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Author: lrennels

src/MimiRFFSPs.jl

@@ -31,9 +31,9 @@ function get_model()
 
     set_dimension!(m, :time, 1750:2300)
 
-    add_comp!(m, SPs, :rffsp, first = 2020, last = 2300)
+    add_comp!(m, SPs, :rffsp, first=2020, last=2300)
 
-    all_countries = CSVFiles.load(joinpath(@__DIR__, "..", "data", "keys", "MimiRFFSPs_ISO3.csv")) |> DataFrame    
+    all_countries = CSVFiles.load(joinpath(@__DIR__, "..", "data", "keys", "MimiRFFSPs_ISO3.csv")) |> DataFrame
 
     set_dimension!(m, :country, all_countries.ISO3)
 
@@ -42,10 +42,9 @@ function get_model()
     return m
 end
 
-function get_mcs(sampling_ids::Union{Vector{Int}, Nothing} = nothing)
+function get_mcs(sampling_ids::Union{Vector{Int},Nothing}=nothing)
     # define the Monte Carlo Simulation and add some simple random variables
-    mcs = @defsim begin
-    end
+    mcs = @defsim begin end
 
     distrib = isnothing(sampling_ids) ? Mimi.EmpiricalDistribution(collect(1:10_000)) : Mimi.SampleStore(sampling_ids)
     Mimi.add_RV!(mcs, :socio_id_rv, distrib)

src/components/RegionAggregatorSum.jl

@@ -1,5 +1,5 @@
 @defcomp RegionAggregatorSum begin
-    
+
     inputregions = Index()
     outputregions = Index()
 
@@ -12,17 +12,17 @@
     input = Parameter(index=[time, inputregions])
     output = Variable(index=[time, outputregions])
 
-    function init(p,v,d)
+    function init(p, v, d)
         idxs = indexin(p.input_output_mapping, p.output_region_names)
         !isnothing(findfirst(i -> isnothing(i), idxs)) ? error("All provided region names in the RegionAggregatorSum's input_output_mapping Parameter must exist in the output_region_names Parameter.") : nothing
         v.input_output_mapping_int[:] = idxs
     end
 
-    function run_timestep(p,v,d,t)
+    function run_timestep(p, v, d, t)
         v.output[t, :] .= 0.
 
         for i in d.inputregions
-            v.output[t, v.input_output_mapping_int[i]] += p.input[t,i]
+            v.output[t, v.input_output_mapping_int[i]] += p.input[t, i]
         end
     end
 end

src/components/SPs.jl

@@ -1,5 +1,5 @@
 # (10/25/2021) BEA Table 1.1.9, line 1 GDP annual values as linked here: https://apps.bea.gov/iTable/iTable.cfm?reqid=19&step=3&isuri=1&select_all_years=0&nipa_table_list=13&series=a&first_year=2005&last_year=2020&scale=-99&categories=survey&thetable=
-const pricelevel_2011_to_2005 = 87.504/98.164
+const pricelevel_2011_to_2005 = 87.504 / 98.164
 
 function fill_socioeconomics!(source_Year, source_Country, source_Pop, source_GDP, population, gdp, country_lookup, start_year, end_year)
     for i in 1:length(source_Year)
@@ -60,50 +60,50 @@ end
     end_year = Parameter{Int}(default=Int(2300)) # year (annual) data should end
     country_names = Parameter{String}(index=[country]) # need the names of the countries from the dimension
     id = Parameter{Int64}(default=Int(6546)) # the sample (out of 10,000) to be used
-    
-    population  = Variable(index=[time, country], unit="million")
-    population_global  = Variable(index=[time], unit="million")
-    deathrate   = Variable(index=[time, country], unit="deaths/1000 persons/yr")
-    gdp         = Variable(index=[time, country], unit="billion US\$2005/yr")
-    gdp_global         = Variable(index=[time], unit="billion US\$2005/yr")
-    
-    population1990  = Variable(index=[country], unit = "million")
-    gdp1990         = Variable(index=[country], unit = unit="billion US\$2005/yr")
-    
-    co2_emissions   = Variable(index=[time], unit="GtC/yr")
-    ch4_emissions   = Variable(index=[time], unit="MtCH4/yr")
-    n2o_emissions   = Variable(index=[time], unit="MtN2/yr")
-
-    function init(p,v,d)
+
+    population = Variable(index=[time, country], unit="million")
+    population_global = Variable(index=[time], unit="million")
+    deathrate = Variable(index=[time, country], unit="deaths/1000 persons/yr")
+    gdp = Variable(index=[time, country], unit="billion US\$2005/yr")
+    gdp_global = Variable(index=[time], unit="billion US\$2005/yr")
+
+    population1990 = Variable(index=[country], unit="million")
+    gdp1990 = Variable(index=[country], unit=unit = "billion US\$2005/yr")
+
+    co2_emissions = Variable(index=[time], unit="GtC/yr")
+    ch4_emissions = Variable(index=[time], unit="MtCH4/yr")
+    n2o_emissions = Variable(index=[time], unit="MtN2/yr")
+
+    function init(p, v, d)
 
