab_BuildingDomesticTables initiated.")
-  # Load region aggregator
-  source(paste0(path$Subroutines,"/Root2Base_RegionAggregator.R"))
-  reg_agg <- Root2Base_RegionAggregator(RegionAggregator)
-  # Load function to compile end use map
-  source(paste0(path$Subroutines,"/makeEndUseMap.R"))
-  # Load slag rate of blast furnace and coefficient per ton of pig iron output
-  WSA_yield <- read.csv(paste0(path$IE_Processed,"/WSA/SteelIndustryYields.csv")) %>%
-    select(Process,Average)
-  # Load extension of the WasteMFAIO model and IO codes
-  load(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Q.RData"))
-  load(paste0(path$IE_Processed,"/EXIOWasteMFAIO/IO.codes.RData"))
-  # Load trade data
-  BACI <- read.csv(paste0(path$IE_Processed,"/BACI/BACI_",year,".csv"))
+  
+  # Add path to IE classification setting file:
+  
+  path[["IE_classification"]] <- paste0(path$Settings,"/Base/",IEdatafeed_name,"_BaseSectorClassification.xlsx")
+  
+  # Load prorating function and Number2File:
+  
+  source(paste0(path$Subroutines,"/Prorate.R"))
+  
+  source(paste0(path$Subroutines,"/Numbers2File.R"))
+ 
+  
+  Codes <- list( "WIO" = read.csv(file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/IO_codes.csv") ) )
+  
+  Setting <- list( "WSA" = read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"), sheet = 1 ),
+                   "IE" = read.xlsx(xlsxFile = paste0(path$Settings,"/Base/IE_settings.xlsx"), sheet = 1 ))
+  
+  Setting$WSA["path"] <- paste0(path$IE_Processed,"/WSA/WSA_",year,"_",Setting$WSA$FeedName,".csv")
+  
+  
+  # Load flow data where all regions are included in one object:
+  
+  data <- list( "FinalDemand" =  read.csv( paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_","FinalDemand.csv") ),
+                "Fab2Demand" = read.csv( paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_","Fabrication2FinalDemand.csv") ),
+                "BACI" =  read.csv(paste0(path$IE_Processed,"/BACI/BACI_",year,".csv") ),
+                "Eol" = read.csv(paste0(path$IE_Processed,"/EOL/EOL_",year,".csv") ),
+                "IRP" = read.csv(paste0(path$IE_Processed,"/IRP/IRP_",year,".csv") ),
+                "Grade" = read.csv(paste0(path$IE_Processed,"/Grades/IronOreGrades.csv") ),
+                "Yield" = select(read.csv(paste0(path$IE_Processed,"/WSA/SteelIndustryYields.csv")),Process,Average),
+                "WSA" = lapply(X = Setting$WSA$path, FUN = read.csv),
+                "Forgings" = read.csv(paste0(path$IE_Processed,"/AligningData/Forgings_",year,".csv") ) 
+              )
+  
+  # Load WSA Source2Root concordances
+  
+  set <- read.xlsx(xlsxFile = paste0(path$Settings,"/Base/IE_settings.xlsx"),sheet = 2)
+  
+  path_sel <- list("flow" = paste0(path$Concordance,"/WSA/",
+                                   set$date[set$aggregator == "sector"],"_WSA_Source2Root_Product.csv"),
+                   "process" = paste0(path$Concordance,"/WSA/",
+                                      set$date[set$aggregator == "sector"],"_WSA_Source2Root_Industry.csv")
+                   )
+  
+  S2R <- list( "WSA" = list( "industry" = as.matrix( read.csv(path_sel$process,header = FALSE) ),
+                             "product" = as.matrix( read.csv(path_sel$flow,header = FALSE) )
+                            )
+              )
+  
+  # Store root to mother concordances in list object:
+  
+  R2M_sel <- list( "WSA" = list( "industry" = R2M$process,
+                                 "product" = R2M$flow
+                                 ) 
+                   )
+  
+  # Normalize source to root concordances (create maps)
+  
+  for(j in 1:2) S2R$WSA[[j]] <- as.matrix( S2R$WSA[[j]] / rowSums(S2R$WSA[[j]]) ) 
+  
+  S2M <- list("WSA" = list())  # Create empty list to store source to mother maps
+  
+  # Create Source to mother map:
+  
+  for(j in 1:2) S2M$WSA[[j]] <- S2R$WSA[[j]] %*% R2M_sel$WSA[[j]]
+  
+  # Load SUT templates
+  
+  SUT_temp <- list( "Supply" = as.matrix( read.xlsx(path$IE_classification, sheet = 5,rowNames = TRUE) ),
+                    "Use" = as.matrix( read.xlsx(path$IE_classification, sheet = 6,rowNames = TRUE) )
+                  )
+  
   # Load allocation function
-  source(paste0(path$Subroutines,"/AllocateSupply2Use.R"))
   
-  # Define general variables
-  n_pro <- nrow(base$product)
-  n_ind <- nrow(base$industry)
-  n_reg <- nrow(base$region)
-  n_va <- 5
-  n_fd <- 3
+  # source(paste0(path$Subroutines,"/AllocateSupply2Use.R"))
   
-  for(i in 1:n_reg)
+  # Define general variables:
+  
+  # num <- list("pro" = nrow(base$product),
+  #             "ind" = nrow(base$industry),
+  #             "reg" = nrow(base$region),
+  #             "va" = nrow(base$input),
+  #             "fd" = nrow(base$demand) )
+             
+  for(i in 1:num$reg)
   {
-    print(i)
-    # Create empty SUT
-    SUT <- data.frame(matrix(0,(n_pro+n_ind+n_va),(n_pro+n_ind+n_fd)))
-    colnames(SUT) <- c(base$industry$Name,base$product$Name,
-                       "FinalConsumption","Landfill","Atmosphere")
-    rownames(SUT) <- c(base$industry$Name,base$product$Name,
-                       "FerrousMinerals","EOLScrap","Limestone","Coke","Air")
-    SUT <- as.matrix(SUT)
-    ############################################################################
-    # 1. Writing boundary inputs of iron ore extraction and end-of-life scrap to table
-    
-    # 1.1 EoL scrap 
-    data <- read.csv(paste0(path$IE_Processed,"/EOL/EOL_",year,".csv"))
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["EOLScrap","Scrap preparation"] <- value }
-    
-    # 1.2 IRP iron ore extraction i.e. inputs from nature
-    data <- read.csv(paste0(path$IE_Processed,"/IRP/IRP_",year,".csv"))
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["FerrousMinerals","Mining"] <- value }
-    
-    ############################################################################
-    # 2. Writing production values into supply table
-    
-    # 2.1 Waste (gangue) output of mining
-    # Read iron ore grades and assume beneficiation to 60% of ore grade
-    data <- read.csv(paste0(path$IE_Processed,"/Grades/IronOreGrades.csv"))  
-    
-    # if no ore grades are available for a region, assume 0.6
-    if(i %in% data$base) 
-    {concen <- data$Concentration[data$base == i]}else  
-    {concen <- 0.6}
-    
-    if(SUT["FerrousMinerals","Mining"] > 0) {
-    iron <- SUT["FerrousMinerals","Mining"]*concen
-    # iron ore weight with 60% grade, when concentration is higher than 0.6, leave it like it is
-    if(concen <= 0.6) {value_new <- iron/0.6} else
-    {value_new <- iron/concen}
-    # Calculate waste flow to landfill 
-    waste <- value-value_new
-    # Allocate output of iron ore (now 60% grade) to mining
-    SUT["Mining","Iron ore"] <- value_new
-    # Allocate boundary outputs of waste to the supply-side of mining, that is landfill
-    SUT["Mining","Landfill"] <- waste }
-    
-    # 2.2 Write pig iron production and use yields to estimate waste/slag flows
-    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_PigIron.csv"))
-    
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["Blast furnace","Pig iron"] <- value
-      # Take slag rate from WSA
-      yield <- WSA_yield$Average[WSA_yield$Process == 'BF slag rate']/1000
-      # Estimate slag output and allocate to landfill (final use)
-      SUT["Blast furnace","Landfill"] <- value * yield
-      # Estimate output of top gas (1.7 tons per ton oig iron)
-      SUT["Blast furnace","Atmosphere"] <- value *1.7}
-    
-    # 2.3 Sponge iron
-    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SpongeIron.csv"))
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["Direct reduction","Sponge iron"] <- value
-      # Allocate slag output assuming losses of 5% of sponge iron output
-      #yield <- 0.05
-      #waste <- value * yield
-      # Alternatively allocate losses including gangue by assuming waste of 600 kg per ton of useful output
-      SUT["Direct reduction","Landfill"] <- value * 0.6 }
+    print(paste("Compiling",base$region$Name[i]))
+    
+    # Create empty SUT:
+    
+    SUT <- data.frame(matrix(0,(num$flow + num$process + num$input),(num$flow + num$process + num$demand)))
+    
+    colnames(SUT) <- c(base$process$Name,
+                       base$flow$Name,
+                       base$demand$Name)
+    
+    rownames(SUT) <- c(base$process$Name,
+                       base$flow$Name,
+                       base$input$Name)
+    
+    SUT <- as.matrix(SUT) # Transform to matrix write numbers using matrix indices
+    
+    
+    ### Flows associated with fabrication ###
+    
+    
+    # Load data on fabrication use:
+    
+    data_sel <- read.csv(paste0(path$IE_Processed,"/Cullen/FabricationUse_",
+                            year,"_",base$region$Name[i],".csv"),
+                     header = FALSE)
+    
+    # Store matrix index in list:
+    
+    index <- list("row" = num$process + filter(base$flow,Type == "Finished") %>% pull(Code),
+                  "col" = filter(base$process,Type == "Final") %>% pull(Code))
+  
+    SUT[index$row,index$col] <- as.matrix(data_sel)  # Write into table
+        
+    # Load data on fabrication scrap output
+    
+    data_sel <- read.csv(paste0(path$IE_Processed,"/Cullen/FabricationScrap_",
+                            year,"_",base$region$Name[i],".csv"))
+    
+    # Matrix indices in list:
+    
+    index <- list("row" = data_sel$base,
+                  "col" = num$process + filter(base$flow, Name == "Fabrication scrap") %>% pull(Code))
+    
+    SUT[index$row,index$col] <- data_sel$Quantity  # Write scrap flows into table
+    
+    # Read data for useful fabrication output
+    
+    data_sel <- select(data$Fab2Demand,base.from,sector.from,Quantity) %>% filter(base.from == i) %>% 
+      group_by(base.from,sector.from) %>% summarise(Quantity = sum(Quantity)) %>% ungroup(base.from,sector.from)
+    
+    # Store matrix index in list:
+    
+    index <- as.matrix(data.frame("row" = filter(base$process,Type == "Final") %>% pull(Code),
+                                  "col" = num$process + filter(base$flow,Type == "Final") %>% pull(Code)
+                                  )
+                       )
+
+    SUT[index] <- data_sel$Quantity  # Write fab output to table
+    
+    
+    # Read data on intermediate use of manufacturing output
+    
+    data_sel <- data$Fab2Demand %>% filter(base.from == i,base.to == i) %>% 
+      select(sector.from,sector.to,Quantity) %>% group_by(sector.from,sector.to) %>% 
+      summarise(Quantity = sum(Quantity)) %>% ungroup(sector.from,sector.from)
+    
+    # Read base sector codes:
+    
+    index <-data.frame( "row" = num$process + filter(base$flow,Type == "Final") %>% pull(Code),
+                        "col" = filter(base$process,Type == "Final") %>% pull(Code) )
+                       
+    data_sel$sector.from <- index$row[data_sel$sector.from]  # Exchange WIO codes with base row codes
+    
+    data_sel$sector.to <- index$col[data_sel$sector.to] # Exchange WIO codes with base col codes
+    
+    # Copy base codes into data frame and transform into matrix:
+    
+    index <- as.matrix(data_sel[,c("sector.from","sector.to")])   
+    
+    SUT[index] <- data_sel$Quantity
+    
+    
+    # Read flows from final production sector (manuf.) to final demand category:
+    
+    data_sel <- data$FinalDemand %>% filter(base.from == i,base.to == i) %>% 
+      select(sector,demand,Quantity) %>% group_by(sector,demand) %>% 
+      summarise(Quantity = sum(Quantity)) %>% ungroup(sector,sector,demand)
+    
+    # Read base sector codes:
+    
+    index <-data.frame( "row" = filter(base$process,Type == "Final") %>% pull(Code))
+    
+    data_sel$sector <- index$row[data_sel$sector]  # Exchange WIO code with base sector code
+    
+    data_sel$demand <- data_sel$demand + num$process + num$flow  # Exchange WIO with base sector code
+    
+    index <- as.matrix( data_sel[,c("sector","demand")] )  # Create matrix for indices
+    
+    SUT[index] <- data_sel$Quantity
+    
+    
+    ### Output of steelmaking sector ###
+    
+    # Read WSA id's of processed data that is relevant:
+    
+    Code_sel <- Setting$WSA %>% filter(Type %in% c("Primary","Secondary","Finished")) %>% 
+      select(id, FeedName, Type)
+    
+    # Use id to extract a list containing the processed data:
+    
+    data_sel <- data$WSA[Code_sel$id]
+    
+    # Create vector in list to store the data for country i:
+    
+    Value <- list( "Source" = vector( mode="integer", length= length(data_sel) ) )
+    
+    # Write production numbers in vector:
+    
+    for( j in 1:nrow(Code_sel) ) 
+    {
+      if(i %in% data_sel[[j]]$base) Value$Source[j] <- filter(data_sel[[j]],base == i) %>% pull(Quantity)
+    }
+    
+    # Map raw data to mother product classification:
+    
+    Value[["Mother"]] <- colSums( S2M$WSA[[2]][Code_sel$id,] * Value$Source )
+    
+    # Read indices of steelmaking sector outputs
+    
+    index <- list( "row" = filter(base$process,Type %in% c("Primary","Secondary","Finished")) %>% pull(Code),
+                   "col" = filter(base$flow,Type %in% c("Primary","Secondary","Finished")) %>% pull(Code) 
+                  )
+    
+    Value[["Supply"]] <- SUT_temp$Supply %*% diag(Value$Mother) # Allocate products to industries
+    
+    # Write values into SUT (note addition of number of industries to column indices):
+    
+    SUT[index$row, num$process + index$col] <- Value$Supply[index$row,index$col]
+    
+    ### Add forging production ###
+    
+    if(i %in% data$Forgings$base)
+    {
+      SUT["Steel casting and forging","Forgings"] <- filter(data$Forgings,base == i) %>% pull(Quantity)
+    }
+    
+    # Read indices of secondary i.e. crude steel:
+    
+    index <- data.frame("industry" = filter(base$process,Name %in% paste("Continuous casting of",c("slabs","billets","blooms"))) %>% pull(Code),
+                        "product" = filter(base$flow,Name %in% c("Slabs","Billets","Blooms")) %>% pull(Code)
+                        )
+
+    
+    # Create map to calculate crude steel (secondary) output by estimating demand of downstream processes:
+    
+    Map <- t( SUT_temp$Use[index$product,] ) / colSums( SUT_temp$Use[index$product,] ) 
+    
+    Map[is.na(Map)] <- 0
+    
+    # Estimate crude steel demand/output:
+    
+    Value <- colSums( Map *  rowSums(SUT[base$process$Code, num$process + base$flow$Code]) )
+    
+    index$product <- index$product + num$process  # Change product (col) code to write in SUT
+    
+    SUT[as.matrix(index)] <- Value  # Write value
+    
+    
+    ### Estimate forming scrap  ###
+    
+    # indices of forming processes:
+    
+    index <- list( "industry" = filter(base$process, Type == "Finished") %>% pull(Code) )
+    
+    # Output of forming processes:
+    
+    Value <- rowSums(SUT[index$industry, num$process + base$flow$Code])
+    
+    # Scrap = ( Useful output / yield ) - Useful output: 
+    
+    Value <- ( Value / filter(data$Yield, Process == "Hot rolling yield") %>% pull(Average) ) - Value
+    
+    SUT[index$industry,"Forming scrap"] <- Value  # Write values in SUT
+    
+    ##############################################
+    ### Estimate final outputs of steel sector ###
+    ##############################################
+    
+    # Read process factor for top gas per pig iron:
+    
+    factor <- filter(Setting$IE, item == "TopGasPerPigIron") %>% pull(value)
+    
+    
+    # Write emissions in final output quadrant:
+    
+    SUT["Blast furnace","Atmosphere"] <- SUT["Blast furnace","Pig iron"] * factor
+    
+    
+    # Read slag per unit pig iron and allocate amount to output to landfill
+    
+    factor <- filter(Setting$IE, item == "SlagPerPigIron") %>% pull(value)
+    
+    SUT["Blast furnace","Landfill"] <- SUT["Blast furnace","Pig iron"] * factor
+    
+    
+    # Read gangue per unit sponge iron factor and add amount to landfill:
+    
+    factor <- filter(Setting$IE, item == "GanguePerSpongeIron") %>% pull(value)
+    
+    SUT["Direct reduction","Landfill"] <- SUT["Direct reduction","Sponge iron"] * factor
+    
+    
+    # Read oxygen blown furnace yield and use this for both OBF and open hearth furnace:
+    
+    factor <- filter(data$Yield,Process == "BOF yield") %>% pull(Average)
+    
+    # Read sector indices: 
+    
+    index <- data.frame("industry" = filter(base$process, Name %in% c("Basic oxygen converter","Open hearth furnace")) %>% pull(Code),
+                        "product" = num$process + filter(base$flow, Name %in% c("Liquid steel OBF","Liquid steel OHF")) %>% pull(Code)
+                        )
+    
+    # Write flow to landfill:
+    
+    SUT[index$industry,"Landfill"] <- ( SUT[as.matrix(index)] / factor ) - SUT[as.matrix(index)] 
+    
+    
+    # Read electric arc furnace yield and estimate waste flow:
+    
+    factor <- filter(data$Yield,Process == "EAF yield") %>% pull(Average)
+    
+    Value <- SUT["Electric arc furnace","Liquid steel EAF"] # EAF steel production
+    
+    SUT["Electric arc furnace","Landfill"] <- ( Value / factor ) - Value
+    
+    
+    # Read slab and billet caster yields:
+    
+    factor <- filter( data$Yield,Process %in% c("Slab caster yield","Billet caster yield") ) %>% pull(Average)
+    
+    index <- data.frame("industry" = filter(base$process,Name %in% paste("Continuous casting of",c("slabs","billets"))) %>% pull(Code),
+                        "product" = num$process + filter(base$flow,Name %in% c("Slabs","Billets") ) %>% pull(Code)
+                        )
+    
+    
+    SUT[index$industry,"Forming scrap"] <- ( SUT[as.matrix(index)] / factor ) - SUT[as.matrix(index)]
+    
+    
+    # Read bloom caster yield and use this for both blooms and ingost:
+    
+    factor <- filter( data$Yield,Process == "Bloom caster yield" ) %>% pull(Average)
+    
+    index <- data.frame("industry" = filter(base$process,Name %in% c("Continuous casting of blooms","Ingot casting")) %>% pull(Code),
+                        "product" = num$process + filter(base$flow,Name %in% c("Blooms","Ingots") ) %>% pull(Code)
+                        )
+    
+    SUT[index$industry,"Forming scrap"] <- ( SUT[as.matrix(index)] / factor ) - SUT[as.matrix(index)] 
+    
+    
+    ########################################################
+    ### Fill use table of forming processes (= Finished) ###
+    ########################################################
+
+    
+    # Read indices of products that are used by these processes 
+    # Note: Hot rolled coil-sheet-strip is handled separatly:
+    
+    index <- list( "product" = filter( base$flow, Name %in% c("Slabs","Billets","Blooms","Ingots") ) %>% pull(Code),
+                   "industry" = filter( base$process, Type == "Finished" ) %>% pull(Code),
+                   "interind" = filter( base$process, Type == "Secondary" ) %>% pull(Code)
+                  )
+    
+    # Read domestic output of intermediate inputs and of process:
+    
+    DomOut <- list( "Process" = rowSums(SUT[index$industry,]),
+                    "Intermed" = rowSums(SUT[index$interind,])
+                  )
+    
+    # Read use structure from template and create map:
+    
+    Map <- SUT_temp$Use[index$product, index$industry]  
+    
+    # Create prorated map:
+    
+    Map <- Prorate(Map, DomOut$Process)
+    
+    Map[is.na(Map)] <- 0
+    
+    
+    # Read export of intermediates:
+    
+    export <- filter(data$BACI,From == i,Product %in% index$product) %>% select(Product,Quantity) %>% 
+      group_by(Product) %>% summarise(Quantity = sum(Quantity)) %>% ungroup(Product)
+    
+    
+    offset <- min(index$product)-1  # Offset product codes to write in Map
+    
+    if(length(export) >= 1)
+    {
+      Value <- DomOut$Intermed
+      Value[export$Product-offset] <- DomOut$Intermed[export$Product-offset] - export$Quantity
+    }
+    
+    # Check if exports are unavailable and if so set to zero:
+    
+    if( length(export) == 0 )
+    {
+      Value <- DomOut$Intermed
+    }
+    
+    
+    
+    # Check if negatives exists and replace them with domestic production:
+    
+    for(j in 1:length(Value)) if(Value[j] < 0) Value[j] <- DomOut$Intermed[j]  
+     
+    # Mulitply with map and write in use table
+    
+    SUT[num$process + index$product, index$industry] <- Map * Value
+    
+    
+    # Estimate use of hot rolled coil-sheet-strip products:
+    # Note that the use of hot rolled CSS by manufacturing is already accounted for by the WIO model extension
+    
+    index <- list( "product" = filter( base$flow, Name == "Hot rolled coil-sheet-strip" ) %>% pull(Code),
+                   "rolling" = filter( base$process, Name %in% c("Tube welding","Cold rolling mill") ) %>% pull(Code),
+                   "manufacturing" = filter( base$process, Type == "Final" ) %>% pull(Code),
+                   "interind" = filter( base$process, Name == "Hot strip mill" ) %>% pull(Code)
+                  )
+    
+    # Read domestic output of intermediate inputs (product) and of relevant processes:
+    
+    DomOut <- list( "rolling" = rowSums(SUT[index$rolling,]),
+                    "interind" = sum(SUT[index$interind,]),
+                    "manufacturing" = sum(SUT[index$interind,])
+                  )
+    
+    SUT[num$process + index$product, index$rolling] <- DomOut$rolling
+    
+    
+    # Estimate liquid steel use of casting:
+    
+    index <- list( "product" = filter( base$flow, Name %in% paste("Liquid steel",c("OBF","OHF","EAF")) ) %>% pull(Code),
+                   "industry" = filter( base$process, Type == "Secondary" ) %>% pull(Code),
+                   "interind" = filter( base$process, Name %in% c("Basic oxygen converter","Open hearth furnace","Electric arc furnace") ) %>% pull(Code)
+                  )
+    
+    # Read domestic output of intermediate inputs and of process:
+    
+    DomOut <- list( "industry" = rowSums(SUT[index$industry,]),
+                    "Interind" = rowSums(SUT[index$interind,])
+                  )
+    
+    # Read use structure from template and create map:
+    
+    Map <- SUT_temp$Use[index$product, index$industry]  
+    
+    Map[is.na(Map)] <- 0
+    
+    Map <- Prorate( Map,  DomOut$industry)  # Prorate map
+    
+    Value <- Map * DomOut$Interind      # Multiply map with steel production
+    
+    SUT[num$process + index$product,index$industry] <- Value  # Write into table
+    
+    
+    ### Primary inputs ###
+    
+    factor <- filter(Setting$IE, item == "AirPerPigIron") %>% pull(value)
+    
+    SUT["Air","Blast furnace"] <- SUT["Blast furnace","Pig iron"] * factor
+    
+    factor <- filter(Setting$IE, item == "CokePerPigIron") %>% pull(value)
+    
+    SUT["Coke","Blast furnace"] <- SUT["Blast furnace","Pig iron"] * factor
+    
+    factor <- filter(Setting$IE, item == "FluxPerPigIron") %>% pull(value)
+    
+    SUT["Flux","Blast furnace"] <- SUT["Blast furnace","Pig iron"] * factor
+    
+    # EoL scrap: 
+    
+    if(i %in% data$Eol$base) SUT["End-of-Life Scrap","Scrap preparation"] <- data$Eol$Quantity[data$Eol$base == i] 
+    
+    # IRP iron ore extraction
+    
+    if(i %in% data$IRP$base) 
+    {
+      crude <- data$IRP$Quantity[data$IRP$base == i]
       
