[AUTO] Format files using DocumentFormat

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)
@@ -27,8 +27,9 @@ function fill_deathrates!(source_Year, source_ISO3, source_DeathRate, deathrate,
 end
 
 function fill_emissions!(source_year, source_value, emissions_var, start_year, end_year)
-    for (t,v) in zip(source_year, source_value)
-        if start_year <= t end_year
+    for (t, v) in zip(source_year, source_value)
+        if start_year <= t
+            end_year
             year_index = TimestepIndex(t - start_year + 1)
             emissions_var[year_index] = v
         end
@@ -58,50 +59,50 @@ 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
@@ -113,7 +114,7 @@ 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)
@@ -124,28 +125,28 @@ 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")) |> 
-            @groupby(_.sample) |>
-            @orderby(key(_)) |>
-            @map(DataFrame(year=_.year, value=_.value)) |>
-            collect
+            g_datasets[:ch4] = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_ch4_emissions.csv")) |>
+                               @groupby(_.sample) |>
+                               @orderby(key(_)) |>
+                               @map(DataFrame(year=_.year, value=_.value)) |>
+                               collect
         end
         if !haskey(g_datasets, :n2o)
-            g_datasets[:n2o] = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_n2o_emissions.csv")) |> 
-            @groupby(_.sample) |>
-            @orderby(key(_)) |>
-            @map(DataFrame(year=_.year, value=_.value)) |>
-            collect
+            g_datasets[:n2o] = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_n2o_emissions.csv")) |>
+                               @groupby(_.sample) |>
+                               @orderby(key(_)) |>
+                               @map(DataFrame(year=_.year, value=_.value)) |>
+                               collect
         end
         if !haskey(g_datasets, :co2)
-            g_datasets[:co2] = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_co2_emissions.csv")) |> 
-            @groupby(_.sample) |>
-            @orderby(key(_)) |>
-            @map(DataFrame(year=_.year, value=_.value)) |>
-            collect
+            g_datasets[:co2] = load(joinpath(datadep"rffsps_v5", "emissions", "rffsp_co2_emissions.csv")) |>
+                               @groupby(_.sample) |>
+                               @orderby(key(_)) |>
+                               @map(DataFrame(year=_.year, value=_.value)) |>
+                               collect
         end
 
         # fill in the variales
@@ -160,15 +161,15 @@ 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 -> DataFrames.stack(i, Not(:sample)) |> 
-                i -> rename!(i, [:sample, :country, :value]) |> 
-                @groupby(_.sample) |>
-                @orderby(key(_)) |>
-                @map(DataFrame(country=_.country, value=_.value)) |>
-                collect
+            g_datasets[:ypc1990] = load(joinpath(datadep"rffsps_v5", "ypc1990", "rffsp_ypc1990.csv")) |>
+                                   DataFrame |>
+                                   i -> insertcols!(i, :sample => 1:10_000) |>
+                                        i -> DataFrames.stack(i, Not(:sample)) |>
+                                             i -> rename!(i, [:sample, :country, :value]) |>
+                                                  @groupby(_.sample) |>
+                                                  @orderby(key(_)) |>
+                                                  @map(DataFrame(country=_.country, value=_.value)) |>
+                                                  collect
         end
         if !haskey(g_datasets, :pop1990)
             g_datasets[:pop1990] = load(joinpath(@__DIR__, "..", "..", "data/population1990.csv")) |> DataFrame
@@ -182,7 +183,7 @@ 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