Format files using DocumentFormat

examples/main.jl

@@ -5,10 +5,10 @@ N = 10000
 seed = 350
 
 Random.seed!(seed)
-MimiIWG.run_scc_mcs(DICE, gas=:CO2, trials = N)
+MimiIWG.run_scc_mcs(DICE, gas=:CO2, trials=N)
 
 Random.seed!(seed)
-MimiIWG.run_scc_mcs(FUND, gas=:CO2, trials = N)
+MimiIWG.run_scc_mcs(FUND, gas=:CO2, trials=N)
 
 Random.seed!(seed)
-MimiIWG.run_scc_mcs(PAGE, gas=:CO2, trials = N)
+MimiIWG.run_scc_mcs(PAGE, gas=:CO2, trials=N)

examples/other_ghg.jl

@@ -1,8 +1,8 @@
 using MimiIWG
 
-#------------------------------------------------------------------------------
+# ------------------------------------------------------------------------------
 # Deterministic single runs of SC-CH4 and SC-N2O
-#------------------------------------------------------------------------------
+# ------------------------------------------------------------------------------
 
 # DICE
 scch4 = MimiIWG.compute_scc(DICE, USG1, gas=:CH4, year=2020, discount=0.025)
@@ -16,19 +16,19 @@ scn2o = MimiIWG.compute_scc(FUND, USG1, gas=:N2O, year=2020, discount=0.025)
 scch4 = MimiIWG.compute_scc(PAGE, USG1, gas=:CH4, year=2020, discount=0.025)
 scn2o = MimiIWG.compute_scc(PAGE, USG1, gas=:N2O, year=2020, discount=0.025)
 
-#------------------------------------------------------------------------------
+# ------------------------------------------------------------------------------
 # Full Monte Carlo simulations for SC-CH4 and SC-N2O for each model
-#------------------------------------------------------------------------------
+# ------------------------------------------------------------------------------
 
 # DICE
 # (will run for all 3 IWG discount rates if none are specified) 
-MimiIWG.run_scc_mcs(DICE, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir = "output")
-MimiIWG.run_scc_mcs(DICE, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir = "output")
+MimiIWG.run_scc_mcs(DICE, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir="output")
+MimiIWG.run_scc_mcs(DICE, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir="output")
 
 # FUND
-MimiIWG.run_scc_mcs(FUND, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir = "output")
-MimiIWG.run_scc_mcs(FUND, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir = "output")
+MimiIWG.run_scc_mcs(FUND, gas=:CH4, trials=10_000, perturbation_years=[2020], output_dir="output")
+MimiIWG.run_scc_mcs(FUND, gas=:N2O, trials=10_000, perturbation_years=[2020], output_dir="output")
 
 # PAGE
-MimiIWG.run_scc_mcs(PAGE, gas=:CH4, trials=10_000, perturbation_years=[2020],  output_dir = "output")
-MimiIWG.run_scc_mcs(PAGE, gas=:N2O, trials=10_000, perturbation_years=[2020],  output_dir = "output")
+MimiIWG.run_scc_mcs(PAGE, gas=:CH4, trials=10_000, perturbation_years=[2020],  output_dir="output")
+MimiIWG.run_scc_mcs(PAGE, gas=:N2O, trials=10_000, perturbation_years=[2020],  output_dir="output")

src/MimiIWG.jl

@@ -11,7 +11,7 @@ using MimiDICE2010
 using MimiFUND          # pinned to version 3.8 in package registration Compat.toml
 using MimiPAGE2009      
 using StatsBase
-using XLSX: readxlsx
+using XLSX:readxlsx
 using CSVFiles
 using DataFrames
 using Query

src/components/IWG_DICE_ScenarioChoice.jl

@@ -6,21 +6,21 @@
     scenarios = Index()
 
     # Variables (each one has its value set for the chosen scenario in the first timestep)
-    l       = Variable(index = [time])  # Population
-    E       = Variable(index = [time])  # Total CO2 emissions
-    forcoth = Variable(index = [time])  # other forcing
-    al      = Variable(index = [time])  # total factor productivity
+    l       = Variable(index=[time])  # Population
+    E       = Variable(index=[time])  # Total CO2 emissions
+    forcoth = Variable(index=[time])  # other forcing
+    al      = Variable(index=[time])  # total factor productivity
     k0      = Variable()                # initial capital stock
 
