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Author: lrennels

src/components/RegionAggregatorSum.jl

@@ -1,7 +1,7 @@
 using Mimi
 
 @defcomp RegionAggregatorSum begin
-    
+
     inputregions = Index()
     outputregions = Index()
 
@@ -14,17 +14,17 @@ using Mimi
     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)
-        v.output[t, :] .= 0.
+    function run_timestep(p, v, d, t)
+        v.output[t, :] .= 0.0
 
         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

@@ -2,7 +2,7 @@ using Mimi, CSVFiles, DataFrames, Query, Interpolations, Arrow, CategoricalArray
 import IteratorInterfaceExtensions, Tables
 
 # (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)
@@ -63,50 +63,50 @@ 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
@@ -118,7 +118,7 @@ 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)
@@ -129,7 +129,7 @@ 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
@@ -150,13 +150,13 @@ 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
@@ -167,7 +167,7 @@ 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/test_Coupled.jl

@@ -9,12 +9,12 @@ m = Model()
 set_dimension!(m, :time, 2000:2300)
 
 # Handle the MimiRFFSPs.SPs component
-add_comp!(m, MimiRFFSPs.SPs, first = 2020)
+add_comp!(m, MimiRFFSPs.SPs, first=2020)
 set_dimension!(m, :country, inputregions)
 update_param!(m, :SPs, :country_names, inputregions)
 
 # Handle the MimiRFFSPs.RegionAggregatorSum component
-add_comp!(m, MimiRFFSPs.RegionAggregatorSum, first = 2020)
+add_comp!(m, MimiRFFSPs.RegionAggregatorSum, first=2020)
 
 set_dimension!(m, :inputregions, inputregions)
 set_dimension!(m, :outputregions, outputregions)
@@ -28,7 +28,7 @@ connect_param!(m, :RegionAggregatorSum, :input, :SPs, :population)
 run(m)
 
 # Should also work if Aggregator runs long, using backup data
-Mimi.set_first_last!(m, :RegionAggregatorSum, first = 2000)
+Mimi.set_first_last!(m, :RegionAggregatorSum, first=2000)
 backup = zeros(301, 184)
 connect_param!(m, :RegionAggregatorSum, :input, :SPs, :population, backup, ignoreunits=true)
 

test/test_RegionAggregatorSum.jl

@@ -21,8 +21,8 @@ run(m)
 
 expected_output = zeros(length(2000:2010), length(outputregions))
 for (i, output_region) in enumerate(outputregions)
-    idxs = findall(i -> i ==  output_region, dummy_input_output[:,2])
-    expected_output[:, i] = sum(data[:, idxs], dims = 2)
+    idxs = findall(i -> i == output_region, dummy_input_output[:, 2])
+    expected_output[:, i] = sum(data[:, idxs], dims=2)
 end
 
 @test (m[:RegionAggregatorSum, :output]) ≈ expected_output atol = 1e-9
@@ -31,5 +31,11 @@ bad_mapping = dummy_input_output.Output_Region
 bad_mapping[5] = "Region-MISSING" # this does not exist in the output regions list
 update_param!(m, :RegionAggregatorSum, :input_output_mapping, bad_mapping)
 
-error_msg = (try eval(run(m)) catch err err end).msg
+error_msg = (
+    try
+        eval(run(m))
+    catch err
+        err
+    end
+).msg
 @test occursin("All provided region names in the RegionAggregatorSum's input_output_mapping Parameter must exist in the output_region_names Parameter.", error_msg)