         # add countrys to a dictionary where each country key has a value holding it's 
         # index in country_names
-        country_lookup = Dict{String,Int}(name=>i for (i,name) in enumerate(p.country_names))
+        country_lookup = Dict{String,Int}(name => i for (i, name) in enumerate(p.country_names))
         country_indices = d.country::Vector{Int} # helper for type stable country indices
 
         # ----------------------------------------------------------------------
         # Socioeconomic Data
         #   population in millions of individuals
         #   GDP in billions of $2005 USD
-       
+
         # Load Feather File
         t = Arrow.Table(joinpath(datadep"rffsps_v5", "pop_income", "rffsp_pop_income_run_$(p.id).feather"))
         fill_socioeconomics!(t.Year, t.Country, t.Pop, t.GDP, v.population, v.gdp, country_lookup, p.start_year, p.end_year)
 
         for year in p.start_year:5:p.end_year-5, country in country_indices
             year_as_timestep = TimestepIndex(year - p.start_year + 1)
-            pop_interpolator = LinearInterpolation(Float64[year, year+5], [log(v.population[year_as_timestep,country]), log(v.population[year_as_timestep+5,country])])
-            gdp_interpolator = LinearInterpolation(Float64[year, year+5], [log(v.gdp[year_as_timestep,country]), log(v.gdp[year_as_timestep+5,country])])
+            pop_interpolator = LinearInterpolation(Float64[year, year+5], [log(v.population[year_as_timestep, country]), log(v.population[year_as_timestep+5, country])])
+            gdp_interpolator = LinearInterpolation(Float64[year, year+5], [log(v.gdp[year_as_timestep, country]), log(v.gdp[year_as_timestep+5, country])])
             for year2 in year+1:year+4
                 year2_as_timestep = TimestepIndex(year2 - p.start_year + 1)
-                v.population[year2_as_timestep,country] = exp(pop_interpolator[year2])
-                v.gdp[year2_as_timestep,country] = exp(gdp_interpolator[year2])
+                v.population[year2_as_timestep, country] = exp(pop_interpolator[year2])
+                v.gdp[year2_as_timestep, country] = exp(gdp_interpolator[year2])
             end
         end
-        
+
         # add global data for future accessibility and quality control
-        v.gdp_global[:,:] = sum(v.gdp[:,:], dims = 2) # sum across countries, which are the second dimension
-        v.population_global[:,:] = sum(v.population[:,:], dims = 2) # sum across countries, which are the second dimension
+        v.gdp_global[:, :] = sum(v.gdp[:, :], dims=2) # sum across countries, which are the second dimension
+        v.population_global[:, :] = sum(v.population[:, :], dims=2) # sum across countries, which are the second dimension
 