-    # 2.4 Write steel production of BOF & OHF
-    data_1 <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOxygenBlownConverters.csv"))
-    data_2 <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelOpenHearthFurnaces.csv"))
-    data <- rbind(data_1,data_2) %>% group_by(base) %>% summarise(Quantity = sum(Quantity))
-    remove(data_1,data_2)
-    
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["Oxygen blown & open hearth furnace","Liquid steel (OBF/OHF)"] <- value
-      # Calculate waste
-      yield <- WSA_yield$Average[WSA_yield$Process == 'BOF yield']
-      waste <- (value/yield)-value
-      SUT["Oxygen blown & open hearth furnace","Landfill"] <- waste }
-    
-    # 2.5 Write steel production of EAF
-    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_SteelElectricFurnaces.csv"))
-    
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["Electric arc furnace","Liquid steel (EAF)"] <- value
-      # Calculate waste
-      yield <- WSA_yield$Average[WSA_yield$Process == 'EAF yield']
-      waste <- (value/yield)-value
-      SUT["Electric arc furnace","Landfill"] <- waste }
-    
-    # 2.6 Write flat rolling production and forming scrap
-    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_FlatRolledProducts.csv"))
-    
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["Flat rolling","Flat rolled products"] <- value
-      # Calculate waste
-      yield <- WSA_yield$Average[WSA_yield$Process == 'Slab caster yield']
-      waste <- (value/yield)-value
-      SUT["Flat rolling","Forming & fabrication scrap"] <- waste }
-    
-    # 2.7 Write long rolling production and forming scrap
-    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_LongRolledProducts.csv"))
-    
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["Long rolling","Long rolled products"] <- value
-      # Calculate waste
-      yield <- WSA_yield$Average[WSA_yield$Process == 'Bloom caster yield']
-      waste <- (value/yield)-value
-      SUT["Long rolling","Forming & fabrication scrap"] <- waste }
-    
-    # 2.8 Write production of ingots and subsequently fill the gap of the casting
-    # process with slabs (because all feeds into flat rolling)
-    data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_Ingots.csv"))
-    
-    if(i %in% data$base) {
-      value <- data$Quantity[data$base == i]
-      SUT["Casting of ingots & slabs","Ingots"] <- value
-      value <- SUT["Flat rolling","Flat rolled products"] - SUT["Casting of ingots & slabs","Ingots"]
-      SUT["Casting of ingots & slabs","Slabs"] <- value
-      yield <- WSA_yield$Average[WSA_yield$Process == 'Slab caster yield']
-      value <- SUT["Flat rolling","Flat rolled products"]
-      waste <- (value/yield)-value
-      SUT["Casting of ingots & slabs","Forming & fabrication scrap"] <- waste }
-    
-    # 2.9 For the production value of billets and blooms use the value of long rolling
-      value <- sum(SUT["Long rolling",c("Long rolled products","Forming & fabrication scrap")])
-      SUT["Casting of billets & blooms","Billets & blooms"] <- value
-      # Estimate scrap flows using yield data
-      yield <- WSA_yield$Average[WSA_yield$Process == 'Bloom caster yield']
-      waste <- (value/yield)-value
-      SUT["Casting of billets & blooms","Forming & fabrication scrap"] <- waste 
+      SUT["Crude Ore","Mining"] <- crude
       
-    # 2.10 Fabrication i.e. manufacturing output
-    data <- read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_FabricationToFinalDemand.csv")) %>%
-      filter(From.Region == i) %>% select(From.Product,Quantity) %>% group_by(From.Product) %>%
-      summarise(Quantity = sum(Quantity))
-    # Set up index to write values directly into cells
-    index <- cbind(base$industry$Name[11:20],base$product$Name[13:22])
-    SUT[index] <- data$Quantity
+      # if no ore grades are available for a region, assume 0.6:
       
-    # 2.11 Fabrication scarp
-    data <- read.csv(paste0(path$IE_Processed,"/FabricationScrap/FabricationScrap_",year,".csv")) %>%
-      filter(base == i)
-    data <- data.frame("commodity" = as.character(data$commodity),
-                       "Quantity" = data$Quantity,
-                       stringsAsFactors = FALSE)
-    index <- as.data.frame(index,stringsAsFactors = FALSE)
-    colnames(index) <- c("Industry","Product")
-    data <- left_join(index,data,c("Product" = "commodity"),copy = FALSE)
-    
-    SUT[data$Industry,"Forming & fabrication scrap"] <- data$Quantity
-
-    # 2.12 Estimate production of scrap steel by summing over "forming and fabrication scrap"
-    # and EoL scrap supply
-    
-    value <- sum(SUT[,"Forming & fabrication scrap"]) + SUT["EOLScrap","Scrap preparation"]
-    SUT["Scrap preparation","Scrap steel"] <- value  
-    
-    ############################################################################
-    # 3. Filling use table
-    # IMPORTANT: The allocation follows the logic to source data on exports from the
-    # BACI data set and subtract this value from the domestic production of the upstream process 
-    # to get an estimate for the domestic inputs of each process. Where this results in negative values
-    # for domestic use, we assume that inputs are 50% from foreign source
-    
-    # 3.1 Steel in final demand
-    data <- read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_SteelInFinalDemand.csv")) %>%
-      filter(From.Region == i & To.Region == i)
-    index <- base$product$Name[base$product$Code %in% data$Product]
-    SUT[index,"FinalConsumption"] <- data$Quantity
-    
-    # 3.2.1 Flat Rolled products used in fabrication
-    item <- "Flat rolled products"
-    # Load map to allocate to end use (fabrication)
-    map <- makeEndUseMap(i,"Flat")
-    map <- map[order(map$index),]
-    share <- map$Share
-    ############################################
-    # The following command may cause problems #
-    ############################################
-    users <- base$industry$Code[11:20][map$index]
-    # Load trade data 
-    data <- filter(BACI,Product == base$product$Code[base$product$Name == item]) %>% select(From,To,Quantity)
-    # Read domestic production
-    pro <- SUT["Flat rolling","Flat rolled products"]
-    # Read domestic use
-    use <- sum(Q["Flat",IO.codes$index[IO.codes$base == i]])
-    
-    # Execute function for estimation script to write values
-    SUT <- AllocateSupply2Use(SUT,item,share,users,data,pro,use,i)
-    
-    # 3.3.2 Long Rolled products used in fabrication
-    item <- "Long rolled products"
-    # Load map to allocate to end use (fabrication)
-    map <- makeEndUseMap(i,"Long")
-    map <- map[order(map$index),]
-    share <- map$Share
-    users <- base$industry$Code[11:20][map$index]
-    # Load trade data 
-    data <- filter(BACI,Product == base$product$Code[base$product$Name == item]) %>% select(From,To,Quantity)
-    # Read domestic production
-    pro <- SUT["Long rolling",item]
-    # Read domestic use
-    use <- sum(Q["Long",IO.codes$index[IO.codes$base == i]])
-    
-    # Execute function for estimation script to write values
-    SUT <- AllocateSupply2Use(SUT,item,share,users,data,pro,use,i)
-    
-    # 3.4 Use of forming and fabrication scrap by scrap preparation
-    SUT["Forming & fabrication scrap","Scrap preparation"] <- sum(SUT[,"Forming & fabrication scrap"])
-    
-    # 3.5 Use of billets and blooms by long rolling
-    # Load number for exports of billets and blooms 
-    item <- "Billets & blooms"
-    users <- "Long rolling"
-    share <- 1
-    data <- filter(BACI,Product == base$product$Code[base$product$Name == item]) %>% select(From,To,Quantity)
-    pro <- SUT["Casting of billets & blooms",item]
-    use <- sum(SUT[users,])
-    
-    # Execute function for estimation script to write values
-    SUT <- AllocateSupply2Use(SUT,item,share,users,data,pro,use,i)
-    
-    # 3.6 Use of ingots and slabs by flat rolling
-    # 3.6.1 Ingots
-    export <- filter(BACI,Product == base$product$Code[base$product$Name == "Ingots"]) %>% 
-      select(From,To,Quantity) %>% filter(From == i) 
-    value <- SUT["Casting of ingots & slabs","Ingots"] - sum(export$Quantity)
-    # In case value is negative, assume that 5% of inputs is coming from domestic ingots
-    if(value > 0) {SUT["Ingots","Flat rolling"] <- value} else
-    {SUT["Ingots","Flat rolling"] <- sum(SUT["Flat rolling",]) * 0.05}
-    
-    # 3.6.2 Slabs
-    export <-  filter(BACI,Product == base$product$Code[base$product$Name == "Slabs"]) %>% 
-      select(From,To,Quantity) %>% filter(From == i) 
-    # Domestic production of slabs minus exports = domestic use
-    value <- SUT["Casting of ingots & slabs","Slabs"] - sum(export$Quantity)
-    if(value > 0) {SUT["Slabs","Flat rolling"] <- value} else
-    {SUT["Slabs","Flat rolling"] <-  sum(SUT["Flat rolling",]) * 0.45}
-    
-    # 3.7 Casting of billets and blooms and casting of ingots and slabs
-    # The only input to casting is liquid steel which is not a traded commodity, 
-    # The inputs to casting are therefor estimated by using the production/output value
-    # of the respective furnace
-    
-    # Steel from Electric arc furnace
-    SUT["Liquid steel (EAF)","Casting of billets & blooms"] <- SUT["Electric arc furnace","Liquid steel (EAF)"]
-    # Fill remaining gap with OBF steel   
-    gap <- sum(SUT["Casting of billets & blooms",]) - SUT["Liquid steel (EAF)","Casting of billets & blooms"]
-    if(gap > 0) SUT["Liquid steel (OBF/OHF)","Casting of billets & blooms"] <- gap
-    
-    # Steel from oxygen blown converter  
-    SUT["Liquid steel (OBF/OHF)","Casting of ingots & slabs"] <- sum(SUT["Casting of ingots & slabs",])
-    
-    # 3.8 Sponge iron used by EAF
-    export <- filter(BACI,Product == base$product$Code[base$product$Name == "Sponge iron"],From == i)
-    
-    if(SUT["Direct reduction","Sponge iron"] != 0) 
+      if(i %in% data$Grade$base) concen <- data$Grade$Concentration[data$Grade$base == i]
+      
+      if(!i %in% data$Grade$base) concen <- 0.6
+      
+      # Calculate iron content:
+      
+      iron <- crude * concen
+      
+      if(concen <= 0.6) ore <- iron / 0.6  # iron ore weight when 60% grade (ready for shipping) 
+      
+      if(concen > 0.6) ore <- iron / concen  # when concentration is > 0.6: not change
+      
+      SUT["Mining","Landfill"] <- crude - ore  # Waste to landfill (supply-side of mining)
+      
+      SUT["Mining","Iron ore"] <- ore  # Supply of iron ore (now 60% grade) by mining
+    }
+    
+ 
+    ### Allocate iron ore use to ironmaking ###
+    
+    index <- list( "supply" = data.frame("industry" = filter(base$process, Name %in% c("Blast furnace","Direct reduction")) %>% pull(Code),
+                                         "product" = num$process + filter(base$flow, Name %in% c("Pig iron","Sponge iron")) %>% pull(Code)
+                                         ),
+                   "use" =  data.frame("industry" = filter(base$process, Name == "Mining") %>% pull(Code),
+                                       "product" = num$process + filter(base$flow, Name == "Iron ore") %>% pull(Code)
+                                      )
+                  )
+    
+    # Read factors for iron ore inputs:
+    
+    factor <- filter(Setting$IE, item %in% c("OrePerPigIron","OrePerSpongeIron") ) %>% pull(value)
+    
+    # Read primary iron output and estimate total iron ore input with factors:
+    
+    TotIn <- SUT[ as.matrix(index$supply) ] * factor 
+    
+    DomOut <- SUT[as.matrix(index$use)]  # Read domestic production of iron ore
+    
+    if(DomOut > 0)
     {
-      value <- SUT["Direct reduction","Sponge iron"] - sum(export$Quantity)
-      if(value > 0) {SUT["Sponge iron","Electric arc furnace"] <- value} else
-      {SUT["Sponge iron","Electric arc furnace"] <- sum(SUT["Direct reduction","Sponge iron"])/2}
+      # Read export of iron ore:
+      
+      export <- filter(data$BACI,From == i,Product %in% (index$use$product-num$process) ) %>% select(Product,Quantity) %>% 
+        group_by(Product) %>% summarise(Quantity = sum(Quantity)) %>% ungroup(Product) %>% pull(Quantity)
+      
+      if( length(export) == 0 ) export <- 0  # Check if exports are unavailable and if so set to zero
+      
+      Value <- DomOut - export  
+      
+      if(Value < 0) Value <- DomOut  # Set to domestic production if exports too large
+      
+      Map <- (TotIn/sum(TotIn))  # Create map
+      
+      Map[is.na(Map)] <- 1  # Set 1 if NA
+      
+      # Write values into table using map:
+      
+      SUT[index$use$product,index$supply$industry] <- Map * Value
+      
     }
     
