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rffscghg · May 5, 2021 · cbe8336 · cbe8336
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diff --git a/examples/main.jl b/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)
diff --git a/examples/other_ghg.jl b/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")
diff --git a/src/MimiIWG.jl b/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
 
 export DICE, FUND, PAGE, # export the enumerated model_choice options
         USG1, USG2, USG3, USG4, USG5 # export the enumerated scenario_choice options 

diff --git a/src/components/IWG_DICE_ScenarioChoice.jl b/src/components/IWG_DICE_ScenarioChoice.jl
@@ -6,36 +6,36 @@
     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)
+    if is_first(t)
             # Get the specified scenario
-            scenario_num = p.scenario_num
-            if ! (scenario_num in d.scenarios)
-                error("Invalid :scenario_num in :IWGScenarioChoice component: $scenario_num. :scenario_num must be in $(d.scenarios).")
-            else
+        scenario_num = p.scenario_num
+        if ! (scenario_num in d.scenarios)
+            error("Invalid :scenario_num in :IWGScenarioChoice component: $scenario_num. :scenario_num must be in $(d.scenarios).")
+        else
                 # Copy over all of the values for that scenario
-                v.l[:]          = p.l_all[:, scenario_num]
-                v.E[:]          = p.E_all[:, scenario_num]
-                v.forcoth[:]    = p.forcoth_all[:, scenario_num]
-                v.al[:]         = p.al_all[:, scenario_num]
-                v.k0            = p.k0_all[scenario_num]
-            end
+            v.l[:]          = p.l_all[:, scenario_num]
+            v.E[:]          = p.E_all[:, scenario_num]
+            v.forcoth[:]    = p.forcoth_all[:, scenario_num]
+            v.al[:]         = p.al_all[:, scenario_num]
+            v.k0            = p.k0_all[scenario_num]
         end
     end
 end
+end
diff --git a/src/components/IWG_DICE_climatedynamics.jl b/src/components/IWG_DICE_climatedynamics.jl
@@ -20,28 +20,28 @@
     function run_timestep(p, v, d, t)
 
         # TA_eq added by EPA "to avoid over- and under-shoots to eq T when CS is very low
-        if is_first(t)
-            v.TA_eq[t] = 0.083
-        else
-            v.TA_eq[t] = p.FORC[t] * p.t2xco2 / p.fco22x
-        end
+    if is_first(t)
+        v.TA_eq[t] = 0.083
+    else
+        v.TA_eq[t] = p.FORC[t] * p.t2xco2 / p.fco22x
+    end
 
         # Define function for TATM
-        if is_first(t)
-            v.TATM[t] = p.tatm0
-        elseif p.t2xco2 < 0.5
+    if is_first(t)
+        v.TATM[t] = p.tatm0
+    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])))
-        end
+            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
-        if is_first(t)
-            v.TOCEAN[t] = p.tocean0
-        else
-            v.TOCEAN[t] = v.TOCEAN[t - 1] + p.c4 * (v.TATM[t - 1] - v.TOCEAN[t - 1])
-        end
-
+    if is_first(t)
+        v.TOCEAN[t] = p.tocean0
+    else
+        v.TOCEAN[t] = v.TOCEAN[t - 1] + p.c4 * (v.TATM[t - 1] - v.TOCEAN[t - 1])
     end
 
+end
+
 end
diff --git a/src/components/IWG_DICE_co2cycle.jl b/src/components/IWG_DICE_co2cycle.jl
@@ -25,28 +25,28 @@
     function run_timestep(p, v, d, t)
 
         # Define function for MU
-        if is_first(t)
-            v.MU[t] = p.mu0
-        else
-            v.MU[t] = v.MAT[t - 1] * p.b12 + v.MU[t - 1] * p.b22 + v.ML[t - 1] * p.b32
-        end
+    if is_first(t)
+        v.MU[t] = p.mu0
+    else
+        v.MU[t] = v.MAT[t - 1] * p.b12 + v.MU[t - 1] * p.b22 + v.ML[t - 1] * p.b32
+    end
 