         # ----------------------------------------------------------------------
         # Death Rate Data
@@ -115,7 +115,7 @@ end
             g_datasets[:pop_trajectory_key] = (load(joinpath(datadep"rffsps_v5", "sample_numbers", "sampled_pop_trajectory_numbers.csv")) |> DataFrame).x
         end
         deathrate_trajectory_id = convert(Int64, g_datasets[:pop_trajectory_key][p.id])
-        
+
         # Load Feather File
         t = Arrow.Table(joinpath(datadep"rffsps_v5", "death_rates", "rffsp_death_rates_run_$(deathrate_trajectory_id).feather"))
         fill_deathrates!(t.Year, t.ISO3, t.DeathRate, v.deathrate, country_lookup, p.start_year, p.end_year)
@@ -126,7 +126,7 @@ end
         #   carbon dioxide emissions in GtC
         #   nitrous oxide emissions in MtN2
         #   methane emissions in MtCH4
-        
+
         # add data to the global dataset if it's not there
         if !haskey(g_datasets, :ch4)
             g_datasets[:ch4] = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_ch4_emissions.csv")) |> DataFrame
@@ -147,13 +147,13 @@ end
         # Population and GDP 1990 Values
 
         if !haskey(g_datasets, :ypc1990)
-            g_datasets[:ypc1990] = load(joinpath(datadep"rffsps_v5", "ypc1990", "rffsp_ypc1990.csv")) |> 
-                DataFrame |> 
-                i -> insertcols!(i, :sample => 1:10_000) |> 
-                i -> stack(i, Not(:sample)) |> 
-                DataFrame |> 
-                i -> rename!(i, [:sample, :country, :value]) |> 
-                DataFrame
+            g_datasets[:ypc1990] = load(joinpath(datadep"rffsps_v5", "ypc1990", "rffsp_ypc1990.csv")) |>
+                                   DataFrame |>
+                                   i -> insertcols!(i, :sample => 1:10_000) |>
+                                        i -> stack(i, Not(:sample)) |>
+                                             DataFrame |>
+                                             i -> rename!(i, [:sample, :country, :value]) |>
+                                                  DataFrame
         end
         if !haskey(g_datasets, :pop1990)
             g_datasets[:pop1990] = load(joinpath(@__DIR__, "..", "..", "data/population1990.csv")) |> DataFrame
@@ -164,7 +164,7 @@ end
 
     end
 
-    function run_timestep(p,v,d,t)
+    function run_timestep(p, v, d, t)
 
         if !(gettime(t) in p.start_year:p.end_year)
             error("Cannot run SP component in year $(gettime(t)), SP data is not available for this model and year.")

test/runtests.jl

@@ -15,4 +15,4 @@ end
 
 @testset "API" begin
     include("test_API.jl")
-end
+end

test/test_API.jl

@@ -19,12 +19,12 @@ update_param!(m, :rffsp, :id, id)
 run(m)
 
 # check emissions
-ch4 = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_ch4_emissions.csv")) |> 
-    DataFrame |> @filter(_.year in collect(2020:2300)) |> @filter(_.sample == id) |> DataFrame
-n2o = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_n2o_emissions.csv")) |> 
-    DataFrame |> @filter(_.year in collect(2020:2300)) |> @filter(_.sample == id) |> DataFrame
-co2 = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_co2_emissions.csv")) |> 
-    DataFrame |> @filter(_.year in collect(2020:2300)) |> @filter(_.sample == id) |> DataFrame
+ch4 = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_ch4_emissions.csv")) |>
+      DataFrame |> @filter(_.year in collect(2020:2300)) |> @filter(_.sample == id) |> DataFrame
+n2o = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_n2o_emissions.csv")) |>
+      DataFrame |> @filter(_.year in collect(2020:2300)) |> @filter(_.sample == id) |> DataFrame
+co2 = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_co2_emissions.csv")) |>
+      DataFrame |> @filter(_.year in collect(2020:2300)) |> @filter(_.sample == id) |> DataFrame
 
 @test m[:rffsp, :co2_emissions][findfirst(i -> i == 2020, collect(1750:2300)):end] ≈ co2.value atol = tolerance
 @test m[:rffsp, :ch4_emissions][findfirst(i -> i == 2020, collect(1750:2300)):end] ≈ ch4.value atol = tolerance
@@ -33,101 +33,101 @@ co2 = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_co2_emissions.csv"))
 # check socioeconomics
 