-    # 3.9 Allocating iron ores used by direct reduction
-    SUT["Iron ore","Direct reduction"] <- sum(SUT["Direct reduction",])
+    ### Add forming and scrap use by steelmaking ###
+    
+    steel <- data.frame("ind" = filter(base$process, Name %in% c("Basic oxygen converter","Electric arc furnace")) %>% pull(Code),
+                        "pro" = num$process + filter(base$flow, Name %in% paste("Liquid steel",c("OBF","EAF"))) %>% pull(Code)
+                        )
+    
+    # Read scrap per unit output factors:
+    
+    factor <- filter(Setting$IE, item %in% c("ScrapPerBOFsteel","ScrapPerEAFsteel") ) %>% pull(value)
+    
+    # Create map from output values:
+    
+    Map <- ( SUT[as.matrix(steel)] * factor ) / sum(SUT[as.matrix(steel)] * factor)
+    
+    Map[is.na(Map)] <- 0
+    
+    # Multiply with forming scrap supply and write in table 
+    
+    SUT["Forming scrap",steel$ind] <- Map * sum(SUT[,"Forming scrap"])
+    
+    
     
-    # 3.10 Allocate iron ores used by Blast furnace 
-    export <- filter(BACI,Product == base$product$Code[base$product$Name == "Iron ore"],From == i)
-    value <- SUT["Mining","Iron ore"] - sum(export$Quantity)
-    if(value > 0) {SUT["Iron ore","Blast furnace"] <- value} else
-    {SUT["Iron ore","Blast furnace"] <- sum(SUT["Blast furnace",])/2}
+    ### Write fabrication scrap use of waste management (scrap preparation) ###
     
-    # 3.10.1
-    # Allocate other inputs used by blast furnace
-    if(SUT["Blast furnace","Pig iron"] > 0)
+    SUT["Fabrication scrap","Scrap preparation"] <- sum(SUT[,"Fabrication scrap"])
+    
+    
+    ### Write steel scrap output in supply table ###
+    
+    SUT["Scrap preparation","Steel scrap"] <- sum(SUT[,"Scrap preparation"])
+    
+    
+    ### Write steel scrap use by steelmaking ###
+    
+    # Read exports of steel scrap:
+    
+    index <- list("industry" = filter(base$process, Name %in% c("Basic oxygen converter","Electric arc furnace")) %>% pull(Code),
+                  "product" = num$process + filter(base$flow, Name == "Steel scrap") %>% pull(Code)
+                  )
+    
+    DomOut <- SUT["Scrap preparation","Steel scrap"]  # Read domestic production of steel scrap
+    
+    export <- filter(data$BACI,From == i,Product %in% (index$product-num$process) ) %>% select(Product,Quantity) %>% 
+      group_by(Product) %>% summarise(Quantity = sum(Quantity)) %>% ungroup(Product) %>% pull(Quantity)
+    
+  
+    
+    if(length(export) >= 1)
     {
-      pro <- SUT["Blast furnace","Pig iron"]
-      # Limestone and fluxes assuming 300 kg per ton of pig iron
-      SUT["Limestone","Blast furnace"] <- pro *0.3
-      # 600 kg coke or coal per ton pig iron
-      SUT["Coke","Blast furnace"] <- pro * 0.6
-      # (Hot) air of approx. 800 kg per ton
-      SUT["Air","Blast furnace"] <- pro * 0.8
+      Value <- DomOut - export
     }
     
-    # 3.11 Allocating pig iron to BOF
-    export <- filter(BACI,Product == base$product$Code[base$product$Name == "Pig iron"],From == i)
-    value <- SUT["Blast furnace","Pig iron"] - sum(export$Quantity)
-    if(value > 0) {SUT["Pig iron","Oxygen blown & open hearth furnace"] <- value} else
-    {SUT["Pig iron","Oxygen blown & open hearth furnace"] <- sum(SUT["Oxygen blown & open hearth furnace",])*0.4}
+    if( length(export) == 0 ) # Check if exports are unavailable and if so set to zero
+    {
+      Value <- DomOut
+    }
+   
+    if(Value < 0) Value <- DomOut  # Check if exports are larger
+    
+    SUT[index$product,index$industry] <- Map * Value  # Write into table 
+    
+    
+    ### Allocate pig and sponge iron to steelmaking ###
+    
+    index <- list("industry" = filter(base$process, Name %in% c("Basic oxygen converter","Open hearth furnace","Electric arc furnace")) %>% pull(Code),
+                  "product" = filter(base$flow, Name %in% c("Pig iron","Sponge iron") ) %>% pull(Code)
+                  )
     
-    # 3.12 Allocate Scrap steel to BOF and EAF
-    SUT["Scrap steel","Oxygen blown & open hearth furnace"] <- sum(SUT["Oxygen blown & open hearth furnace",])*0.15
-    SUT["Scrap steel","Electric arc furnace"] <- sum(SUT["Electric arc furnace",]) - SUT["Direct reduction","Electric arc furnace"]
+    steel <- data.frame("ind" = filter(base$process, Name %in% c("Basic oxygen converter","Open hearth furnace","Electric arc furnace")) %>% pull(Code),
+                        "pro" = num$process + filter(base$flow, Name %in% paste("Liquid steel",c("OBF","OHF","EAF"))) %>% pull(Code)
+                        )
+    
+    iron <- data.frame("ind" = filter(base$process, Name %in% c("Blast furnace","Direct reduction") ) %>% pull(Code),
+                       "pro" = num$process + filter(base$flow, Name %in% c("Pig iron","Sponge iron") ) %>% pull(Code)
+                        )
+    
+    # Estimate steel and iron outputs:
+    
+    DomOut <- list("steel" = SUT[as.matrix(steel)],
+                   "iron" = SUT[as.matrix(iron)]
+                   )
+    
+    # Read export of pig iron and sponge iron:
+    
+    export <- filter(data$BACI,From == i,Product %in% (iron$pro - num$process) ) %>% select(Product,Quantity) %>% 
+      group_by(Product) %>% summarise(Quantity = sum(Quantity)) %>% ungroup(Product)
+    
+    offset <- min(iron$pro - num$process)-1
+    
+    if(length(export) >= 1)
+    {
+      Value <- DomOut$iron
+      Value[export$Product-offset] <- DomOut$iron[export$Product-offset] - export$Quantity
+    }
+    
+    # Check if exports are unavailable and if so set to zero:
+    
+    if( length(export) == 0 )
+    {
+      Value <- DomOut$iron
+    }
+    
+    for(j in 1:length(Value)) if(Value[j] < 0) Value[j] <- DomOut$iron[j]  
+    
+    factor <- filter(Setting$IE, item %in% c("PigIronPerBOFsteel","SpongeIronPerEAFsteel") ) %>% pull(value)
+    
+    Map <- SUT_temp$Use[index$pro,index$ind]  # Create map for allocation
+    
+    Demand <- DomOut$steel * c( factor[1],1,factor[2] )  # Estimate iron demand
+    
+    Map <- Prorate(Map,Demand) # prorate the map
+    
+    Value <- Map * Value  # Allocate values with map to use
+    
+    SUT[(index$pro + num$process), index$industry] <- Value  # Write value in table
+    
+    
+    ### Save SUT ###
     
-    # 4. Save SUT
     # Check if subfolder in processed data exists and if not create it
+    
     path_set <- paste0(path$IE_Processed,"/SUT")
+    
     if(!dir.exists(path_set)) dir.create(path_set)
+    
     # Setting decimals to two digits
+    
     SUT <- round(SUT,2)
+    
     print(min(SUT))
+    
     # Decompose SUT into single elements, that is supply, use, final demand, inputs from nature and eol scrap
-    Use <- SUT[base$product$Name,base$industry$Name]
-    Supply <- SUT[base$industry$Name,base$product$Name]
-    # What is final demand in monetary IO is called hereafter boundary output
-    BoundaryOutput <- SUT[c(base$industry$Name,base$product$Name),c("FinalConsumption","Landfill","Atmosphere")]
     
-    # What is value added in monetary IO is called hereafter boundary input
-    pi <- c("FerrousMinerals","EOLScrap","Air","Limestone","Coke")
-    BoundaryInput <- matrix(SUT[pi,base$industry$Name],length(pi),nrow(base$industry))
+    Use <- SUT[base$flow$Name,base$process$Name]
+    
+    Supply <- SUT[base$process$Name,base$flow$Name]
+    
+    # Final output (= final demand + landfill + atmosphere):
+    
+    FinalOutput <- SUT[base$process$Name,base$demand$Name]
+
+    # Primary inputs:
+    
+    PrimaryInput <- SUT[base$input$Name,base$process$Name]
+
+    # Remove all column and row names:
     
-    # Remove all column and row names
     colnames(Use) <- NULL
     rownames(Use) <- NULL
     colnames(Supply) <- NULL
     rownames(Supply) <- NULL
-    colnames(BoundaryOutput) <- NULL
-    rownames(BoundaryOutput) <- NULL
-    colnames(BoundaryInput) <- NULL
-    rownames(BoundaryInput) <- NULL
-    
-    # Write Supply, Use, In- and Output to root folder, note that because write.csv will always export colnames
-    # had to use the write.tabel function.
-    
-    write.table(Supply,file = paste0(path_set,"/",year,"_DomesticSupply_Region",i,".csv"),
-                col.names = FALSE,
-                row.names = FALSE,
-                sep = ",")
-    
-    write.table(Use,file = paste0(path_set,"/",year,"_DomesticUse_Region",i,".csv"),
-                col.names = FALSE,
-                row.names = FALSE,
-                sep = ",")
-    
-    write.table(BoundaryOutput,file = paste0(path_set,"/",year,"_BoundaryOutput_Region",i,".csv"),
-                col.names = FALSE,
-                row.names = FALSE,
-                sep = ",")
-    
-    write.table(BoundaryInput,file = paste0(path_set,"/",year,"_BoundaryInput_Region",i,".csv"),
-                col.names = FALSE,
-                row.names = FALSE,
-                sep = ",")
+    colnames(FinalOutput) <- NULL
+    rownames(FinalOutput) <- NULL
+    colnames(PrimaryInput) <- NULL
+    rownames(PrimaryInput) <- NULL
+
+    # Write tables to folder:
+
+    Numbers2File(Supply, paste0(path_set,"/",year,"_Supply_Region",i,".csv") )
+    Numbers2File(Use, paste0(path_set,"/",year,"_Use_Region",i,".csv") )
+    Numbers2File(FinalOutput, paste0(path_set,"/",year,"_FinalOutput_Region",i,".csv") )
+    Numbers2File(PrimaryInput, paste0(path_set,"/",year,"_PrimaryInput_Region",i,".csv") )
     
     print(paste0("Minimum value: ",min(SUT)))
   }
-  print("IEDataProcessing_PIOLab_BuildingDomesticTables finished.")
 }
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildingTradeBlocks.R b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildingTradeBlocks.R
index b8f98a8..493114f 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildingTradeBlocks.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_BuildingTradeBlocks.R
@@ -9,30 +9,20 @@ IEDataProcessing_PIOLab_BuildingTradeBlocks <- function(year,path)
 {
   print("IEDataProcessing_PIOLab_BuildingTradeBlocks initiated.")
   
-  path[["IE_classification"]] <- paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx")
+  path[["IE_classification"]] <- paste0(path$Settings,"/Base/",IEdatafeed_name,"_BaseSectorClassification.xlsx")
   
-  # Define general variables:
-  
-  # num <- list("pro" = nrow(base$product),
-  #             "ind" = nrow(base$industry),
-  #             "reg" = nrow(base$region),
-  #             "va" = nrow(base$input),
-  #             "fd" = nrow(base$demand) )
-
   # Load WSA settings (for codes and feed names):
-  
   Settings <- read.xlsx(paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"))
   
   # Load prorating function and Number2File:
   
   source(paste0(path$Subroutines,"/Prorate.R"))
-  
   source(paste0(path$Subroutines,"/Numbers2File.R"))
   
   
-  Setting <- list( "WSA" = read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"), sheet = 1 ),
-                   "IE" = read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/IE_settings.xlsx"), sheet = 1 )
-                  )
+  # Setting <- list( "WSA" = read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"), sheet = 1 ),
+  #                  "IE" = read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/IE_settings.xlsx"), sheet = 1 )
+  #                 )
   
   # Load flow data where all regions are included in one object:
   
@@ -59,12 +49,12 @@ IEDataProcessing_PIOLab_BuildingTradeBlocks <- function(year,path)
   
   # Run syntax to load Source2Root maps:
   
-  set <- read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"),sheet = 2)
+  set <- read.xlsx(xlsxFile = paste0(path$Settings,"/Base/IE_settings.xlsx"),sheet = 2)
   
   path_sel <- list("flow" = paste0(path$Concordance,"/WSA/",
-                                   set$date[set$aggregator == "product"],"_WSA_Source2Root_Product.csv"),
+                                   set$date[set$aggregator == "sector"],"_WSA_Source2Root_Product.csv"),
                    "process" = paste0(path$Concordance,"/WSA/",
-                                      set$date[set$aggregator == "industry"],"_WSA_Source2Root_Industry.csv")
+                                      set$date[set$aggregator == "sector"],"_WSA_Source2Root_Industry.csv")
                    )
   
   Source2Root <- list("WSA" = as.matrix( read.csv(path_sel$flow,header = FALSE) ))
@@ -274,5 +264,4 @@ IEDataProcessing_PIOLab_BuildingTradeBlocks <- function(year,path)
       
     }
   }
-  print("End of IEDataProcessing_PIOLab_BuildingTradeBlocks.")
 }
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_WasteMFAIOExtension.R b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_WasteMFAIOExtension.R
index de6c7c4..fc989df 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_WasteMFAIOExtension.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_WasteMFAIOExtension.R
@@ -10,140 +10,276 @@
 
 IEDataProcessing_PIOLab_WasteMFAIOExtension <- function(year,path)
 {
+  
   print("IEDataProcessing_PIOLab_WasteMFAIOExtension initiated.")
-  #############################################################################
+
+  
+  # Load sector codes of aggregated EXIOBASE data
+  
+  IO.codes <- read.csv(file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/IO_codes.csv"),
+                    row.names = NULL)
+                
+  # Load variables and calculate gross output (x):
   
-  # Load IO codes of aggregated EXIOBASE data
-  load(paste0(path$IE_Processed,"/EXIOWasteMFAIO/IO.codes.RData"))
+  L <- as.matrix( read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_L.csv"),
+                  row.names = NULL,
+                  header = FALSE)
+                )
+  
+  Y <- as.matrix( read.csv(paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Y.csv"),
+                           row.names = NULL,
+                           header = FALSE)
+                )
+  
+  x <- rowSums( L %*% Y ) # Calculate total output and create vector
+  
+  source(paste0(path$Subroutines,"/Numbers2File.R"))  # Load fun. to write arrays to files
   
-  # Load function to create allocation map
-  source(paste0(path$Subroutines,"/makeEndUseMap.R"))
   # 1. Defining functions
   
