Remove old files not used

iiasa · Dec 2, 2024 · ce5ec04 · ce5ec04
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diff --git a/message_ix_models/data/water/ppl_cooling_tech/POWER_PLANTS_2016_Raptis.csv b/message_ix_models/data/water/ppl_cooling_tech/POWER_PLANTS_2016_Raptis.csv
diff --git a/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_cooling_type.csv b/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_cooling_type.csv
diff --git a/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_unit_type.csv b/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_unit_type.csv
diff --git a/message_ix_models/model/water/data/pre_processing/calculate_ppl_cooling_technology_shares.R b/message_ix_models/model/water/data/pre_processing/calculate_ppl_cooling_technology_shares.R
@@ -200,101 +200,3 @@ cooltech_cost_shares_c <- cooltech_cost_shares %>%
 cooltech_cost_shares_c[is.na(cooltech_cost_shares_c)] <- 0
 # write new file
 write.csv(cooltech_cost_shares_c, paste0(data_subf, "/cooltech_cost_and_shares_country.csv"), row.names = FALSE)
-
-
-# OLD DATABASE, with the new one, most of this processing is done already by the script
-# https://github.com/OFR-IIASA/message_data/blob/RES_add_5_year_timesteps2/data/model/platts_historical_capacity/merge_WEPP_Raptis_CARMA.py
-
-# # loading
-# temp = readOGR(path.expand(paste0(data_subf,'\\delineation')),'MSG11_reg_simpl',verbose=FALSE)
-#
-# # the input shape file should come externally, in some model setup
-# # usually it has two columns, the first should be called 'PID', the second 'REGION'
-# # only the second column matter in terms of name, and should have the region names used in the model
-#
-# region_units.spdf = spTransform(temp, CRS("+proj=longlat"))
-# rm(temp)
-
-# # Read in Raptis et al data
-# ppl.df = data.frame(read.csv(paste0(data_subf,'/ppl_cooling_tech/POWER_PLANTS_2016_Raptis.csv'),stringsAsFactors=FALSE))
-# raptis_types_map_cooling.df = data.frame(read.csv(paste0(data_subf,'/ppl_cooling_tech/raptis_types_map_cooling_type.csv'),stringsAsFactors=FALSE))
-# raptis_types_map_unit.df = data.frame(read.csv(paste0(data_subf,'/ppl_cooling_tech/raptis_types_map_unit_type.csv'),stringsAsFactors=FALSE))
-#
-# # MESSAGE technologies
-# msg_types.df = data.frame(read.csv(paste0(data_subf,'/ppl_cooling_tech/tech_names_ssp_msg.csv'),stringsAsFactors=FALSE))
-# msg_ppl = unique(c(raptis_types_map_unit.df$msgssp_type,raptis_types_map_unit.df$alt1,raptis_types_map_unit.df$alt2,raptis_types_map_unit.df$alt3))
-# msg_ppl = msg_ppl[-1*(which(msg_ppl==''))]
-# msg_cool = unique(raptis_types_map_cooling.df$msgssp_type)
-#
-# fuel_group = lapply(1:length(msg_ppl),function(x){
-# 	ind1 = which( as.character(raptis_types_map_unit.df$msgssp_type) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt1) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt2) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt3) == as.character(msg_ppl[x]) )
-# 	return(unique(unlist(strsplit(  as.character(raptis_types_map_unit.df$raptis_type[ind1]),'[.]' ))[seq(1,length(unlist(strsplit(  as.character(raptis_types_map_unit.df$raptis_type[ind1]),'[.]' ))),b=2)]))
-# 	})
-#
-# utype = lapply(1:length(msg_ppl),function(x){
-# 	ind1 = which( as.character(raptis_types_map_unit.df$msgssp_type) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt1) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt2) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt3) == as.character(msg_ppl[x]) )
-# 	return(unique(unlist(strsplit(  as.character(raptis_types_map_unit.df$raptis_type[ind1]),'[.]' ))[seq(2,length(unlist(strsplit(  as.character(raptis_types_map_unit.df$raptis_type[ind1]),'[.]' ))),b=2)]))
-# 	}	)
-#
-# cool = lapply(1:length(msg_cool),function(x){ as.character(raptis_types_map_cooling.df$raptis_type[which( as.character(raptis_types_map_cooling.df$msgssp_type) == as.character(msg_cool[x]) )])})
-#
-# # Pull out data for this specific technology for each region