 t = Arrow.Table(joinpath(datadep"rffsps_v5", "pop_income", "rffsp_pop_income_run_$id.feather"))
-socio_df = DataFrame(   :Year => copy(t.Year), 
-                        :Country => copy(t.Country), 
-                        :Pop => copy(t.Pop), 
-                        :GDP => copy(t.GDP)
-                    ) |>
-                    @filter(_.Year in collect(2020:5:2300)) |>
-                    DataFrame
+socio_df = DataFrame(:Year => copy(t.Year),
+               :Country => copy(t.Country),
+               :Pop => copy(t.Pop),
+               :GDP => copy(t.GDP)
+           ) |>
+           @filter(_.Year in collect(2020:5:2300)) |>
+           DataFrame
 
 for country in all_countries.ISO3
 
     pop_data_model = getdataframe(m, :rffsp, :population) |>
-        @filter(_.time in collect(2020:5:2300) && _.country == country) |>
-        DataFrame |>
-        @orderby(:time) |>
-        DataFrame
+                     @filter(_.time in collect(2020:5:2300) && _.country == country) |>
+                     DataFrame |>
+                     @orderby(:time) |>
+                     DataFrame
 
     gdp_data_model = getdataframe(m, :rffsp, :gdp) |>
-        @filter(_.time in collect(2020:5:2300) && _.country == country) |>
-        DataFrame |>
-        @orderby(:time) |>
-        DataFrame
+                     @filter(_.time in collect(2020:5:2300) && _.country == country) |>
+                     DataFrame |>
+                     @orderby(:time) |>
+                     DataFrame
 
     socio_df_country = socio_df |>
-        @filter(_.Year in collect(2020:5:2300) && _.Country == country) |>
-        DataFrame |>
-        @orderby(:Year) |>
-        DataFrame
+                       @filter(_.Year in collect(2020:5:2300) && _.Country == country) |>
+                       DataFrame |>
+                       @orderby(:Year) |>
+                       DataFrame
 
-    @test pop_data_model.population  ≈ socio_df_country.Pop ./ 1e3  atol = tolerance
-    @test gdp_data_model.gdp  ≈ socio_df_country.GDP ./ 1e3 .* MimiRFFSPs.pricelevel_2011_to_2005 atol = tolerance
+    @test pop_data_model.population ≈ socio_df_country.Pop ./ 1e3 atol = tolerance
+    @test gdp_data_model.gdp ≈ socio_df_country.GDP ./ 1e3 .* MimiRFFSPs.pricelevel_2011_to_2005 atol = tolerance
 end
 
 socio_gdf = groupby(socio_df, :Year)
 
 model_population_global = getdataframe(m, :rffsp, :population_global) |> @filter(_.time in collect(2020:5:2300)) |> @orderby(:time) |> DataFrame
-model_gdp_global = getdataframe(m, :rffsp, :gdp_global) |> @filter(_.time in collect(2020:5:2300)) |> @orderby(:time)|> DataFrame
+model_gdp_global = getdataframe(m, :rffsp, :gdp_global) |> @filter(_.time in collect(2020:5:2300)) |> @orderby(:time) |> DataFrame
 
-@test model_population_global.population_global ≈ (combine(socio_gdf, :Pop => sum).Pop_sum ./ 1e3)  atol = tolerance
-@test model_gdp_global.gdp_global ≈ (combine(socio_gdf, :GDP => sum).GDP_sum ./ 1e3 .* MimiRFFSPs.pricelevel_2011_to_2005)  atol = 1e-7 # slightly higher tolerance
+@test model_population_global.population_global ≈ (combine(socio_gdf, :Pop => sum).Pop_sum ./ 1e3) atol = tolerance
+@test model_gdp_global.gdp_global ≈ (combine(socio_gdf, :GDP => sum).GDP_sum ./ 1e3 .* MimiRFFSPs.pricelevel_2011_to_2005) atol = 1e-7 # slightly higher tolerance
 