-  # aggregation function
-  Agg <- function(x)
-  {
-    x <- as.matrix(x) %*% sapply(unique(colnames(x)),"==",colnames(x))
-    return(x)
-  }
+  # Load WSA settings (for codes and feed names):
+  
+  Settings <- read.xlsx(paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"))
+  
+  
+  # Select codes for finished steel:
+  
+  Code <- list("base" = list("Finished" = filter(base$flow,Type == 'Finished') %>% pull(Code),
+                             "Final" = filter(base$flow,Type == 'Final') %>% pull(Code),
+                             "Forgings" = base$flow$Code[base$flow$Name == 'Forgings'],
+                             "CoilSheetStrip" = base$flow$Code[base$flow$Name == "Hot rolled coil-sheet-strip"]),
+               "WSA" = Settings %>% filter(Type == 'Finished') %>% pull(id) )
+  
+ 
+  # Run syntax to load Source2Root maps:
+  
+  set <- read.xlsx(xlsxFile = paste0(path$Settings,"/Base/IE_settings.xlsx"),sheet = 2)
+  
+  paste0(path$Concordance,"/WSA/",set$date[set$aggregator == "sector"],"_WSA_Source2Root_Product.csv")
+  
+  path_sel <- list("flow" = paste0(path$Concordance,"/WSA/",
+                                   set$date[set$aggregator == "sector"],"_WSA_Source2Root_Product.csv"),
+                   "process" = paste0(path$Concordance,"/WSA/",
+                                      set$date[set$aggregator == "sector"],"_WSA_Source2Root_Industry.csv")
+                   )
+  
+  Source2Root <-list("WSA" = as.matrix( read.csv(path_sel$flow,header = FALSE) ))
+  
+  Source2Root$WSA <- Source2Root$WSA[Code$WSA,]  # Select only finished steel
+  
+  Source2Root$WSA <- Source2Root$WSA / rowSums(Source2Root$WSA)  # Create map (normalized concordance)
+  
+  Source2Root[["Cullen"]] <- list("Product" = as.matrix( read.csv(paste0(path$Concordance,"/Cullen/Cullen_Source2Root_Product.csv"),header = FALSE) ),
+                                  "EndUse" = as.matrix( read.csv(paste0(path$Concordance,"/Cullen/Cullen_Source2Root_EndUse.csv"),header = FALSE) ))
+  
+  # Compile Source to mother concordance for finished steel products
+  
+  Source2Mother <- list("WSA" = Source2Root$WSA %*% as.matrix(R2M$flow[,Code$base$Finished]) )
+  
+  # Load WSA Source2Source map for estimate final use of hot rolled coil-sheet-strip
+  
+  Source2Source <- list("WSA" = as.matrix(read.csv(paste0(path$Concordance,"/WSA/WSA_Source2Source.csv"),header = FALSE)) )
+  
+  Source2Source$WSA <- Source2Source$WSA[Code$WSA,Code$WSA]
+  
+  
+  # Create map to allocate mother to source:
+  
+  Mother2Source_Map <- list("WSA" = t(Source2Mother$WSA) / colSums(Source2Mother$WSA) ) 
+  
+  Mother2Source_Map$WSA[is.na(Mother2Source_Map$WSA)] <- 0 # Because forgings is not form WSA, set NA to 0
+  
+  # Create mother2mother concordance = parent to child processes
+  
+  Mother2Mother <-  list("WSA" = Mother2Source_Map$WSA %*% Source2Source$WSA %*% Source2Mother$WSA )
+  
+  colnames(Mother2Mother$WSA) <- rownames(Mother2Mother$WSA) <- Code$base$Finished
+  
+  
+  # Load products to end-use map from Cullen et al 2012
+  
+  Products2EndUse <- read.csv(paste0(path$IE_Processed,"/Cullen/ProductsToEndUse.csv"))
+  
+  rownames(Products2EndUse) <- Products2EndUse$X  # Write products names to row names
+  
+  Products2EndUse$X <- NULL  # Delete product name column 
+  
+  Products2EndUse <- as.matrix(Products2EndUse)
+  
+  # Create Source to Root and Source to mother maps:
+  
+  Source2Root_Map <- list("Cullen" = list("EndUse" = (Source2Root$Cullen$EndUse / rowSums(Source2Root$Cullen$EndUse) ),
+                                          "Product" = (Source2Root$Cullen$Product / rowSums(Source2Root$Cullen$Product) )
+  )
+  )
+  
+  Source2Mother[["Cullen"]] <- list("EndUse" =  Source2Root_Map$Cullen$EndUse %*% as.matrix(R2M$flow[,Code$base$Final]),
+                                    "Product" = Source2Root_Map$Cullen$Product %*% as.matrix(R2M$flow[,Code$base$Finished])
+  )
+  
+  Source2Mother$Cullen$Yields <- Source2Mother$Cullen$EndUse  # Add concordance for yields
+  
+  Source2Mother$Cullen$Yields[Source2Mother$Cullen$Yields > 0] <- 1  # Set shares to 1 for yields
+  
+  
+  # Add mother sector names to maps:
+  
+  colnames(Source2Mother$Cullen$EndUse) <- base$product$Name[Code$base$Final]
+  
+  colnames(Source2Mother$Cullen$Product) <- base$product$Name[Code$base$Finished]
+  
+  # Translate product to end-use map to base classification of mother table: 
+  
+  Products2EndUse <- t(Source2Mother$Cullen$Product)  %*% Products2EndUse %*% Source2Mother$Cullen$EndUse
+  
+  rownames(Products2EndUse) <- Code$base$Finished
+  
+  # Load Yield data from Cullen et al.:
+  
+  Yields <- read.csv(paste0(path$IE_Processed,"/Cullen/FabricationYields.csv"))
+  
+  Yields <- Yields[Yields$Yield.Sector != "All products",] # Remove average yield
+  
+  Yields <- Source2Mother$Cullen$Yields * Yields$Yield.Factor # Distribute yields to base sectors
+  
+  Yields <- apply(Yields, 2, function(x) max(x, na.rm = TRUE))  # Select largest yields available
+  
+  
+  # Load BACI trade data:
+  
+  BACI <- read.csv(paste0(path$IE_Processed,"/BACI/BACI_",year,".csv")) 
+  
+  
+  # Create two empty arrays for steel for the extension (Q) and for the fabrication total use (R)
+  
+  Q <- R <- as.data.frame(matrix(0,length(Code$base$Finished),nrow(IO.codes)))
+  
+  rownames(Q) <- rownames(R) <- base$flow$Name[Code$base$Finished]
   
-  # Writing values of rolled products into arrays (extension and fabrication scrap)
-  WriteValues2Array <- function(data,name,Q,Scrap)
+  
+  # Loop over base regions:
+  
+  for(r in base$region$Code)
   {
-    for(i in 1:nrow(data))
+    
+    map <- Source2Mother$WSA  # Create Map for base region and finished steel 
+    
+    colnames(map) <- Code$base$Finished
+    
+    # Loop over finished steel data feeds:
+    
+    for(p in 1:length(Code$WSA))
     {
-      #print(i)
-      reg <- data$base[i]
-      
-      # call function to create specific map 
-      map <- makeEndUseMap(reg,name)
-      
-      map <- map %>% mutate(Value = Share * data$Quantity[data$base == reg]) %>% 
-        select(index,IO,Yield.Sector,Value) 
       
-      map <- left_join(map,Yields,c("Yield.Sector"),copy = FALSE) %>% mutate(EndUse = Value*Yield.Factor,Scrap = Value - EndUse) %>%
-        select(index,IO,EndUse,Scrap)
+      data <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_",Settings$FeedName[Code$WSA[p]],".csv"))
       
-      index <- map$index+((reg-1)*10)
+      vec <- rep(1,nrow(map)) # create empty vector
       
-      if(name == "Flat")
+      if(r %in% data$base) # Check if region exists in production account
       {
-        Q[1,index] <<- map$EndUse
-        Scrap[1,index] <<- map$Scrap
-      }else
-      {
-        Q[2,index] <<- map$EndUse
-        Scrap[2,index] <<- map$Scrap
+        vec[p] <- filter(data,base == r) %>% pull(Quantity) # Write value in vector   
       }
+      
+      map <- map * vec  # Add value to map
+      
     }
     
-  }
-  
-  # Load processed data from Cullen et al 2012
-  Yields <- read.csv(paste0(path$IE_Processed,"/Cullen/FabricationYields.csv"))
-
-  ##############################################################################
-  # 1. The first section calculates the net use of rolled steel products in regions
-  # by adding imports and subtracting exports from the WSA production values
-  
-  BACI <- read.csv(paste0(path$IE_Processed,"/BACI/BACI_",year,".csv")) # Load trade data
-  
-  # Load finished steel production
-  Flat <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_FlatRolledProducts.csv"))
-  Long <- read.csv(paste0(path$IE_Processed,"/WSA/WSA_",year,"_LongRolledProducts.csv"))
+    # Load forging production
+    data <- read.csv(paste0(path$IE_Processed,"/AligningData/Forgings_",year,".csv"))
+    
+    if(r %in% data$base) # Check if region exists in production account
+    {
+      value <- filter(data,base == r) %>% pull(Quantity)
+    }
+    
+    map <- rbind(map,0) # Add empty row
+    
+    map[nrow(map),colnames(map) == Code$base$Forgings] <- value
+    
+    # Aggregate across sources and transform in data table:
+    
+    df <- data.frame("Code" = Code$base$Finished,  
+                     "Production" = colSums(map),
+                     "Import" = 0,
+                     "Export" = 0,
+                     "Use" = 0)
+    
+    # Read and aggregate exports and imports across trade partners:
+    
+    BACI_export <- filter(BACI,From == r) %>% select(Product,Quantity) %>%
+      group_by(Product) %>% summarise(Quantity = sum(Quantity)) %>%
+      ungroup(Product) %>% filter(Product %in% Code$base$Finished)
+    
+    BACI_import <- filter(BACI,To == r) %>% select(Product,Quantity) %>%
+      group_by(Product) %>% summarise(Quantity = sum(Quantity)) %>%
+      ungroup(Product) %>% filter(Product %in% Code$base$Finished)
+    
+    
+    # Write trade flows into data frame and estimate domestic use:
+    
+    df$Import[df$Code %in% BACI_import$Product] <- BACI_import$Quantity
+    
+    df$Export[df$Code %in% BACI_export$Product] <- BACI_export$Quantity
+    
+    df$Use <- df$Production + df$Import - df$Export
+    
    
-  
-  
-  Flat_trade <- BACI %>% filter(Product == base$product$Code[base$product$Name == "Flat rolled products"]) %>% 
-    select(From,To,Quantity)
-  
-  Long_trade <- BACI %>% filter(Product == base$product$Code[base$product$Name == "Long rolled products"]) %>% 
-    select(From,To,Quantity)
-  
-  # Estimate the net-use of products in countries
-  Flat_import <- Flat_trade %>% group_by(To) %>% summarise(Quantity = sum(Quantity))
-  Flat_export <- Flat_trade %>% group_by(From) %>% summarise(Quantity = sum(Quantity))
-  Flat_trade <- full_join(Flat_import,Flat_export,c("To" = "From"),copy = FALSE)
-  colnames(Flat_trade) <- c("base","import","export")
-  # Regional production + imports - exports = net-use
-  Flat <- full_join(Flat,Flat_trade,c("base"),copy = FALSE) 
-  Flat[is.na(Flat)] <- 0
-  
-  Flat <- Flat %>% mutate(NetUse = Quantity + import - export) 
-  
-  #####################################
-  # When negatives: set net use to import value
-  Flat$NetUse[Flat$NetUse < 0] <- Flat$import[Flat$NetUse < 0] 
-  Flat <- Flat %>% select(base,NetUse)
-  colnames(Flat)[2] <- "Quantity"
-  #####################################
-  
-  Long_import <- Long_trade %>% group_by(To) %>% summarise(Quantity = sum(Quantity))
-  Long_export <- Long_trade %>% group_by(From) %>% summarise(Quantity = sum(Quantity))
-  Long_trade <- full_join(Long_import,Long_export,c("To" = "From"),copy = FALSE)
-  colnames(Long_trade) <- c("base","import","export")
-  # Regional production + imports - exports = net-use
-  Long <- full_join(Long,Long_trade,c("base"),copy = FALSE) 
-  Long[is.na(Long)] <- 0
-  
-  Long <- Long %>% mutate(NetUse = Quantity + import - export) 
-  #####################################
-  # When negatives: set net use to import value
-  Long$NetUse[Long$NetUse < 0] <- Long$import[Long$NetUse < 0] 
-  #####################################
-  Long <- Long %>% select(base,NetUse)
-  colnames(Long)[2] <- "Quantity"
-  
-  ##############################################################################
-  # 3. Start processing
-  
-  # Create two empty arrays for steel for the extension (Q) and for the fabrication scrap
-  Scrap <- Q <- as.data.frame(matrix(0,2,nrow(IO.codes)))
-  rownames(Scrap) <- rownames(Q) <- c("Flat","Long")
-  
-  # Execute functions
-  # Warnings are turned of for the run.
-  options(warn = -1)
-  WriteValues2Array(Flat,"Flat",Q,Scrap)
-  WriteValues2Array(Long,"Long",Q,Scrap)
-  options(warn = 0)
+    # Subtract production values of cold rolled products from hot rolled coil-sheet-strip
+    
+    Subtract <- sum(Mother2Mother$WSA[rownames(Mother2Mother$WSA) == Code$base$CoilSheetStrip,] * df$Production)
+    
+    df$Use[df$Code == Code$base$CoilSheetStrip] <- df$Use[df$Code == Code$base$CoilSheetStrip] - Subtract 
+    
+    print("minimum values before adjustment to domestic production:")
+    print(df[df$Use < 0,])
+    
+    df$Use[df$Use < 0] <- df$Production[df$Use < 0] # Set use to production where negative
+    
+    
+    print(paste0("Minimum of ",base$region$Name[r],": ",min(df$Use))) # Check min.
+    
+    
+    x_filter <- diag(x[IO.codes$index[IO.codes$base == r]])  # Read production of region r
+    
+    x_filter[x_filter > 0] <- 1  # Set non-zero values to 1 for filtering 
+    
+    map <- Products2EndUse %*% x_filter  # Apply filter
+    
+    colnames(map) <- IO.codes$commodity[IO.codes$base == r]
+    
 
-  # Save extension of the MFA-Waste-IO Model
-  save(Q,file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Q.RData"))
-  
-  # Prepate fabrication scrap data for export
-  Scrap <- colSums(Scrap)
-  Scrap <- data.frame(IO.codes,
-                     "Quantity" = Scrap,
-                     stringsAsFactors = FALSE)
+    map[rownames(map) == Code$base$Forgings, c(1,2,7,8)] <- 1  # Add forgings (no information)
+    
+    map <- ( map / rowSums(map) ) * df$Use  # Create map and add material use
+    
+    Scrap <- colSums(map) * (1-Yields)  # Estimate scrap flows
+    
+    # Write into data frame with base industry codes
+    
+    Scrap <- data.frame("base" = base$process$Code[base$process$Type == "Final"],
+                        "Quantity" = Scrap)
+    
+    rownames(Scrap) <- NULL 
+    
+    # Write Scrap data to folder
+    
+    write.csv(Scrap,file = paste0(path$IE_Processed,"/Cullen/FabricationScrap_",
+                                  year,"_",base$region$Name[r],".csv"),row.names = FALSE)
+    
+    # Write fabrication-use data to folder
+    
+    Numbers2File(map,paste0(path$IE_Processed,"/Cullen/FabricationUse_",year,
+                            "_",base$region$Name[r],".csv"))
+    
+    map <- map %*% diag(Yields)  # Estimate useful output
+    
+    Q[,IO.codes$index[IO.codes$base == r]] <- map  # Write values into extension
+    
+  }
   
-  # Check if subfolder in processed data exists and if not create it
-  path_set <- paste0(path$IE_Processed,"/FabricationScrap")
-  if(!dir.exists(path_set)) dir.create(path_set)
   
-  # Write to folder
-  write.csv(Scrap,file = paste0(path_set,"/FabricationScrap_",year,".csv"),row.names = FALSE)
+  # Save extension of the MFA-Waste-IO Model
   
-  print("IEDataProcessing_PIOLab_WasteMFAIOExtension finished.")
+  Numbers2File( Q, paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Q.csv") )
   
 }
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R
index 85ec43c..7a37a63 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R
@@ -6,10 +6,10 @@
 #                                       #
 #########################################
 
-IEDataProcessing_PIOLab_WasteMFAIOModelRunV2 <- function(year,path)
+IEDataProcessing_PIOLab_WasteMFAIOModelRun <- function(year,path)
 {
   
-  print("IEDataProcessing_PIOLab_WasteMFAIOModelRunV2 initiated.")
+  print("IEDataProcessing_PIOLab_WasteMFAIOModelRun initiated.")
   