     # The number for which scenario to use 
     scenario_num = Parameter{Integer}()
 
     # Parameters (each one holds all five scenarios)
-    l_all       = Parameter(index = [time, scenarios])
-    E_all       = Parameter(index = [time, scenarios])
-    forcoth_all = Parameter(index = [time, scenarios])
-    al_all      = Parameter(index = [time, scenarios])
-    k0_all      = Parameter(index = [scenarios])
+    l_all       = Parameter(index=[time, scenarios])
+    E_all       = Parameter(index=[time, scenarios])
+    forcoth_all = Parameter(index=[time, scenarios])
+    al_all      = Parameter(index=[time, scenarios])
+    k0_all      = Parameter(index=[scenarios])
 
     function run_timestep(p, v, d, t)
         if is_first(t)

src/components/IWG_DICE_climatedynamics.jl

@@ -32,7 +32,7 @@
         elseif p.t2xco2 < 0.5
             v.TATM[t] = v.TA_eq[t]
         else
-            v.TATM[t] = v.TATM[t - 1] + p.c1 * ((p.FORC[t] - (p.fco22x/p.t2xco2) * v.TATM[t - 1]) - (p.c3 * (v.TATM[t - 1] - v.TOCEAN[t - 1])))
+            v.TATM[t] = v.TATM[t - 1] + p.c1 * ((p.FORC[t] - (p.fco22x / p.t2xco2) * v.TATM[t - 1]) - (p.c3 * (v.TATM[t - 1] - v.TOCEAN[t - 1])))
         end
 
         # Define function for TOCEAN

src/components/IWG_DICE_neteconomy.jl

@@ -42,10 +42,10 @@
         v.CPC[t] = 1000 * v.C[t] / p.l[t]
 
         # Define function for CPRICE
-        if t.t==26
+        if t.t == 26
             v.CPRICE[t] = v.CPRICE[t - 1]
         else
-            v.CPRICE[t] = p.pbacktime[t] * 1000 * p.MIU[t] ^ (p.expcost2 - 1)
+            v.CPRICE[t] = p.pbacktime[t] * 1000 * p.MIU[t]^(p.expcost2 - 1)
         end
 
     end

src/components/IWG_DICE_radiativeforcing.jl

@@ -16,7 +16,7 @@
             MAT_avg = 0.9796 * (p.MAT[t - 1] + p.MAT[t]) / 2
         end
 
-        v.FORC[t] = p.fco22x * (log((MAT_avg + 0.000001) / 596.4 ) / log(2)) + p.forcoth[t]
+        v.FORC[t] = p.fco22x * (log((MAT_avg + 0.000001) / 596.4) / log(2)) + p.forcoth[t]
 
     end
 

src/components/IWG_DICE_simple_gas_cycle.jl

@@ -1,40 +1,40 @@
 @defcomp IWG_DICE_simple_gas_cycle begin
 
     # Output: forcing
-    F_CH4 = Variable(index = [decades])    # Path of CH4 forcing [W/m^2] - decadal time step
-    F_N2O = Variable(index = [decades])    # Path of N2O forcing [W/m^2] - decadal time step
+    F_CH4 = Variable(index=[decades])    # Path of CH4 forcing [W/m^2] - decadal time step
+    F_N2O = Variable(index=[decades])    # Path of N2O forcing [W/m^2] - decadal time step
 
     # Input: emissions
-    E_CH4A = Parameter(index = [time])  # Annual CH4 emissions
-    E_N2OA = Parameter(index = [time])  # Annual N2O emissions
+    E_CH4A = Parameter(index=[time])  # Annual CH4 emissions
+    E_N2OA = Parameter(index=[time])  # Annual N2O emissions
 
     # Atmospheric concentration dynamics coefficients
-    delta_ch4 = Parameter(default = 1/12)   # Annual rate of decay of CH4 (IPCC FAR)
-    gamma_ch4 = Parameter(default = 0.3597) # Mass to volume conversion factor [ppb CH4/Mt CH4]
-    alpha_ch4 = Parameter(default = 0.036)  # Radiative forcing parameter (TAR page 358)
-    delta_n2o = Parameter(default = 1/114)  # Rate of decay of N2O (IPCC FAR)
-    gamma_n2o = Parameter(default = 0.2079) # Mass to volume conversion factor [ppb N2O/Mt N]
-    alpha_n2o = Parameter(default = 0.12)   # Radiative forcing parameter    
-    ipcc_adj =  Parameter(default = 1.4)    # IPCC AR4 adjustment for tropospheric ozone effect (25%) and stratospheric water vapor effect (15%)
-
-    M_pre = Parameter(default = 700)    # Pre-industrial CH4 concentrations [ppb](TAR page 358)
-    N_pre = Parameter(default = 270)    # Pre-industrial N2O concentrations [ppb](TAR page 358)
+    delta_ch4 = Parameter(default=1 / 12)   # Annual rate of decay of CH4 (IPCC FAR)
+    gamma_ch4 = Parameter(default=0.3597) # Mass to volume conversion factor [ppb CH4/Mt CH4]
+    alpha_ch4 = Parameter(default=0.036)  # Radiative forcing parameter (TAR page 358)
+    delta_n2o = Parameter(default=1 / 114)  # Rate of decay of N2O (IPCC FAR)
+    gamma_n2o = Parameter(default=0.2079) # Mass to volume conversion factor [ppb N2O/Mt N]
+    alpha_n2o = Parameter(default=0.12)   # Radiative forcing parameter    
+    ipcc_adj =  Parameter(default=1.4)    # IPCC AR4 adjustment for tropospheric ozone effect (25%) and stratospheric water vapor effect (15%)
+
+    M_pre = Parameter(default=700)    # Pre-industrial CH4 concentrations [ppb](TAR page 358)
+    N_pre = Parameter(default=270)    # Pre-industrial N2O concentrations [ppb](TAR page 358)
 