 # check death rate
 pop_trajectory_key = (load(joinpath(datadep"rffsps_v5", "sample_numbers", "sampled_pop_trajectory_numbers.csv")) |> DataFrame).x
 deathrate_trajectory_id = convert(Int64, pop_trajectory_key[id])
-        
+
 # Load Feather File
 original_years = collect(2023:5:2300)
 t = Arrow.Table(joinpath(datadep"rffsps_v5", "death_rates", "rffsp_death_rates_run_$(deathrate_trajectory_id).feather"))
-deathrate_df = DataFrame(:Year => copy(t.Year), 
-                        :Country => copy(t.ISO3), 
-                        :DeathRate => copy(t.DeathRate)
-                    ) |>
-                    @filter(_.Year in original_years) |>
-                    DataFrame
+deathrate_df = DataFrame(:Year => copy(t.Year),
+                   :Country => copy(t.ISO3),
+                   :DeathRate => copy(t.DeathRate)
+               ) |>
+               @filter(_.Year in original_years) |>
+               DataFrame
 
 for country in all_countries.ISO3
 
     deathrate_data_model = getdataframe(m, :rffsp, :deathrate) |>
-        @filter(_.time in original_years && _.country == country) |>
-        DataFrame |>
-        @orderby(:time) |>
-        DataFrame
-
-    deathrate_df_country = deathrate_df |>         
-        @filter(_.Year in original_years && _.Country == country) |>
-        DataFrame |>
-        @orderby(:Year) |>
-        DataFrame
-
-    @test deathrate_data_model.deathrate  ≈ deathrate_df_country.DeathRate  atol = tolerance
+                           @filter(_.time in original_years && _.country == country) |>
+                           DataFrame |>
+                           @orderby(:time) |>
+                           DataFrame
+
+    deathrate_df_country = deathrate_df |>
+                           @filter(_.Year in original_years && _.Country == country) |>
+                           DataFrame |>
+                           @orderby(:Year) |>
+                           DataFrame
+
+    @test deathrate_data_model.deathrate ≈ deathrate_df_country.DeathRate atol = tolerance
 end
 
 # check pop 1990
 
-population1990 = load(joinpath(@__DIR__, "..", "data", "population1990.csv")) |> 
-    DataFrame |>
-    @orderby(_.ISO3) |>
-    DataFrame
+population1990 = load(joinpath(@__DIR__, "..", "data", "population1990.csv")) |>
+                 DataFrame |>
+                 @orderby(_.ISO3) |>
+                 DataFrame
 
 @test m[:rffsp, :population1990] ≈ population1990.Population atol = tolerance
 
 # check gdp 1990
 
-ypc1990 = load(joinpath(datadep"rffsps_v5", "ypc1990", "rffsp_ypc1990.csv")) |> 
-                DataFrame |> 
-                i -> insertcols!(i, :sample => 1:10_000) |> 
-                i -> stack(i, Not(:sample)) |> 
-                DataFrame |> 
-                @filter(_.sample == id) |>
-                DataFrame |>
-                @orderby(_.variable) |>
-                DataFrame
-                
+ypc1990 = load(joinpath(datadep"rffsps_v5", "ypc1990", "rffsp_ypc1990.csv")) |>
+          DataFrame |>
+          i -> insertcols!(i, :sample => 1:10_000) |>
+               i -> stack(i, Not(:sample)) |>
+                    DataFrame |>
+                    @filter(_.sample == id) |>
+                    DataFrame |>
+                    @orderby(_.variable) |>
+                    DataFrame
+
 gdp1990_model = getdataframe(m, :rffsp, :gdp1990) # billions
 pop1990_model = getdataframe(m, :rffsp, :population1990) # millions
-ypc1990_model = gdp1990_model.gdp1990  ./ pop1990_model.population1990 .* 1e3 # per capita
+ypc1990_model = gdp1990_model.gdp1990 ./ pop1990_model.population1990 .* 1e3 # per capita
 
 @test ypc1990_model ≈ ypc1990.value .* MimiRFFSPs.pricelevel_2011_to_2005 atol = tolerance
 
@@ -153,9 +153,9 @@ for id in ids
 end
 
 # Alternatively run the Monte Carlo Simulation on model `m` for sample ids 1,2, and 3
-mcs = MimiRFFSPs.get_mcs([1,2,3])
+mcs = MimiRFFSPs.get_mcs([1, 2, 3])
 Mimi.add_save!(mcs, (:rffsp, :id))
 Mimi.add_save!(mcs, (:rffsp, :co2_emissions))
 results = run(mcs, m, 3)
 
-@test getdataframe(results, :rffsp, :id).id == [1,2,3]
+@test getdataframe(results, :rffsp, :id).id == [1, 2, 3]