   
   # Load codes and indices of model:
@@ -20,7 +20,7 @@ IEDataProcessing_PIOLab_WasteMFAIOModelRunV2 <- function(year,path)
   
   Y.codes <- read.csv( paste0(path$IE_Processed,"/EXIOWasteMFAIO/Y_codes.csv"))
   
-  Y.codes["Key"] <- paste0(Y.codes$base,"-",Y.codes$sector)
+  Y.codes["Key"] <- paste0(Y.codes$base,"-",Y.codes$demand)
   
   
   # Load model variables:
@@ -36,13 +36,13 @@ IEDataProcessing_PIOLab_WasteMFAIOModelRunV2 <- function(year,path)
   
   x <- rowSums( L %*% Y )  # Calculate gross output
   
-  # Calculate multipliers
   
-  E <- colSums(Q)/x
+  
+  E <- colSums(Q)/x  # Calculate direct intensity
   
   E[is.na(E)] <- 0
   
-  MP <- L*E
+  MP <- L*E  # Calculate material mulitplier
   
   # 1. Estimate flows from fabrication to final product
   
@@ -79,9 +79,9 @@ IEDataProcessing_PIOLab_WasteMFAIOModelRunV2 <- function(year,path)
   
   # Set col and row names:
   
-  colnames(FP) <- paste0(Y.codes$base,"-",Y.codes$demand)
+  colnames(FP) <- Y.codes$Key
   
-  rownames(FP) <- paste0(IO.codes$base,"-",IO.codes$sector)
+  rownames(FP) <- IO.codes$Key
   
   FP[1:nrow(Y),1:ncol(Y)] <- 0  # Set values to zero
   
@@ -105,37 +105,22 @@ IEDataProcessing_PIOLab_WasteMFAIOModelRunV2 <- function(year,path)
                    copy = FALSE, 
                    suffix = c(".from",".to") ) 
   
-  FP <- left_join( x = FP, y = select(IO.codes,base,sector,Key), 
+  FP <- left_join( x = FP, y = select(Y.codes,base,demand,Key), 
                    by = c("To" = "Key"), 
                    copy = FALSE, 
                    suffix = c(".from",".to") ) 
   
-  Fabrication2Final <- select(FP,-From,-To) # Remove key (not needed anymore)
-  
-  
-  
-  
-  colSums(SteelInFinalDemand)/1000000
+  FinalDemand <- select(FP,-From,-To) # Remove key (not needed anymore)
   
+  # 4. Save results to folder 
   
-  sum(SteelInFinalDemand)
+  write.csv(Fabrication2Final,
+            file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Fabrication2FinalDemand.csv"),
+            row.names = FALSE)
   
-  SteelInFinalDemand <- tibble::rownames_to_column(SteelInFinalDemand, "Key")
-  SteelInFinalDemand <- melt(SteelInFinalDemand,id.vars = "Key")
-  colnames(SteelInFinalDemand) <- c("Key","To.Region","Quantity")
-  # Warning messages are turned off for the following join
-  options(warn = -1)
-  SteelInFinalDemand <- left_join(SteelInFinalDemand,IO.codes,c("Key"),copy = FALSE) %>%
-    select(base,To.Region,commodity,Quantity)
-  options(warn = 0)
-  colnames(SteelInFinalDemand)[c(1,3)] <- c("From.Region","Commodity")
-  SteelInFinalDemand <- left_join(SteelInFinalDemand,base$product,c("Commodity" = "Name"),copy = FALSE) %>%
-    select(From.Region,To.Region,Code,Quantity)
-  colnames(SteelInFinalDemand)[3] <- "Product"
+  write.csv(FinalDemand,
+            file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_FinalDemand.csv"),
+            row.names = FALSE)
   
-  # 4. Save results to folder 
-  write.csv(FabricationToFinal,file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_FabricationToFinalDemand.csv"),row.names = FALSE)
-  write.csv(SteelInFinalDemand,file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_SteelInFinalDemand.csv"),row.names = FALSE)
   
-  print("DataProcessing_PIOLab_WasteMFAIOModelRun finished.")
 }
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_BACI.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_BACI.R
index 0e9f2e5..d6e0b95 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_BACI.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_BACI.R
@@ -36,8 +36,7 @@ IEFeed_PIOLab_BACI <- function(year,path)
   write.csv(data_clean,file = paste0(path_set,"BACI_",year,".csv"),row.names = FALSE)
     
   # Note that trade flows are measured in metric tons of material 
-  
-  print("IEFeed_PIOLab_BACI finished.")
+
 }
 
 
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_Cullen.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_Cullen.R
index 401397a..5584ed7 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_Cullen.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_Cullen.R
@@ -80,6 +80,4 @@ IEFeed_PIOLab_Cullen <- function(path)
   # Write to file
   
   write.csv(Conco,file = paste0(path_set,"/ProductsToEndUse.csv"))
-  
-  print("IEFeed_PIOLab_Cullen finished.")
 }
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_EOL.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_EOL.R
index 86d389d..8445905 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_EOL.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_EOL.R
@@ -35,9 +35,6 @@ IEFeed_PIOLab_EOL <- function(year,path)
   write.csv(data_clean,
             file = paste0(path_set,"/EOL_",year,".csv"),
             row.names = FALSE)
-  
-  print("IEFeed_PIOLab_EOL finished.")
-  
 }
 
 
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_EXIOWasteMFAIO.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_EXIOWasteMFAIO.R
index baf9954..b9df14b 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_EXIOWasteMFAIO.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_EXIOWasteMFAIO.R
@@ -9,138 +9,263 @@
 
 IEFeed_PIOLab_EXIOWasteMFAIO <- function(year,path)
 {
+  
   print("IEFeed_PIOLab_EXIOWasteMFAIO initiated.")
-  # Function for aggregating the IOT
-  Agg <- function(x)
-  {
-    x <- as.matrix(x) %*% sapply(unique(colnames(x)),"==",colnames(x))
-    return(x)
-  }
-  
-  # 1. Loading the original EXIOBASE 3.6 variables
-  # Transaction matrix:
-  load(paste0(path$Raw,"/EXIOWasteMFAIO/",year,"_Z.RData"))
-  # Final demand:
-  load(paste0(path$Raw,"/EXIOWasteMFAIO/",year,"_Y.RData"))
-  # IO sector codes:
-  load(paste0(path$Raw,"/EXIOWasteMFAIO/","IO.codes.RData")) 
-  IO.codes <- IO.codes %>% select(Index,Product.Name,Country.Code)
-  regions <- unique(IO.codes$Country.Code)
-  # Final demand codes
-  load(paste0(path$Raw,"/EXIOWasteMFAIO/","Y.codes.RData"))
-  
-  # Load the root classifciation
-  root_class <- root$region %>% select(Code,ISO2digitCode)
-  
-  # Load region aggregator
+  
+  
+  # Set path tpo functions:
+  
+  fun <- list(paste0(path$Subroutines,"/Root2Base_RegionAggregator.R"),
+              paste0(path$Subroutines,"/Agg.R"),
+              paste0(path$Subroutines,"/Numbers2File.R"))
+  
+  lapply(fun,source)  # Load functions
+  
+  
   source(paste0(path$Subroutines,"/Root2Base_RegionAggregator.R"))
   
   # Load source-to-root and calculate Source2Base concordances
+  
   Source2Root <- read.csv(paste0(path$Concordance,"/EXIOBASE/Source2Root_Region_EXIOBASE.csv"),
                           header = FALSE)
   
-  Root2Base <- as.matrix(RegionAggregator)
-    
+  # Root2Mother <- as.matrix(RegionAggregator)
+  
   Source2Root <- as.matrix(Source2Root)
-  # Create map from concordance
-  Source2Root <- Source2Root/rowSums(Source2Root)
-  Root2Base <- as.matrix(Root2Base)
+  
+ 
+  
+  Source2Root <- Source2Root/rowSums(Source2Root)   # Create map from concordance
+  
+  # Root2Mother <- as.matrix(Root2Mother)
+  
   # Create Source-to-base matrix
-  Source2Base <- Source2Root %*% Root2Base
+  
+  Source2Mother <- Source2Root %*% R2M$region
   
   # Create concordance
-  Source2Base <- t(Source2Base)*(1:ncol(Source2Base))
-  rownames(Source2Base) <- NULL
-  Source2Base <- melt(Source2Base)
-  Source2Base <- filter(Source2Base,value > 0) %>% select(-value)
-  colnames(Source2Base) <- c("base","root")
-  
-  IO.codes <- data.frame(IO.codes,"NewProducts" = "NEC",stringsAsFactors = FALSE)
-  IO.codes["base"] <- rep(Source2Base$base,each = 200)
-  
-  # select steel containing commodities
-  steel.id <- c(117:125,150)
-  IO.codes$NewProducts[IO.codes$Product.Name %in% IO.codes$Product.Name[steel.id]] <- IO.codes$Product.Name[IO.codes$Product.Name %in% IO.codes$Product.Name[steel.id]] 
-
-  # Create key for aggregation
-  IO.codes <- data.frame(IO.codes,
-                         "Key" = paste0(as.character(IO.codes$base),"§",IO.codes$NewProducts),
-                         stringsAsFactors = FALSE)
-  
-  # Aggregate transaction matrix
-  colnames(Z) <- IO.codes$Key
-  Z <- Agg(Z)
-  Z <- t(Z)
-  colnames(Z) <- IO.codes$Key
-  Z <- Agg(Z)
-  Z <- t(Z)
-
-  # Aggregate final demand
-  Y.codes["base"] <- rep(Source2Base$base,each = 7) 
-  colnames(Y) <- Y.codes$base
-  Y <- Agg(Y)
-  Y <- t(Y)
-  colnames(Y) <- IO.codes$Key
-  Y <- Agg(Y)
-  Y <- t(Y)
-  # Rearrange columns so that the region code of the base table is in increasing order 
-  Y <- Y[,as.character(1:max(Source2Base$base))]
-  
-  # Create new IO.codes to rearrange the variables so that base region codes increases
-  IO.codes <- colnames(Z)
-  IO.codes <- as.data.frame(str_split_fixed(IO.codes, "§", 2),stringsAsFactors = FALSE)
-  colnames(IO.codes) <- c("base","commodity")
-  storage.mode(IO.codes$base) <- "numeric"
-  IO.codes <- IO.codes[order(IO.codes$base),]
-  IO.codes$index <- 1:nrow(IO.codes)
-  IO.codes$Key <- paste0(as.character(IO.codes$base),"§",IO.codes$commodity)
-  IO.codes <- IO.codes[,c("index","base","commodity","Key")]
-  
-  # New Y.codes 
-  Y.codes <- data.frame("base" = 1:max(Source2Base$base),
-                        "Region.Name" = base$region$Name, 
-                        stringsAsFactors = FALSE)
-  
-  # Rearrange Z & Y (if necessary)
-  Z <- Z[,IO.codes$Key]
-  Z <- t(Z)
-  Z <- Z[,IO.codes$Key]
-  Y <- t(Y)
-  Y <- Y[,IO.codes$Key]
-  Y <- t(Y)
-
-  # Compute new variables of the aggreagted table
-  x <- rowSums(Z) + rowSums(Y)
-  A <- t(t(Z)/x)
-  A[is.na(A)] <- 0
-  
-  # Delete input/flows not containing steel
-  A[IO.codes$index[IO.codes$commodity == "NEC"],] <- 0
-  A[,IO.codes$index[IO.codes$commodity == "NEC"]] <- 0
-  L <- solve(diag(nrow(A))-A)
-  
-  # Construct new final demand (now only steel-containing products) 
-  Y[IO.codes$index[IO.codes$commodity == "NEC"],] <- 0
+  
+  Source2Mother <- t(Source2Mother)*(1:ncol(Source2Mother))
+  
+  rownames(Source2Mother) <- NULL
+  
+  Source2Mother <- melt(Source2Mother)
+  
+  Source2Mother <- filter(Source2Mother,value > 0) %>% select(-value)
+  
+  colnames(Source2Mother) <- c("MotherRegion","SourceRegion")
+  
+  
+  # Loading IO codes:
+  
+  Code <- list("Source" = list("Product" = read.csv(paste0(path$Raw,"/EXIOWasteMFAIO/IO_product_codes.csv")),
+                               "Industry" = read.csv(paste0(path$Raw,"/EXIOWasteMFAIO/IO_industry_codes.csv")),
+                               "Demand" = read.csv(paste0(path$Raw,"/EXIOWasteMFAIO/IO_finaldemand_codes.csv")) 
+                               ) 
+               )
+               
+  
+  Code$Source$Product <- left_join(Code$Source$Product,Source2Mother,by = c("RegionCode" = "SourceRegion"))
+  
+  Code$Source$Industry <- left_join(Code$Source$Industry,Source2Mother,by = c("RegionCode" = "SourceRegion"))
+  
+  Code$Source$Demand <- left_join(Code$Source$Demand,Source2Mother,by = c("RegionCode" = "SourceRegion"))
+  
+ 
+  n_pro <- max(Code$Source$Product$MotherProductCode)  # Number of manufactured products
+  
+  n_ind <- max(Code$Source$Industry$MotherIndustryCode)  # Number of manufacturing industries
+  
+  n_reg <- ncol(R2M$region)  # Number of regions
+  
+  
+  # 1. Load EXIOBASE 3.6 mrSUTs
+  
+  # Supply table (products by industry):
+  
+  S <- read.csv(paste0(path$Raw,"/EXIOWasteMFAIO/MRSUT_",year,"/supply.csv"),header = FALSE)
+  
+  S <- as.matrix(S) # Transform into matrix
+  
+  # Aggregate from source to mother classification:
+  
+  S <- Agg(S,paste0(Code$Source$Industry$MotherRegion,"-",Code$Source$Industry$MotherIndustryCode),2)
+  
+  S <- Agg(S,paste0(Code$Source$Product$MotherRegion,"-",Code$Source$Product$MotherProductCode),1)
+  
+  # Use table (products by industry):
+  
+  U <- read.csv(paste0(path$Raw,"/EXIOWasteMFAIO/MRSUT_",year,"/use.csv"),header = FALSE)
+  
+  U <- as.matrix(U)  # Transform into matrix
+  
+  # Aggregate from source to mother:
+  
+  U <- Agg(U,paste0(Code$Source$Industry$MotherRegion,"-",Code$Source$Industry$MotherIndustryCode),2)
+  
+  U <- Agg(U,paste0(Code$Source$Product$MotherRegion,"-",Code$Source$Product$MotherProductCode),1)
+  
+  
+  # Final demand block (product by final demand category)
+  
+  Y <- read.csv(paste0(path$Raw,"/EXIOWasteMFAIO/MRSUT_",year,"/final_demand.csv"),header = FALSE)
+  
+  Y <- as.matrix(Y)  # Transform into matrix
+  
+  # Aggregate from source to mother:
+  
+  Y <- Agg(Y,paste0(Code$Source$Demand$MotherRegion,"-",Code$Source$Demand$FinalDemandCode),2)
+  
+  Y <- Agg(Y,paste0(Code$Source$Product$MotherRegion,"-",Code$Source$Product$MotherProductCode),1)
+  
+  
+  x <- colSums(S)  # Gross production of all industry
 
-  # Remove NEC product from WIO model
-  L <- L[IO.codes$index[IO.codes$commodity != "NEC"],IO.codes$index[IO.codes$commodity != "NEC"]]
-  Y <- Y[IO.codes$index[IO.codes$commodity != "NEC"],]
-  x <- rowSums(L%*%Y)
-  IO.codes <- IO.codes[IO.codes$commodity != "NEC",]
-  IO.codes$index <- 1:nrow(IO.codes)
-  IO.codes$WIOcode <- 1:length(unique(IO.codes$commodity))
-  IO.codes$base.product <- base$product$Code[base$product$Name  %in% IO.codes$commodity]
+  q <- rowSums(S)  # Gross production of all products
+  
+  
+  D <- t(S/q)  # Matrix of all commodity output proportions (industry by product)
+  
+  D[is.na(D)] <- 0  # Set NaN (due to zero gross output) to zero
+  
+  C <- t(t(S)/x)  # Matrix of all industry output proportions (product by industry)
+  
+  C[is.na(C)] <- 0  # Set NaN (due to zero gross output) to zero
+  
+  
+  B <- t(t(U)/x) # All commodity by industry coefficient matrix
+  
+  B[is.na(B)] <- 0   # Set NaN (due to zero gross output) to zero
+  
+  A <- B %*% D  # Calculate pro-by-pro technology matrix
+  
+  
+  filter <- rep(c(0,rep(1,n_pro)),n_reg)  # Create filter vector
+  
+  A_f <- diag(filter) %*% A %*% diag(filter)  # Apply filter
+  
+  I <- diag( rep(1, (n_pro+1) * n_reg ) )  # Create Identity matrix
+  
+  
+  # Filter mrSUTs for steel containing sectors and recalculate market share matrices:
+  
+  S_f <-  diag(filter) %*% S %*% diag(filter)  # Filter supply table
+  
+  x_f <- colSums(S_f)  # Filter industry output
+  
+  q_f <- rowSums(S_f)  # Filter product output
+  
+  
+  D_f <- t(S_f/q_f)  # Matrix of all commodity output proportions (industry by product)
+  
+  D_f[is.na(D_f)] <- 0  # Set NaN (due to zero gross output) to zero
+  
+  C_f <- t(t(S_f)/x_f)  # Matrix of all industry output proportions (product by industry)
+  
+  C_f[is.na(C_f)] <- 0  # Set NaN (due to zero gross output) to zero
+  
+  
+  
+  TRM_f <- solve( I - A_f ) %*% C_f  # Calculate pro-by-ind total requirement matrix
+  
+  Y_f <- D_f %*% diag(filter) %*% Y  # Filter and calculate final demand by industry output
+  
+  print( sum(TRM_f %*% rowSums(Y_f)) / sum(x) )  # Share of manuf. steel in production
+  
+  
+  Code[["Mother"]] <- list( "Sector" = colnames(A),
+                            "Demand" = colnames(Y) )  # Create new codes for WIO-MFA model
+  
+  
+  # Split colname string into region and sector code:
+  
+  Code$Mother$Sector <- as.data.frame(str_split_fixed(Code$Mother$Sector, "-", 2),stringsAsFactors = FALSE)
+  
+  Code$Mother$Demand <- as.data.frame(str_split_fixed(Code$Mother$Demand, "-", 2),stringsAsFactors = FALSE)
+  
+  # Change storage mode to numeric:
+  
+  Code$Mother$Sector <- as.data.frame(apply(Code$Mother$Sector, c(2),as.numeric))
+  
+  Code$Mother$Demand <- as.data.frame(apply(Code$Mother$Demand, c(2),as.numeric))
+  
+  # Add indices:
+  
+  Code$Mother$Sector["index"] <- 1:nrow(Code$Mother$Sector)
+  
+  Code$Mother$Demand["index"] <- 1:nrow(Code$Mother$Demand)
+  
+  
+  # Set colnames:
+  
+  colnames(Code$Mother$Sector) <- c("base","sector","index")
+  
+  colnames(Code$Mother$Demand) <- c("base","demand","index")
+  
+  
+  # Remove NEC sectors and export demand category (which is always zero):
+  
+  Code$Mother$Sector <- Code$Mother$Sector[Code$Mother$Sector$sector != 0,] 
+  
+  Code$Mother$Demand <- Code$Mother$Demand[Code$Mother$Demand$demand != 7,]
+  
+  
+  # Re-order region list:
+  
+  Code$Mother$Sector <- Code$Mother$Sector[order(Code$Mother$Sector$base),] 
+  
+  Code$Mother$Demand <- Code$Mother$Demand[order(Code$Mother$Demand$base),] 
+  
+  
+  remove(A,x,q,Y,S,U)  # Remove tables not needed
+  
+  
+  # Filter only WIO-MFA sectors:
+  
+  A <- A_f[Code$Mother$Sector$index,Code$Mother$Sector$index]
+  
+  S <- S_f[Code$Mother$Sector$index,Code$Mother$Sector$index]
+  
+  TRM <- TRM_f[Code$Mother$Sector$index,Code$Mother$Sector$index]
+  
+  Y <- Y_f[Code$Mother$Sector$index,Code$Mother$Demand$index]
+  
+  
+  # Adapt indices to new model classification:
+  
+  Code$Mother$Sector$index <- 1:nrow(Code$Mother$Sector)
+  
+  Code$Mother$Demand$index <- 1:nrow(Code$Mother$Demand)
+  
   