-    M_AA_2005 = Parameter(default = 1774)  # 2005 concentration of CH4
-    N_AA_2005 = Parameter(default = 319)   # 2005 concentration of N2O
+    M_AA_2005 = Parameter(default=1774)  # 2005 concentration of CH4
+    N_AA_2005 = Parameter(default=319)   # 2005 concentration of N2O
 
     # Other intermediate variables to calculate
-    M_AA     = Variable(index = [time]) # Atmospheric CH4 concentration (annual)
-    F_CH4A   = Variable(index = [time]) # Contribution of atmospheric CH4 to radiative forcing (annual)
-    N_AA     = Variable(index = [time]) # Atmospheric N2O concentration (annual)
-    F_N2OA   = Variable(index = [time]) # Contribution of atmospheric N2O to radiative forcing (annual)
+    M_AA     = Variable(index=[time]) # Atmospheric CH4 concentration (annual)
+    F_CH4A   = Variable(index=[time]) # Contribution of atmospheric CH4 to radiative forcing (annual)
+    N_AA     = Variable(index=[time]) # Atmospheric N2O concentration (annual)
+    F_N2OA   = Variable(index=[time]) # Contribution of atmospheric N2O to radiative forcing (annual)
     pre_f    = Variable()    # pre-industrial interaction effect
 
     function run_timestep(p, v, d, t)
 
         function f(M_A, N_A)
             # calculate the interaction effect on radiative forcing
-            return 0.47 * log(1 + 2.01 * 10 ^ -5 * (M_A * N_A) ^ 0.75 + 5.31 * 10^-15 * M_A * (M_A * N_A) ^ 1.52)
+            return 0.47 * log(1 + 2.01 * 10^-5 * (M_A * N_A)^0.75 + 5.31 * 10^-15 * M_A * (M_A * N_A)^1.52)
         end
 
         # Calculate the annual atmospheric concentrations
@@ -45,7 +45,7 @@
             v.pre_f = f(p.M_pre, p.N_pre)   # only need to calculate this once; used in each timestep below
         else
             v.M_AA[t] = (1 - p.delta_ch4) * v.M_AA[t - 1] + p.delta_ch4 * p.M_pre + p.gamma_ch4 * p.E_CH4A[t - 1]
-            v.N_AA[t] = (1 - p.delta_n2o) * v.N_AA[t - 1] + p.delta_n2o * p.N_pre + p.gamma_n2o * (28/44) * p.E_N2OA[t - 1]
+            v.N_AA[t] = (1 - p.delta_n2o) * v.N_AA[t - 1] + p.delta_n2o * p.N_pre + p.gamma_n2o * (28 / 44) * p.E_N2OA[t - 1]
         end
 
         # Calculate the annual forcing

src/components/IWG_FUND_ScenarioChoice.jl

@@ -6,23 +6,23 @@
     scenarios = Index()
 
     # Variables (each one has its value set for the chosen scenario in the first timestep)
-    globch4     = Variable(index = [time])
-    globn2o     = Variable(index = [time])
-    pgrowth     = Variable(index = [time, regions])
-    ypcgrowth   = Variable(index = [time, regions])
-    aeei        = Variable(index = [time, regions])
-    acei        = Variable(index = [time, regions])
+    globch4     = Variable(index=[time])
+    globn2o     = Variable(index=[time])
+    pgrowth     = Variable(index=[time, regions])
+    ypcgrowth   = Variable(index=[time, regions])
+    aeei        = Variable(index=[time, regions])
+    acei        = Variable(index=[time, regions])
 