-# cool_techs = unlist(lapply(1:length(msg_cool), function(x){as.matrix(unlist(lapply(1:length(msg_ppl),function(y){paste(msg_ppl[y],msg_cool[x],sep='.')})))}))
-# xs = rep(seq(1,length(msg_ppl),by=1),length(msg_cool))
-# ys = unlist(lapply(1:length(msg_cool),function(dd){ rep(dd,length(msg_ppl)) }))
-#
-# # Cooling techs by pid
-# cool_techs_by_pid = data.frame(do.call(rbind, lapply(1:length(cool_techs), function(z){
-# 	xx = xs[z]
-# 	yy = ys[z]
-# 	temp = ppl.df[which( as.character(ppl.df$Fuel_group) %in% as.character(fuel_group[[xx]]) &
-# 	                       as.character(ppl.df$UTYPE) %in% as.character(utype[[xx]]) &
-# 	                       as.character(ppl.df$COOL_complete) %in% as.character(cool[[yy]]) ),]
-# 	if(nrow(temp)>0)
-# 		{
-# 		temp$x = temp$long
-# 		temp$y = temp$lat
-# 		coordinates(temp) = ~ x + y
-# 		proj4string(temp) = proj4string(region_units.spdf)
-# 		temp$pid = unlist(over(temp,region_units.spdf[,which(names(region_units.spdf) == 'PID')]))
-# 		temp = temp[-1*(which(is.na(temp$pid) | is.na(temp$MW))),]
-# 		mw_by_pid = unlist(lapply(1:length(region_units.spdf@data$PID), function(bb){ max(0, sum( temp$MW[ which( as.character(temp$pid) == as.character(region_units.spdf@data$PID[bb]) ) ], na.rm=TRUE ), na.rm=TRUE) } ) )
-# 		return(mw_by_pid)
-# 		}else
-# 		{
-# 		mw_by_pid  = rep(0,length(region_units.spdf@data$REGION))
-# 		return(mw_by_pid)
-# 		}
-# 	})))
-# row.names(cool_techs_by_pid) = cool_techs
-#
-# type_general = unlist(strsplit( cool_techs, '[.]' ))[seq(1,length(unlist(strsplit( cool_techs, '[.]' ))),by=2)]
-# cool_general = unlist(strsplit( cool_techs, '[.]' ))[seq(2,length(unlist(strsplit( cool_techs, '[.]' ))),by=2)]
-# cool_techs_shares_by_pid = data.frame( do.call( cbind, lapply(1:length(region_units.spdf@data$REGION), function(r){ as.matrix( unlist( lapply( 1:length(cool_techs), function(z){
-# 	share =  cool_techs_by_pid[z,r] / sum( c( cool_techs_by_pid[ which( as.character(type_general) == as.character(type_general[z]) ), r] ), na.rm = TRUE)
-# 	if( is.na(share) | length(share) == 0 ){share =  round( sum( c( cool_techs_by_pid[ which( as.character(cool_general) == as.character(cool_general[z]) ), r] ), na.rm = TRUE) / sum( c( cool_techs_by_pid[ , r] ), na.rm = TRUE), digits = 3)}
-# 	return(round(share, digits = 3)) } ) ) ) } ) ) )
-# row.names(cool_techs_shares_by_pid) = cool_techs
-# names(cool_techs_shares_by_pid) = 	as.character(region_units.spdf@data$REGION)
-# cool_techs_shares_by_pid$cooling_utype = as.character(cool_techs)
-# cool_techs_shares_by_pid$cooling = as.character(cool_general)
-# cool_techs_shares_by_pid$utype = as.character(type_general)
-# cool_techs_shares_by_pid = cool_techs_shares_by_pid[,c('utype','cooling',as.character(region_units.spdf@data$REGION))]
-# cool_techs_shares_by_pid = cool_techs_shares_by_pid[ order( cool_techs_shares_by_pid$utype ), ]
-#
-# # This will be the file
-# write.csv(cool_techs_shares_by_pid,paste0(data_subf,'/cool_techs_region_share.csv'),row.names = FALSE)
-#
-# #add 'mix_' to the columns names for regions
-# cool_techs_shares_by_pid2 = cool_techs_shares_by_pid
-# new_names = paste0('mix_', names(cool_techs_shares_by_pid2)[!names(cool_techs_shares_by_pid2) %in% c('utype','cooling')] )
-# names(cool_techs_shares_by_pid2)[!names(cool_techs_shares_by_pid2) %in% c('utype','cooling')] = new_names
-#
-# # add the data to the cooltech_cost_and_shares_ssp_msg.csv file
-# cooltech_cost_shares = read.csv(paste0(data_subf,'/cooltech_cost_and_shares_ssp_msg.csv'),stringsAsFactors=FALSE)
-# cooltech_cost_shares = cooltech_cost_shares %>%
-#   select(utype,	cooling,investment_million_USD_per_MW_low,	investment_million_USD_per_MW_mid,	investment_million_USD_per_MW_high) %>%
-#   left_join(cool_techs_shares_by_pid2)
-#
-# # write new file
-# write.csv(cool_techs_shares_by_pid,paste0(data_subf,'/cooltech_cost_and_shares_ssp_msg.csv'),row.names = FALSE)