   # Check if subfolder in processed data exists and if not create it
+  
   path_set <- paste0(path$IE_Processed,"/EXIOWasteMFAIO")
+  
   if(!dir.exists(path_set)) dir.create(path_set)
   
-  write.table(L,file = paste0(path_set,"/",year,"_L.csv"),row.names = FALSE,col.names = FALSE,sep = ",")
-  write.table(Y,file = paste0(path_set,"/",year,"_Y.csv"),row.names = FALSE,col.names = FALSE,sep = ",")
-  write.table(x,file = paste0(path_set,"/",year,"_x.csv"),row.names = FALSE,col.names = FALSE,sep = ",")
-  save(IO.codes,file = paste0(path_set,"/IO.codes.RData"))
-  save(Y.codes,file = paste0(path_set,"/Y.codes.RData"))
   
-  print("IEFeed_PIOLab_EXIOWasteMFAIO finished.")
+  # Write codes and tables to folder: 
+  
+  write.csv(x = Code$Mother$Sector,
+            file =  paste0(path$IE_Processed,"/EXIOWasteMFAIO/IO_codes.csv"),
+            row.names = FALSE)
+  
+  write.csv(x = Code$Mother$Demand, 
+            file = paste0(path$IE_Processed,"/EXIOWasteMFAIO/Y_codes.csv"),
+            row.names = FALSE )
+  
+  
+  Numbers2File(Y,paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_Y.csv"))
+  
+  Numbers2File(A,paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_A.csv"))
+  
+  Numbers2File(TRM,paste0(path$IE_Processed,"/EXIOWasteMFAIO/",year,"_L.csv"))
+  
+  
+  
+  print(paste0( "Share of final demand in total gross production: ", 
+                 round( sum(Y)/sum(TRM %*% Y),digits = 2) )
+        ) 
   
-}
\ No newline at end of file
+}
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_Grades.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_Grades.R
index b43a693..f44c961 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_Grades.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_Grades.R
@@ -40,6 +40,4 @@ IEFeed_PIOLab_Grades <- function(path)
   if(!dir.exists(path_set)) dir.create(path_set)
   
   write.csv(data_clean,file = paste0(path_set,"/IronOreGrades.csv"),row.names = FALSE)
-  
-  print("IEFeed_PIOLab_Grades finished.")
 }
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_IEA.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_IEA.R
index 519d209..239c536 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_IEA.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_IEA.R
@@ -76,6 +76,4 @@ IEFeed_PIOLab_IEA <- function(year,path)
             file = paste0(path_set,"/IEA_",year,"_SteelIndustryEnergyConsumption.csv"),
             row.names = FALSE)
   
-  print("IEFeed_PIOLab_IEA finished.")
-  
 }
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_IRP.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_IRP.R
index 3e8f8b7..a9fe0b5 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_IRP.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_IRP.R
@@ -24,6 +24,4 @@ IEFeed_PIOLab_IRP <- function(year,path)
   
   # Export data to folder (note that the unit is metric tons)
   write.csv(data,file = paste0(path_set,"/IRP_",year,".csv"),row.names = FALSE)
-  
-  print("IEFeed_PIOLab_IRP finished")
 }
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_SteelIndustryYields.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_SteelIndustryYields.R
index 708929f..a6b6a03 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_SteelIndustryYields.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_SteelIndustryYields.R
@@ -16,6 +16,5 @@ IEFeed_PIOLab_SteelIndustryYields <- function(path)
   
   # Write to folder 
   write.csv(data,file = paste0(path$IE_Processed,"/WSA/SteelIndustryYields.csv"),row.names = FALSE)
-  
-  print("IEFeed_PIOLab_SteelIndustryYields finished.")
+
 }
\ No newline at end of file
diff --git a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_WSA.R b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_WSA.R
index ab30554..f748bb2 100644
--- a/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_WSA.R
+++ b/Rscripts/IEfeeds_code/IE_subroutines/IEFeed_PIOLab_WSA.R
@@ -41,5 +41,4 @@ IEFeed_PIOLab_WSA <- function(year,path)
               file = paste0(path_set,"/WSA_",year,"_",item_name,".csv"),
               row.names = FALSE)
   }
-  print("IEFeed_PIOLab_WSA finished.")
 }
diff --git a/Rscripts/IEfeeds_code/Ind30Pro39v1_InitialEstimate.R b/Rscripts/IEfeeds_code/Ind30Pro39v1_InitialEstimate.R
index a10abe1..b2e4203 100644
--- a/Rscripts/IEfeeds_code/Ind30Pro39v1_InitialEstimate.R
+++ b/Rscripts/IEfeeds_code/Ind30Pro39v1_InitialEstimate.R
@@ -7,7 +7,7 @@
 ###############################################
 # IE feed for 20 industries/processes and 22 products base classifications
 # hanspeter.wieland@wu.ac.at (c)
-# 02.19.2020 
+# 04.16.2020 
 
 # In case the code is executed not on the server (and the GUI) for debugging, 
 # the user can choose the desired region aggregator by setting the following variable
@@ -16,7 +16,7 @@
 # test_regagg <- "049"
 
 ################################################################################
-# 1. Set up environment for building the initial estimate
+### 1. Set up workplace for building the initial estimate
 
 IEdatafeed_name <- "Ind30Pro39v1" 
 
@@ -38,10 +38,6 @@ source(paste0(path$root,"Rscripts/Subroutines/Read_BaseClassification.R"))
 # Read root to mother sector aggregators
 source(paste0(path$root,"Rscripts/Subroutines/Load_Root2Mother_sectors.R"))
 
-# Read root to mother region aggregator (from mat-file if available)
-# source(paste0(path$root,"Rscripts/Subroutines/Load_Root2Mother_regions.R"))
-
-
 # Read region aggregation from classification to set the right path for the IE data
 
 if(max(base$region$Code) < 10) 
@@ -64,13 +60,10 @@ remove(regagg)
 if(!dir.exists(path$Agg_Processed)) dir.create(path$Agg_Processed)
 
 # Check whether output folder for processed data for the specific aggregation exists, if yes, delete it
-
 if(dir.exists(path$IE_Processed)) unlink(path$IE_Processed,recursive = TRUE) 
-
 dir.create(path$IE_Processed)
 
 # Check if ALANG files of old initial estimate exist and delete
-
 source(paste0(path$Subroutines,"/DeleteALANGfilesOfOldIEfeeds.R"))
 
 # Functions to process raw data for Initial Estimate
@@ -82,68 +75,48 @@ IE_fun <- list("/IEFeed_PIOLab_WSA.R",
                "/IEFeed_PIOLab_Grades.R",
                "/IEFeed_PIOLab_EOL.R",
                "/IEFeed_PIOLab_IEA.R",
-               "/IEFeed_PIOLab_EXIOWasteMFAIOV2.R",
+               "/IEFeed_PIOLab_EXIOWasteMFAIO.R",
                "/IEFeed_PIOLab_Cullen.R",
-               "/IEDataProcessing_PIOLab_AligningDataV2.R",
-               "/IEDataProcessing_PIOLab_WasteMFAIOExtensionV2.R",
-               "/IEDataProcessing_PIOLab_WasteMFAIOModelRunV2.R",
-               "/IEDataProcessing_PIOLab_BuildingDomesticTablesV2.R",
+               "/IEDataProcessing_PIOLab_AligningData.R",
+               "/IEDataProcessing_PIOLab_WasteMFAIOExtension.R",
+               "/IEDataProcessing_PIOLab_WasteMFAIOModelRun.R",
+               "/IEDataProcessing_PIOLab_BuildingDomesticTables.R",
                "/IEDataProcessing_PIOLab_BuildingTradeBlocks.R",
                "/IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables.R")
 
 IE_fun <- paste0(path$IE_Subroutines,IE_fun)  # Add path to functions
-
-lapply(IE_fun,source)  # Load functions into workspace
+lapply(IE_fun,source)                         # Load functions into workspace
 
 
-# 2. Commencing data pre-processing (from source to root classification)
-
-IEFeed_PIOLab_WSA(year,path)  # WSA production numbers
+### 2. Commencing data pre-processing (from source to root classification)
 
+IEFeed_PIOLab_WSA(year,path)             # WSA production numbers
 IEFeed_PIOLab_SteelIndustryYields(path)  # Yield information
+IEFeed_PIOLab_BACI(year,path)            # Trade data
+IEFeed_PIOLab_IRP(year,path)             # Extraction data
+IEFeed_PIOLab_Grades(path)               # Ore grades
+IEFeed_PIOLab_EOL(year,path)             # end-of-life i.e. old scrap
+IEFeed_PIOLab_IEA(year,path)             # IEA energy data
+IEFeed_PIOLab_EXIOWasteMFAIO(year,path)  # Aggregate EXIOBASE Waste-MFA IO model (WIO)
+IEFeed_PIOLab_Cullen(path)               # Fabrication yields from Cullen et al. 2012
 
-IEFeed_PIOLab_BACI(year,path)  # Trade data
-
-IEFeed_PIOLab_IRP(year,path)  # Extraction data
-
-IEFeed_PIOLab_Grades(path)  # Ore grades
-
-IEFeed_PIOLab_EOL(year,path)  # end-of-life i.e. old scrap
-
-IEFeed_PIOLab_IEA(year,path) # IEA energy data
-
-IEFeed_PIOLab_EXIOWasteMFAIOV2(year,path) # Aggregate EXIOBASE Waste-MFA IO model (WIO)
 
-IEFeed_PIOLab_Cullen(path)  # Fabrication yields from Cullen et al. 2012
+### 3. Commencing data processing
 
+IEDataProcessing_PIOLab_AligningData(year,path)        # Align WSA, IRP extraction
+IEDataProcessing_PIOLab_WasteMFAIOExtension(year,path) # Compile extension for WIO  
+IEDataProcessing_PIOLab_WasteMFAIOModelRun(year,path)  # Run WIO Model calculation
 
-# 3. Commencing data processing
-
-IEDataProcessing_PIOLab_AligningDataV2(year,path) # Align WSA, IRP extraction
-
-IEDataProcessing_PIOLab_WasteMFAIOExtensionV2(year,path) # Compile extension for WIO  
-
-IEDataProcessing_PIOLab_WasteMFAIOModelRunV2(year,path) # Run WIO Model calculation
-
-
-# Compile domestic SUTs
-
-IEDataProcessing_PIOLab_BuildingDomesticTablesV2(year,path)
-
-# Compiling trade blocks
+### 4. Compile domestic SUTs, trade blocks and S8 files thereof
 
+IEDataProcessing_PIOLab_BuildingDomesticTables(year,path)
 IEDataProcessing_PIOLab_BuildingTradeBlocks(year,path)
-
-################################################################################
-# 4. Create S8 files (see AISHA manual annex for further information) for easy data import to AISHA 
-   
 IEDataProcessing_PIOLab_BuildS8fromSupplyUseTables(year,path)
 
-################################################################################
-# 5. Write ALANG commands
-print("Start writing ALANG commands.")
+### 5. Write ALANG commands
 
 # Set up wrapper for adding rows to ALANG
+
 NewALANG <- function(name,SE,ALANG)
 {
   file <- paste0("S8 ",path$mother,"Data/IE/",gsub("-","",Sys.Date()),
@@ -162,7 +135,7 @@ NewALANG <- function(name,SE,ALANG)
 # Create empty file with header
 source(paste0(path$Subroutines,"/makeALANGheadline.R"))
 
-RSE <- read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/IE_RSE_settings.xlsx"))
+RSE <- read.xlsx( paste0(path$Settings,"/Base/IE_settings.xlsx"), sheet = 3 )
 
 # Write ALANG commands
 
diff --git a/Rscripts/Subroutines/InitializationR.R b/Rscripts/Subroutines/InitializationR.R
index fb298dc..5b92678 100644
--- a/Rscripts/Subroutines/InitializationR.R
+++ b/Rscripts/Subroutines/InitializationR.R
@@ -57,14 +57,21 @@ path <- list("Raw" = paste0(root_folder,"RawDataRepository"),
              "root" = root_folder,
              "mother" = mother,
              "Settings" = paste0(root_folder,"Settings"),
-             "RSE_settings" = paste0(root_folder,"Settings/datafeeds_settings/RSE_settings.xlsx"))
+             "RSE_settings" = paste0(root_folder,"Settings/datafeeds_settings/df_RSE_settings.xlsx"))
 
 # Set the year
 year <- 2008
 
 # Read root region, industry and products
-root <- list("region" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/PIOLab_RootClassification.xlsx"),sheet = 1),
-             "process" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/PIOLab_RootClassification.xlsx"),sheet = 2)[,1:2],
-             "flow" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/PIOLab_RootClassification.xlsx"),sheet = 3))
 
-remove(mother,OnServer,root_folder)
+path_rootclass <- paste0(path$Settings,"/Root/Legends/PIOLab_RootClassification.xlsx")
+
+root <- list("region" = read.xlsx(path_rootclass,sheet = 1),
+             "process" = read.xlsx(path_rootclass,sheet = 2)[,1:2],
+             "flow" = read.xlsx(path_rootclass,sheet = 3),
+             "demand" = read.xlsx(path_rootclass,sheet = 4),
+             "input" = read.xlsx(path_rootclass,sheet = 5) )
+
+
+
+remove(mother,OnServer,root_folder, path_rootclass)
diff --git a/Rscripts/Subroutines/Load_Root2Mother_sectors.R b/Rscripts/Subroutines/Load_Root2Mother_sectors.R
index e3fa544..245c1e4 100644
--- a/Rscripts/Subroutines/Load_Root2Mother_sectors.R
+++ b/Rscripts/Subroutines/Load_Root2Mother_sectors.R
@@ -2,35 +2,15 @@
 # Load root to mother sector aggregators
 
 
-
-if(IEdatafeed_name == "Ind20Pro22v1")
-{
-  ProductAggregator <- read.csv(paste0(path$Concordance,"/Sector Aggregators/22ProV1_SectorAggregatorProducts.csv"),
-                                stringsAsFactors=FALSE, sep = ",",header = FALSE)
-  
-  IndustryAggregator <- read.csv(paste0(path$Concordance,"/Sector Aggregators/20IndV1_SectorAggregatorIndustries.csv"),
-                                 stringsAsFactors=FALSE, sep = ",",header = FALSE)
-}
-
-if(IEdatafeed_name == "Ind30Pro40v1")
-{
-  
-  ProductAggregator <- read.csv(paste0(path$Concordance,"/Sector Aggregators/40ProV1_SectorAggregatorProducts.csv"),
-                                stringsAsFactors=FALSE, sep = ",",header = FALSE)
-  
-  IndustryAggregator <- read.csv(paste0(path$Concordance,"/Sector Aggregators/30IndV1_SectorAggregatorIndustries.csv"),
-                                 stringsAsFactors=FALSE, sep = ",",header = FALSE)
-}
-
 if(IEdatafeed_name == "Ind30Pro39v1")
 {
   
-  set <- read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/IE_settings.xlsx"),sheet = 2)
+  set <- read.xlsx(xlsxFile = paste0(path$Settings,"/Base/IE_settings.xlsx"),sheet = 2)
   
   path_sel <- list("flow" = paste0(path$Concordance,"/Sector Aggregators/",
-                                   set$date[set$aggregator == "product"],"_",num$flow,"Products_SectorAggregator.csv"),
+                                   set$date[set$aggregator == "sector"],"_",num$flow,"Products_SectorAggregator.csv"),
                    "process" = paste0(path$Concordance,"/Sector Aggregators/",
-                                      set$date[set$aggregator == "industry"],"_",num$process,"Industries_SectorAggregator.csv")
+                                      set$date[set$aggregator == "sector"],"_",num$process,"Industries_SectorAggregator.csv")
                    )
   
   
diff --git a/Rscripts/Subroutines/Read_BaseClassification.R b/Rscripts/Subroutines/Read_BaseClassification.R
index ef65ffd..0c7e893 100644
--- a/Rscripts/Subroutines/Read_BaseClassification.R
+++ b/Rscripts/Subroutines/Read_BaseClassification.R
@@ -16,11 +16,14 @@ if(file.exists(reg_path))
   # Read the number of regions from the name of the aggregator 
   RegionAgg <- substr(reg_map,nchar(reg_map)-23,nchar(reg_map)-21)
   
-  base <- list("region" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",RegionAgg,"_BaseRegionClassification.xlsx"),sheet = 1),
-               "process" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx"),sheet = 1),
-               "flow" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx"),sheet = 2),
-               "demand" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx"),sheet = 3),
-               "input" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx"),sheet = 4))
+  path_base_reg <- paste0(path$Settings,"/Base/",RegionAgg,"_BaseRegionClassification.xlsx")
+  path_base_sec <- paste0(path$Settings,"/Base/",IEdatafeed_name,"_BaseSectorClassification.xlsx")
+  
+  base <<- list("region" = read.xlsx(path_base_reg,sheet = 1),
+                "process" = read.xlsx(path_base_sec,sheet = 1),
+                "flow" = read.xlsx(path_base_sec,sheet = 2),
+                "demand" = read.xlsx(path_base_sec,sheet = 3),
+                "input" = read.xlsx(path_base_sec,sheet = 4))
   
   remove(reg_map,RegionAgg)
   
@@ -37,11 +40,14 @@ if(file.exists(reg_path))
                                     )
               )
   