     # The number for which scenario to use 
     scenario_num = Parameter{Integer}()
 
     # Parameters (each one holds all five scenarios)
-    globch4_all     = Parameter(index = [time, scenarios])
-    globn2o_all     = Parameter(index = [time, scenarios])
-    pgrowth_all     = Parameter(index = [time, regions, scenarios])
-    ypcgrowth_all   = Parameter(index = [time, regions, scenarios])
-    aeei_all        = Parameter(index = [time, regions, scenarios])
-    acei_all        = Parameter(index = [time, regions, scenarios])
+    globch4_all     = Parameter(index=[time, scenarios])
+    globn2o_all     = Parameter(index=[time, scenarios])
+    pgrowth_all     = Parameter(index=[time, regions, scenarios])
+    ypcgrowth_all   = Parameter(index=[time, regions, scenarios])
+    aeei_all        = Parameter(index=[time, regions, scenarios])
+    acei_all        = Parameter(index=[time, regions, scenarios])
 
     function run_timestep(p, v, d, t)
         if is_first(t)

src/components/IWG_FUND_impactsealevelrise.jl

@@ -30,17 +30,17 @@
 
     imigrate = Variable(index=[regions,regions])
 
-    incdens     = Parameter(default = 0.000635) # Normalization income density
-    emcst       = Parameter(default = 3) # emigration loss benchmark value
-    immcst      = Parameter(default = 0.4) # immigration loss benchmark
-    dvydl       = Parameter(default = 1) # income density elasticity of dryland value
-    wvel        = Parameter(default = 1.16) # wetland value income elasticity
-    wvbm        = Parameter(default = 0.00588) # wetland value benchmark value
-    slrwvpopdens0 = Parameter(default = 27.5937717888728) # wetland value income normalization value
-    wvpdl       = Parameter(default = 0.47)
-    wvsl        = Parameter(default = -0.11)
-    dvbm        = Parameter(default = 0.004) # dryland value benchmark value
-    slrwvypc0   = Parameter(default = 25000)
+    incdens     = Parameter(default=0.000635) # Normalization income density
+    emcst       = Parameter(default=3) # emigration loss benchmark value
+    immcst      = Parameter(default=0.4) # immigration loss benchmark
+    dvydl       = Parameter(default=1) # income density elasticity of dryland value
+    wvel        = Parameter(default=1.16) # wetland value income elasticity
+    wvbm        = Parameter(default=0.00588) # wetland value benchmark value
+    slrwvpopdens0 = Parameter(default=27.5937717888728) # wetland value income normalization value
+    wvpdl       = Parameter(default=0.47)
+    wvsl        = Parameter(default=-0.11)
+    dvbm        = Parameter(default=0.004) # dryland value benchmark value
+    slrwvypc0   = Parameter(default=25000)
 
     pc = Parameter(index=[regions])
     slrprtp = Parameter(index=[regions])

src/components/IWG_PAGE_ClimateTemperature.jl

@@ -39,20 +39,20 @@
     rtl_g_landtemperature = Variable(index=[time], unit="degreeC")
     rto_g_oceantemperature = Variable(index=[time], unit="degreeC")
     rt_g_globaltemperature = Variable(index=[time], unit="degreeC")
-    rt_g0_baseglobaltemp=Variable(unit="degreeC") #needed for feedback in CO2 cycle component
-    rtl_g0_baselandtemp=Variable(unit="degreeC") #needed for feedback in CH4 and N2O cycles
+    rt_g0_baseglobaltemp = Variable(unit="degreeC") # needed for feedback in CO2 cycle component
+    rtl_g0_baselandtemp = Variable(unit="degreeC") # needed for feedback in CH4 and N2O cycles
 
 
     function init(p, v, d)
-        #calculate global baseline temperature from initial regional temperatures
+        # calculate global baseline temperature from initial regional temperatures
 
         ocean_prop_ortion = 1. - sum(p.area) / 510000000.
 
         # Equation 21 from Hope (2006): initial global land temperature
         v.rtl_g0_baselandtemp = sum(p.rtl_0_realizedtemperature' .* p.area') / sum(p.area)
 
         # initial ocean and global temperatures
-        rto_g0_baseoceantemp = v.rtl_g0_baselandtemp/ p.rlo_ratiolandocean
+        rto_g0_baseoceantemp = v.rtl_g0_baselandtemp / p.rlo_ratiolandocean
         v.rt_g0_baseglobaltemp = ocean_prop_ortion * rto_g0_baseoceantemp + (1. - ocean_prop_ortion) * v.rtl_g0_baselandtemp
     end
 
@@ -84,7 +84,7 @@
             end
         else
             for rr in d.region
-                v.rt_realizedtemperature[tt, rr] = v.rt_realizedtemperature[tt-1, rr] + (1 - exp(-(p.y_year[tt] - p.y_year[tt-1]) / p.frt_warminghalflife)) * (v.et_equilibriumtemperature[tt, rr] - v.rt_realizedtemperature[tt-1, rr])
+                v.rt_realizedtemperature[tt, rr] = v.rt_realizedtemperature[tt - 1, rr] + (1 - exp(-(p.y_year[tt] - p.y_year[tt - 1]) / p.frt_warminghalflife)) * (v.et_equilibriumtemperature[tt, rr] - v.rt_realizedtemperature[tt - 1, rr])
             end
         end