-  base <<- list("region" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",test_regagg,"_BaseRegionClassification.xlsx"),sheet = 1),
-                "process" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx"),sheet = 1),
-                "flow" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx"),sheet = 2),
-                "demand" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx"),sheet = 3),
-                "input" = read.xlsx(paste0(path$Concordance,"/LabelsAndCodes/",IEdatafeed_name,"_BaseSectorClassification.xlsx"),sheet = 4))
+  path_base_reg <- paste0(path$Settings,"/Base/",test_regagg,"_BaseRegionClassification.xlsx")
+  path_base_sec <- paste0(path$Settings,"/Base/",IEdatafeed_name,"_BaseSectorClassification.xlsx")
+  
+  base <<- list("region" = read.xlsx(path_base_reg,sheet = 1),
+                "process" = read.xlsx(path_base_sec,sheet = 1),
+                "flow" = read.xlsx(path_base_sec,sheet = 2),
+                "demand" = read.xlsx(path_base_sec,sheet = 3),
+                "input" = read.xlsx(path_base_sec,sheet = 4))
   
   remove(test_regagg)
 }
diff --git a/Rscripts/concfeeds_code/concfeed_Root2Mother_sectors.R b/Rscripts/concfeeds_code/concfeed_Root2Mother_sectors.R
index a698719..3282fa7 100644
--- a/Rscripts/concfeeds_code/concfeed_Root2Mother_sectors.R
+++ b/Rscripts/concfeeds_code/concfeed_Root2Mother_sectors.R
@@ -14,7 +14,7 @@ if(Sys.info()[1] == "Linux"){
   .libPaths("/suphys/hwie3321/R/x86_64-redhat-linux-gnu-library/3.5")
   # Define location for root directory
   root_folder <- "/import/emily1/isa/IELab/Roots/PIOLab/"}else{
-    root_folder <- "C:/Users/hwieland/Github workspace/PIOLab/"}
+  root_folder <- "C:/Users/hwieland/Github workspace/PIOLab/"}
 
 # Initializing R script (load R packages and set paths to folders etc.)
 source(paste0(root_folder,"Rscripts/Subroutines/InitializationR.R"))
@@ -25,18 +25,24 @@ source(paste0(path$root,"Rscripts/Subroutines/Read_BaseClassification.R"))
 # Load function to write tables to file
 source(paste0(path$root,"Rscripts/Subroutines/Numbers2File.R"))
 
+set <- read.xlsx(paste0(path$Settings,"/Base/IE_settings.xlsx"),sheet = 2)
+
 # Load Source to root sector aggregator of (i) WSA and (ii) IRP accounts 
 # and (iii) additionally pseudo S2R where no raw data is available 
 # Note that all tables are transposed (i.e. in root to source format):
 
-S2R <- list("WSA" = list("process" =  read.xlsx(paste0(path$Concordance,"/WSA/WSA_Source2Root_WithLabelsV3.xlsx"), sheet = 1),
-                         "flow" = read.xlsx(paste0(path$Concordance,"/WSA/WSA_Source2Root_WithLabelsV3.xlsx"), sheet = 2)
+path_wsa <- paste0(path$Concordance,"/WSA/WSA_Source2Root_WithLabels",set$date[set$aggregator == "WSA"],".xlsx")
+path_IRP <- paste0(path$Concordance,"/IRP/",set$date[set$aggregator == "sector"],"_IRP_Extraction_SecConc.xlsx")
+path_add <- paste0(path$Concordance,"/Additional/",set$date[set$aggregator == "Additional"],"_Additional_Root2Mother_Sec_Ind30Pro39v1.xlsx")
+  
+S2R <- list("WSA" = list("process" =  read.xlsx(path_wsa, sheet = 1),
+                         "flow" = read.xlsx(path_wsa, sheet = 2)
                          ),
-            "IRP" = list("process" =  read.xlsx(paste0(path$Concordance,"/IRP/20200420_IRP_Extraction_SecConc.xlsx"), sheet = 1),
-                         "flow" = read.xlsx(paste0(path$Concordance,"/IRP/20200420_IRP_Extraction_SecConc.xlsx"), sheet = 2)
+            "IRP" = list("process" =  read.xlsx(path_IRP, sheet = 1),
+                         "flow" = read.xlsx(path_IRP, sheet = 2)
                         ),
-            "Add" = list("process" =  read.xlsx(paste0(path$Concordance,"/Additional/20200420_Additional_Root2Mother_Sec_Ind30Pro39v1.xlsx"), sheet = 1),
-                         "flow" = read.xlsx(paste0(path$Concordance,"/Additional/20200420_Additional_Root2Mother_Sec_Ind30Pro39v1.xlsx"), sheet = 2)
+            "Add" = list("process" =  read.xlsx(path_add, sheet = 1),
+                         "flow" = read.xlsx(path_add, sheet = 2)
                         )
             )
 
@@ -56,7 +62,7 @@ Code <- list("WSA" = base$flow[base$flow$feed %in% colnames(S2R$WSA$flow), ],
             
 # Extract Source to root concordances (just to make sure the right subset is selected)
 Conco <- list("WSA" = as.matrix( S2R$WSA$flow[1:nrow(root$flow), Code$WSA$feed] ),
-              "IRP" = S2R$IRP$flow$IRPextraction,
+              "IRP" = S2R$IRP$flow$binary,
               "Add" = as.matrix( S2R$Add$flow[1:nrow(root$flow), as.character( Code$Add$Code )] )
               )
  
@@ -90,7 +96,7 @@ Code <- list("WSA" = base_sel[base_sel$feed_1 %in% colnames(S2R$WSA$process), ],
 
 # Extract Source to root concordances (just to make sure the right subset is selected)
 Conco <- list("WSA" = as.matrix( S2R$WSA$process[1:nrow(root$process), Code$WSA$feed_1] ),
-              "IRP" = S2R$IRP$process$IRPextraction,
+              "IRP" = S2R$IRP$process$binary,
               "Add" = as.matrix( S2R$Add$process[1:nrow(root$process), as.character( Code$Add$Code )] )
               )
 
diff --git a/Rscripts/concfeeds_code/concfeed_WSA_Source2Root_sectors.R b/Rscripts/concfeeds_code/concfeed_WSA_Source2Root_sectors.R
index 1dd98ef..66900b8 100644
--- a/Rscripts/concfeeds_code/concfeed_WSA_Source2Root_sectors.R
+++ b/Rscripts/concfeeds_code/concfeed_WSA_Source2Root_sectors.R
@@ -19,9 +19,8 @@ source(paste0(root_folder,"Rscripts/Subroutines/InitializationR.R"))
 # Load function to write tables to file
 source(paste0(path$root,"Rscripts/Subroutines/Numbers2File.R"))
 
-S2R <- list("process" =  read.xlsx(paste0(path$Concordance,"/WSA/WSA_Source2Root_WithLabelsV3.xlsx"), sheet = 1),
-            "flow" = read.xlsx(paste0(path$Concordance,"/WSA/WSA_Source2Root_WithLabelsV3.xlsx"), sheet = 2)
-            )
+S2R <- list("process" =  read.xlsx(paste0(path$Concordance,"/WSA/WSA_Source2Root_WithLabelsV4.xlsx"), sheet = 1),
+            "flow" = read.xlsx(paste0(path$Concordance,"/WSA/WSA_Source2Root_WithLabelsV4.xlsx"), sheet = 2) )
 
 # Read WSA settings to get exact number of feeds
 Settings <- read.xlsx(paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"))
diff --git a/Rscripts/datafeeds_code/Archive/datafeed_PIOLab_IRPexports.R b/Rscripts/datafeeds_code/Archive/datafeed_PIOLab_IRPexports.R
new file mode 100644
index 0000000..e932152
--- /dev/null
+++ b/Rscripts/datafeeds_code/Archive/datafeed_PIOLab_IRPexports.R
@@ -0,0 +1,137 @@
+################################################################################
+
+datafeed_name <- "IRPexports"
+print(paste0("datafeed_PIOLab_",datafeed_name," initiated."))
+
+################################################################################
+# Set library path when running on suphys server
+if(Sys.info()[1] == "Linux"){
+  .libPaths("/suphys/hwie3321/R/x86_64-redhat-linux-gnu-library/3.5")
+  # Define location for root directory
+  root_folder <- "/import/emily1/isa/IELab/Roots/PIOLab/"}else{
+    root_folder <- "C:/Users/hwieland/Github workspace/PIOLab/"}
+################################################################################
+
+# Initializing R script (load R packages and set paths to folders etc.)
+source(paste0(root_folder,"Rscripts/Subroutines/InitializationR.R"))
+
+path["df_Processed"] <- paste0(path$Processed,"/",datafeed_name)
+
+# Load function to write arrays to files
+source(paste0(path$Subroutines,"/Numbers2File.R"))
+
+# Check if folder with processed data exists, in case delete and create empty one
+
+if(dir.exists(path$df_Processed)) unlink(path$df_Processed,recursive = TRUE) 
+
+dir.create(path$df_Processed)
+
+# Loading raw data
+data <- read.csv(paste0(path$Raw,"/IRP/all_CCC_Exp_ResearchDB.csv"),stringsAsFactors=FALSE)
+colnames(data)[6:49] <- 1970:2013
+data <- data %>% filter(CCC_IEATrade_Name == "Iron ores") %>% select(Country,AlphaNumISO,as.character(year)) 
+# Remove NA
+data <- data[!is.na(data$`2008`),]
+
+# transform 3-digit ISO codes and translate into root classification, first load the root
+data <- data %>% separate(AlphaNumISO, into = c('ISOCode', 'num'), sep = 3) %>% select(-num)  
+colnames(data)[ncol(data)] <- "Quantity"
+data <- filter(data,Quantity > 0)
+# Look up root codes
+data <- left_join(data,root$region[,c("Code","RootCountryAbbreviation")],by = c("ISOCode" = "RootCountryAbbreviation"),copy = FALSE) 
+
+# No root codes for Faroe Islands and Mayotte (remove for now and talk Manfred/Arne)
+data <- data[!is.na(data$Code),]
+
+# Remove Yugoslavia and USSR from data
+data <- data %>% filter(!Code %in% c(233,242)) 
+
+data <- data[!is.na(data$Code),] # Mayotte and Farour Isl. are not in root (tbc)
+
+n_reg <- nrow(root$region)
+
+# Read standard errors: 
+
+RSE <- filter(read.xlsx(path$RSE_settings),Item == datafeed_name)
+
+data_new <- data.frame("code" = 1:n_reg,"RHS" = 0)
+
+data_new$RHS[data$Code] <- data$Quantity
+
+data <- data_new
+
+remove(data_new)
+
+
+ConcoReg <- diag(nrow(root$region)) # Create pseudo aggregator (for columns)
+
+# Set names and paths to data and concordances:
+
+filename <- list("RHS" = paste0("/",datafeed_name,"/",datafeed_name,"_RHS_",year,".csv"),
+                 "ConcoReg" = "/Root2Root_Reg_Concordance.csv")
+
+Numbers2File( t(data$RHS), paste0(path$Processed,filename$RHS)) # Save S2R reg aggregator 
+
+Numbers2File( ConcoReg, paste0(path$Concordance,filename$ConcoReg)) # Save S2R reg aggregator 
+
+
+# Create empty ALANG table with header:
+
+source(paste0(path$Subroutines,"/makeALANGheadline.R")) # Create ALANG header
+
+ALANG <- ALANG[,c(1:19,11:19)]  # Extend table with additional columns
+
+ALANG <- add_row(ALANG,'1' = "DataFeed IRP export")  # Create command
+
+ALANG$Value <- paste0("DATAPATH",filename$RHS)
+
+ALANG$S.E. <- paste0("E MX",RSE$Maximum,"; MN",RSE$Minimum,";")
+
+
+# Write first 8-tuple (sum of domestic use and imports for each region)
+
+ALANG$Coef1 <- "-1"
+
+ALANG$Years <- "1"
+ALANG$Margin <- "1"
+
+ALANG$`Row parent` <- paste0("1:e a CONCPATH",filename$ConcoReg)
+ALANG$`Row child` <- "2"
+ALANG$`Row grandchild` <- "1-e"
+
+ALANG$`Column parent` <- "1:e~3"
+ALANG$`Column child` <- "1"
+ALANG$`Column grandchild` <- "1-e"
+
+# Write second 8-tuple (subtract sum of domestic use table)
+
+ALANG$Coef1.1 <- "1"
+
+ALANG$Years.1 <- "1"
+ALANG$Margin.1 <- "1"
+
+ALANG$`Row parent.1` <- "1:e"
+ALANG$`Row child.1` <- "2"
+ALANG$`Row grandchild.1` <- "1-e"
+
+ALANG$`Column parent.1` <- "1-e"
+ALANG$`Column child.1` <- "1-e"
+ALANG$`Column grandchild.1` <- "1-e"
+
+ALANG$`#` <- as.character(1:nrow(ALANG))
+ALANG$Incl <- "Y"
+ALANG$Parts <- "2"
+
+ALANG$`Pre-map` <- ""
+ALANG$`Post-map` <- ""
+ALANG$`Pre-Map` <- ""
+ALANG$`Post-Map` <- ""
+
+
+# Call script that writes the ALANG file to the repsective folder in the root
+source(paste0(path$root,"Rscripts/datafeeds_code/datafeed_subroutines/WriteALANG2Folder.R"))
+
+print(paste0("datafeed_PIOLab_",datafeed_name," finished."))
+
+rm(list = ls()) # clear workspace
+
diff --git a/Rscripts/datafeeds_code/Archive/datafeed_PIOLab_IRPimports.R b/Rscripts/datafeeds_code/Archive/datafeed_PIOLab_IRPimports.R
new file mode 100644
index 0000000..1fb93bc
--- /dev/null
+++ b/Rscripts/datafeeds_code/Archive/datafeed_PIOLab_IRPimports.R
@@ -0,0 +1,139 @@
+################################################################################
+
+datafeed_name <- "IRPimports"
+print(paste0("datafeed_PIOLab_",datafeed_name," initiated."))
+
+################################################################################
+# Set library path when running on suphys server
+if(Sys.info()[1] == "Linux"){
+  .libPaths("/suphys/hwie3321/R/x86_64-redhat-linux-gnu-library/3.5")
+  # Define location for root directory
+  root_folder <- "/import/emily1/isa/IELab/Roots/PIOLab/"}else{
+  root_folder <- "C:/Users/hwieland/Github workspace/PIOLab/"}
+################################################################################
+
+# Initializing R script (load R packages and set paths to folders etc.)
+
+source(paste0(root_folder,"Rscripts/Subroutines/InitializationR.R"))
+
+path["df_Processed"] <- paste0(path$Processed,"/",datafeed_name)
+
+source(paste0(path$Subroutines,"/Numbers2File.R")) # Load fun. to write arrays to files
+
+
+# Check if folder with processed data exists, in case delete and create empty one
+
+if(dir.exists(path$df_Processed)) unlink(path$df_Processed,recursive = TRUE) 
+
+dir.create(path$df_Processed)
+
+
+# Loading raw data
+data <- read.csv(paste0(path$Raw,"/IRP/all_CCC_Imp_ResearchDB.csv"),stringsAsFactors=FALSE)
+colnames(data)[6:49] <- 1970:2013
+data <- data %>% filter(CCC_IEATrade_Name == "Iron ores") %>% select(Country,AlphaNumISO,as.character(year)) 
+# Remove NA
+data <- data[!is.na(data$`2008`),]
+
+data <- data %>% separate(AlphaNumISO, into = c('ISOCode', 'num'), sep = 3) %>% 
+  select(-num)  
+
+colnames(data)[ncol(data)] <- "Quantity"
+data <- filter(data,Quantity > 0)
+
+# Remove Yugoslavia and USSR from data
+data <- data %>% filter(!ISOCode %in% c("YUG","SUN")) 
+
+# Look up root codes
+data <- left_join(data,root$region[,c("Code","RootCountryAbbreviation")],
+                  by = c("ISOCode" = "RootCountryAbbreviation"),copy = FALSE) %>% 
+  select(Code,Quantity)
+
+data <- data[!is.na(data$Code),] # Mayotte and Farour Isl. are not in root (tbc)
+
+RSE <- filter(read.xlsx(path$RSE_settings),Item == datafeed_name) # Import RSE
+
+# # Add standard errors 
+# source(paste0(path$Subroutines,"/SE_LogRegression.R"))
+# data <- SE_LogRegression(data,RSE$Minimum,RSE$Maximum)
+
+n_reg <- nrow(root$region) # Read number of root regions
+
+data_new <- data.frame("code" = 1:n_reg,"RHS" = 0)
+
+data_new$RHS[data$Code] <- data$Quantity
+
+data <- data_new
+
+remove(data_new)
+
+
+ConcoReg <- diag(nrow(root$region)) # Create pseudo aggregator (for columns)
+
+# Set names and paths to data and concordances:
+
+filename <- list("RHS" = paste0("/",datafeed_name,"/",datafeed_name,"_RHS_",year,".csv"),
+                 "ConcoReg" = "/Root2Root_Reg_Concordance.csv")
+
+Numbers2File( t(data$RHS), paste0(path$Processed,filename$RHS)) # Save S2R reg aggregator 
+
+Numbers2File( ConcoReg, paste0(path$Concordance,filename$ConcoReg)) # Save S2R reg aggregator 
+
+
+source(paste0(path$Subroutines,"/makeALANGheadline.R")) # Create ALANG header
+
+ALANG <- ALANG[,c(1:19,11:19)]  # Extend table with additional columns
+
+ALANG <- add_row(ALANG,'1' = "DataFeed IRP imports")  # Create command
+
+ALANG$Value <- paste0("DATAPATH",filename$RHS)
+
+ALANG$S.E. <- paste0("E MX",RSE$Maximum,"; MN",RSE$Minimum,";")
+
+# Write first 8-tuple (sum of domestic use and imports for each region)
+
+ALANG$Coef1 <- "-1"
+
+ALANG$Years <- "1"
+ALANG$Margin <- "1"
+
+ALANG$`Row parent` <- paste0("1:e a CONCPATH",filename$ConcoReg)
+ALANG$`Row child` <- "2"
+ALANG$`Row grandchild` <- "1-e"
+ 
+ALANG$`Column parent` <- "1:e~3"
+ALANG$`Column child` <- "1"
+ALANG$`Column grandchild` <- "1-e"
+
+# Write second 8-tuple (subtract sum of domestic use table)
+
+ALANG$Coef1.1 <- "1"
+
+ALANG$Years.1 <- "1"
+ALANG$Margin.1 <- "1"
+
+ALANG$`Row parent.1` <- "1-e"
+ALANG$`Row child.1` <- "2"
+ALANG$`Row grandchild.1` <- "1-e"
+
+ALANG$`Column parent.1` <- "1:e"
+ALANG$`Column child.1` <- "1"
+ALANG$`Column grandchild.1` <- "1-e"
+
+ALANG$`#` <- as.character(1:nrow(ALANG))
+ALANG$Incl <- "Y"
+ALANG$Parts <- "2"
+
+ALANG$`Pre-map` <- ""
+ALANG$`Post-map` <- ""
+ALANG$`Pre-Map` <- ""
+ALANG$`Post-Map` <- ""
+
+# Call script that writes the ALANG file to the repsective folder in the root
+
+source(paste0(path$root,"Rscripts/datafeeds_code/datafeed_subroutines/WriteALANG2Folder.R"))
+
+print(paste0("datafeed_PIOLab_",datafeed_name," finished."))
+
+rm(list = ls()) # clear workspace
+
diff --git a/Rscripts/datafeeds_code/datafeed_PIOLab_BACI.R b/Rscripts/datafeeds_code/datafeed_PIOLab_BACI.R
index e3b0879..5a23e2d 100644
--- a/Rscripts/datafeeds_code/datafeed_PIOLab_BACI.R
+++ b/Rscripts/datafeeds_code/datafeed_PIOLab_BACI.R
@@ -88,8 +88,10 @@ ALANG$RHS$Value <- paste0("DATAPATH/",datafeed_name,"/",ALANG$RHS$`1`,".csv")
 ALANG$SE$S.E. <- paste0("DATAPATH/",datafeed_name,"/",ALANG$SE$`1`,".csv")
 
 vec <- data.frame("pro" = 1:pro_max,
-                  "RHS" = NA,
-                  "SE" = NA)
+                  "RHS" = 0,
+                  "SE" = 0)
+
+vec[256:266,2:3] <- NA
 
 ### 2. Write commands by looping over export regions:
 
@@ -113,11 +115,10 @@ for( i in 1:nrow(index) )
 }
   
 
-
 ### 3. Merge ALANG sheets and complete missing entries 
 
-ALANG <- rbind(ALANG$RHS,ALANG$SE)  # Combine SE and RHS commands
-ALANG$`#` <- 1:nrow(ALANG) # Add index
+ALANG <- rbind(ALANG$RHS,ALANG$SE)     # Combine SE and RHS commands
+ALANG$`#` <- 1:nrow(ALANG)             # Add index
 ALANG[] <- lapply(ALANG,as.character)  # Convert all entries to character
 
 ### 4. Call script that writes the ALANG file to the repsective folder in the root
diff --git a/Rscripts/datafeeds_code/datafeed_PIOLab_IRPextraction.R b/Rscripts/datafeeds_code/datafeed_PIOLab_IRPextraction.R
index d3964d5..898a62d 100644
--- a/Rscripts/datafeeds_code/datafeed_PIOLab_IRPextraction.R
+++ b/Rscripts/datafeeds_code/datafeed_PIOLab_IRPextraction.R
@@ -13,11 +13,17 @@ if(Sys.info()[1] == "Linux"){
 ################################################################################
 # Initializing R script (load R packages and set paths to folders etc.)
 source(paste0(root_folder,"Rscripts/Subroutines/InitializationR.R"))
-
-path["df_Processed"] <- paste0(path$Processed,"/",datafeed_name)
+path["df_Processed"] <- paste0(path$Processed,"/",datafeed_name)  # Add datafeed specific path for output data
+path["df_conco"] <- paste0(path$Concordance,"/IRP/")  # Add datafeed specific path for S2R concordance
 
 # Load function to write arrays to files
-source(paste0(path$Subroutines,"/Numbers2File.R"))
+source( paste0(path$Subroutines,"/Numbers2File.R") )
+
+# Read date of concordance for sourc2root industries and products
+set <- read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/IRP_settings.xlsx"),sheet = 1)
+          
+Conco <- list("process" = read.xlsx(xlsxFile = paste0(path$df_conco,set$date,"_IRP_Extraction_SecConc.xlsx"),sheet = 1),
+              "flow" =  read.xlsx(xlsxFile = paste0(path$df_conco,set$date,"_IRP_Extraction_SecConc.xlsx"),sheet = 2) )
 
 # Loading raw data
 source(paste0(path$Subroutines,"/Read_ExtractionIRP.R"))
@@ -57,17 +63,17 @@ ALANG <- add_row(ALANG,
                  S.E. = paste0("E MX",RSE$Maximum,"; MN",RSE$Minimum,";") )
  
 ALANG$`#` <- 1:nrow(ALANG)
-ALANG$Coef1 <- "1"
-ALANG$Years <- "1"
-ALANG$Margin <- "1"
+ALANG$Coef1 <- 1
+ALANG$Years <- 1
+ALANG$Margin <- 1
 
 ALANG$`Row parent` <- "1-e"
-ALANG$`Row child` <- "3"
-ALANG$`Row grandchild` <- "4"
+ALANG$`Row child` <- 3
+ALANG$`Row grandchild` <- root$input$Code[ root$input$Name == "Hematite & Magnetite" ]
 
 ALANG$`Column parent` <- "1:e t2 CONCPATH/Root2Root_Reg_Concordance.csv"
 ALANG$`Column child` <- "1"
-ALANG$`Column grandchild` <- "1-4"
+ALANG$`Column grandchild` <- paste( Conco$process$Code[Conco$process$binary == 1] , collapse = "," )
 
 ALANG$Incl <- "Y"
 ALANG$Parts <- "1"
@@ -80,3 +86,4 @@ ALANG$`Post-Map` <- ""
 source(paste0(path$root,"Rscripts/datafeeds_code/datafeed_subroutines/WriteALANG2Folder.R"))
   
 print(paste0("datafeed_PIOLab_",datafeed_name," finished."))
+
diff --git a/Rscripts/datafeeds_code/datafeed_subroutines/CreateALANGforWSAdata.R b/Rscripts/datafeeds_code/datafeed_subroutines/CreateALANGforWSAdata.R
index 91029ec..d8814a2 100644
--- a/Rscripts/datafeeds_code/datafeed_subroutines/CreateALANGforWSAdata.R
+++ b/Rscripts/datafeeds_code/datafeed_subroutines/CreateALANGforWSAdata.R
@@ -6,36 +6,27 @@
 
 
 # Initializing R script (R packages, folders etc.):
-
 source(paste0(root_folder,"Rscripts/Subroutines/InitializationR.R"))
-
 path["df_Processed"] <- paste0(path$Processed,"/",datafeed_name) # Add path for processed data
 
 
 print(paste0("datafeed_PIOLab_",datafeed_name," initiated."))
 
 source(paste0(path$Subroutines,"/Numbers2File.R"))  # Load fun. to write arrays to files
-
 source(paste0(path$Subroutines,"/Read_ProductionWSA.R")) # Fun. to align data with root classification
 
 
-
 # Check if folder with processed data exists and in case delete:
-
 if(dir.exists(path$df_Processed)) unlink(path$df_Processed,recursive = TRUE) 
-
 dir.create(path$df_Processed) # Create new (empty) folder
   
 
 # Get relative standard error for smallest and largest values in the data set:
-
 RSE <- filter(read.xlsx(path$RSE_settings),Item == datafeed_name)
 
   
 # Read settings (e.g. page in the specific yearbook) for WSA data feeds:
-
 Settings <- read.xlsx(paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"))
-  
 Settings <- Settings[Settings$FeedName == datafeed_name,] # Select which feed
   
 
@@ -74,12 +65,12 @@ concord <- read.xlsx(xlsxFile = paste0(path$Concordance,"/WSA/WSA_RegionConcorda
 
 # Load concordance for sourc2root industries and products
 
-set <- read.xlsx(xlsxFile = paste0(path$Settings,"/datafeeds_settings/WSA_settings.xlsx"),sheet = 2)
+set <- read.xlsx(xlsxFile = paste0(path$Settings,"/Base/IE_settings.xlsx"),sheet = 2)
 
 path_sel <- list("flow" = paste0(path$Concordance,"/WSA/",
-                                 set$date[set$aggregator == "product"],"_WSA_Source2Root_Product.csv"),
+                                 set$date[set$aggregator == "sector"],"_WSA_Source2Root_Product.csv"),
                  "process" = paste0(path$Concordance,"/WSA/",
-                                    set$date[set$aggregator == "industry"],"_WSA_Source2Root_Industry.csv")
+                                    set$date[set$aggregator == "sector"],"_WSA_Source2Root_Industry.csv")
                  )
 
 ConcoInd <- read.table(path_sel$process,sep = ",")
@@ -104,7 +95,7 @@ RHS <- matrix(0,nrow = 1,ncol = n_reg) # vector for RHS values all root regions
 
 RHS[1,data$Code] <- data$Quantity  # Write right-hand-side in vector
 
-ConcoReg <- diag(nrow(root$region)) # Create pseudo aggregator
+# ConcoReg <- diag(nrow(root$region)) # Create pseudo aggregator
 
 
 # Set names and paths to data and concordances:
@@ -114,7 +105,7 @@ filename <- list("RHS" = paste0("/",datafeed_name,"/",datafeed_name,"_RHS_",year
                  "ConcoInd" = paste0("/WSA_Source2Root_Industry_",datafeed_name,".csv"),
                  "ConcoPro" = paste0("/WSA_Source2Root_Product_",datafeed_name,".csv"))
 
-Numbers2File( ConcoReg, paste0(path$Concordance,filename$ConcoReg)) # Save region aggregator 
+# Numbers2File( ConcoReg, paste0(path$Concordance,filename$ConcoReg)) # Save region aggregator 
 
 Numbers2File( RHS, paste0(path$Processed, filename$RHS)) # Write data to folder 
 
@@ -134,13 +125,14 @@ ALANG$`Column parent` <- paste0("1:e t2 CONCPATH",filename$ConcoReg)
 
 # Write command for elements that are zero:
 
-ALANG <- add_row(ALANG,'1' = paste(datafeed_name,year, "Zero elements")) # Create entry
-
-ALANG$Value[2] <- paste0("0")
-ALANG$S.E.[2] <- paste0("0")
-ALANG$`Row grandchild`[2] <- ConcoInd$Zero
-ALANG$`Column parent`[2] <- paste0("1-e")
+# ALANG <- add_row(ALANG,'1' = paste(datafeed_name,year, "Zero elements")) # Create entry
+# 
+# ALANG$Value[2] <- paste0("0")
+# ALANG$S.E.[2] <- paste0("0")
+# ALANG$`Row grandchild`[2] <- ConcoInd$Zero
+# ALANG$`Column parent`[2] <- paste0("1-e")
 
+# Write other variables
 
 ALANG$`Row parent` <- "1-e"
 ALANG$`Row child` <- "1"
diff --git a/Settings/Base/002_BaseRegionClassification.xlsx b/Settings/Base/002_BaseRegionClassification.xlsx
new file mode 100644
index 0000000..884a165
Binary files /dev/null and b/Settings/Base/002_BaseRegionClassification.xlsx differ
diff --git a/Settings/Base/005_BaseRegionClassification.xlsx b/Settings/Base/005_BaseRegionClassification.xlsx
new file mode 100644
index 0000000..6c84340
Binary files /dev/null and b/Settings/Base/005_BaseRegionClassification.xlsx differ
diff --git a/Settings/Base/049_BaseRegionClassification.xlsx b/Settings/Base/049_BaseRegionClassification.xlsx
new file mode 100644
index 0000000..4051d0f
Binary files /dev/null and b/Settings/Base/049_BaseRegionClassification.xlsx differ
diff --git a/Settings/Base/Archive/Ind20Pro22v1_BaseSectorClassification.xlsx b/Settings/Base/Archive/Ind20Pro22v1_BaseSectorClassification.xlsx
new file mode 100644
index 0000000..2854938
Binary files /dev/null and b/Settings/Base/Archive/Ind20Pro22v1_BaseSectorClassification.xlsx differ
diff --git a/Settings/Base/Archive/Ind30Pro40v1_BaseSectorClassification.xlsx b/Settings/Base/Archive/Ind30Pro40v1_BaseSectorClassification.xlsx
new file mode 100644
index 0000000..a04fac4
Binary files /dev/null and b/Settings/Base/Archive/Ind30Pro40v1_BaseSectorClassification.xlsx differ
diff --git a/Settings/Base/Archive/PIOLab_RootClassification_OLD.xlsx b/Settings/Base/Archive/PIOLab_RootClassification_OLD.xlsx
new file mode 100644
index 0000000..390d013
Binary files /dev/null and b/Settings/Base/Archive/PIOLab_RootClassification_OLD.xlsx differ
diff --git a/Settings/Base/IE_settings.xlsx b/Settings/Base/IE_settings.xlsx
new file mode 100644
index 0000000..ad85d11
Binary files /dev/null and b/Settings/Base/IE_settings.xlsx differ
diff --git a/Settings/Base/Ind30Pro39v1_BaseSectorClassification.xlsx b/Settings/Base/Ind30Pro39v1_BaseSectorClassification.xlsx
new file mode 100644
index 0000000..c0bed3a
Binary files /dev/null and b/Settings/Base/Ind30Pro39v1_BaseSectorClassification.xlsx differ
diff --git a/Settings/Root/Legends/Archive/._RootRegionLegend.xlsx b/Settings/Root/Legends/Archive/._RootRegionLegend.xlsx
new file mode 100644
index 0000000..c51f873
Binary files /dev/null and b/Settings/Root/Legends/Archive/._RootRegionLegend.xlsx differ
diff --git a/Settings/Root/Legends/Archive/._RootSectorLegend.xlsx b/Settings/Root/Legends/Archive/._RootSectorLegend.xlsx
new file mode 100644
index 0000000..4478dc5
Binary files /dev/null and b/Settings/Root/Legends/Archive/._RootSectorLegend.xlsx differ
diff --git a/Settings/Root/Legends/Archive/.~lock.RootRegionLegend.xlsx# b/Settings/Root/Legends/Archive/.~lock.RootRegionLegend.xlsx#
new file mode 100644
index 0000000..80fff98
--- /dev/null
+++ b/Settings/Root/Legends/Archive/.~lock.RootRegionLegend.xlsx#
@@ -0,0 +1 @@
+,hwie3321,gateway.physics.usyd.edu.au,26.02.2020 13:19,file:///suphys/hwie3321/.config/libreoffice/4;
\ No newline at end of file
diff --git a/Settings/Root/Legends/Archive/RootRegionLegend.xlsx b/Settings/Root/Legends/Archive/RootRegionLegend.xlsx
new file mode 100644
index 0000000..40e765e
Binary files /dev/null and b/Settings/Root/Legends/Archive/RootRegionLegend.xlsx differ
diff --git a/Settings/Root/Legends/Archive/RootSectorLegend.xlsx b/Settings/Root/Legends/Archive/RootSectorLegend.xlsx
new file mode 100644
index 0000000..6ad69b8
Binary files /dev/null and b/Settings/Root/Legends/Archive/RootSectorLegend.xlsx differ
diff --git a/Settings/Root/Legends/PIOLab_RootClassification.xlsx b/Settings/Root/Legends/PIOLab_RootClassification.xlsx
new file mode 100644
index 0000000..2c298f1
Binary files /dev/null and b/Settings/Root/Legends/PIOLab_RootClassification.xlsx differ
diff --git a/Settings/datafeeds_settings/IRP_settings.xlsx b/Settings/datafeeds_settings/IRP_settings.xlsx
new file mode 100644
index 0000000..d3b6c09
Binary files /dev/null and b/Settings/datafeeds_settings/IRP_settings.xlsx differ
diff --git a/Settings/datafeeds_settings/WSA_settings.xlsx b/Settings/datafeeds_settings/WSA_settings.xlsx
index 1448156..ca3c754 100644
Binary files a/Settings/datafeeds_settings/WSA_settings.xlsx and b/Settings/datafeeds_settings/WSA_settings.xlsx differ
diff --git a/Settings/datafeeds_settings/df_RSE_settings.xlsx b/Settings/datafeeds_settings/df_RSE_settings.xlsx
new file mode 100644
index 0000000..a5290f6
Binary files /dev/null and b/Settings/datafeeds_settings/df_RSE_settings.xlsx differ
diff --git a/datafeeds_code/Archive/datafeed_PIOLab_IRPexports.m b/datafeeds_code/Archive/datafeed_PIOLab_IRPexports.m
new file mode 100644
index 0000000..468137a
--- /dev/null
+++ b/datafeeds_code/Archive/datafeed_PIOLab_IRPexports.m
@@ -0,0 +1,5 @@
+function datafeed_PIOLab_IRPexports(handles)
+
+system('Rscript /import/emily1/isa/IELab/Roots/PIOLab/Rscripts/datafeeds_code/datafeed_PIOLab_IRPexports.R','-echo');
+
+end
\ No newline at end of file
diff --git a/datafeeds_code/Archive/datafeed_PIOLab_IRPimports.m b/datafeeds_code/Archive/datafeed_PIOLab_IRPimports.m
new file mode 100644
index 0000000..7ec22a9
--- /dev/null
+++ b/datafeeds_code/Archive/datafeed_PIOLab_IRPimports.m
@@ -0,0 +1,5 @@
+function datafeed_PIOLab_IRPimports(handles)
+
+system('Rscript /import/emily1/isa/IELab/Roots/PIOLab/Rscripts/datafeeds_code/datafeed_PIOLab_IRPimports.R','-echo');
+
+end
\ No newline at end of file