         # Define function for ML
-        if is_first(t)
-            v.ML[t] = p.ml0
-        else
-            v.ML[t] = v.ML[t - 1] * p.b33 + v.MU[t - 1] * p.b23
-        end
+    if is_first(t)
+        v.ML[t] = p.ml0
+    else
+        v.ML[t] = v.ML[t - 1] * p.b33 + v.MU[t - 1] * p.b23
+    end
 
         # Define function for MAT
-        if is_first(t)
-            v.MAT[t] = p.mat0
+    if is_first(t)
+        v.MAT[t] = p.mat0
             # and also calculate MAT[2] below
-        end
-        if !is_last(t)
-            v.MAT[t + 1] = v.MAT[t] * p.b11 + v.MU[t] * p.b21 + p.E[t]
-        end
-
     end
+    if !is_last(t)
+        v.MAT[t + 1] = v.MAT[t] * p.b11 + v.MU[t] * p.b21 + p.E[t]
+    end
+
+end
 
 end
diff --git a/src/components/IWG_DICE_neteconomy.jl b/src/components/IWG_DICE_neteconomy.jl
@@ -24,30 +24,30 @@
     function run_timestep(p, v, d, t)
 
         # Define function for YNET
-        v.YNET[t] = p.YGROSS[t] / (1 + p.DAMFRAC[t])
+    v.YNET[t] = p.YGROSS[t] / (1 + p.DAMFRAC[t])
 
         # Define function for ABATECOST
-        v.ABATECOST[t] = p.YGROSS[t] * p.cost1[t] * (p.MIU[t]^p.expcost2) * (p.partfract[t]^(1 - p.expcost2))
+    v.ABATECOST[t] = p.YGROSS[t] * p.cost1[t] * (p.MIU[t]^p.expcost2) * (p.partfract[t]^(1 - p.expcost2))
 
         # Define function for Y
-        v.Y[t] = v.YNET[t] - v.ABATECOST[t]
+    v.Y[t] = v.YNET[t] - v.ABATECOST[t]
 
         # Define function for I
-        v.I[t] = p.S[t] * v.Y[t]
+    v.I[t] = p.S[t] * v.Y[t]
 
         # Define function for C
-        v.C[t] = v.Y[t] - v.I[t]
+    v.C[t] = v.Y[t] - v.I[t]
 
         # Define function for CPC
-        v.CPC[t] = 1000 * v.C[t] / p.l[t]
+    v.CPC[t] = 1000 * v.C[t] / p.l[t]
 
         # Define function for CPRICE
-        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)
-        end
-
+    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)
     end
 
 end
+
+end
diff --git a/src/components/IWG_DICE_radiativeforcing.jl b/src/components/IWG_DICE_radiativeforcing.jl
@@ -10,14 +10,14 @@
 
     function run_timestep(p, v, d, t)
 
-        if !is_last(t)
-            MAT_avg = (p.MAT[t] + p.MAT[t + 1]) / 2
-        else 
-            MAT_avg = 0.9796 * (p.MAT[t - 1] + p.MAT[t]) / 2
-        end
+    if !is_last(t)
+        MAT_avg = (p.MAT[t] + p.MAT[t + 1]) / 2
+    else 
+        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
+end
 
 end
diff --git a/src/components/IWG_DICE_simple_gas_cycle.jl b/src/components/IWG_DICE_simple_gas_cycle.jl
@@ -1,64 +1,64 @@
 @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)
+    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)
-        end
+        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
-        if is_first(t)
-            v.M_AA[t] = p.M_AA_2005
-            v.N_AA[t] = p.N_AA_2005  
-
-            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]
-        end
+    if is_first(t)
+        v.M_AA[t] = p.M_AA_2005
+        v.N_AA[t] = p.N_AA_2005  
+
+        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]
+    end
 
         # Calculate the annual forcing
-        v.F_CH4A[t] = p.ipcc_adj * (p.alpha_ch4 * (sqrt(v.M_AA[t]) - sqrt(p.M_pre)) - (f(v.M_AA[t], p.N_pre) - v.pre_f))
-        v.F_N2OA[t] = p.alpha_n2o * (sqrt(v.N_AA[t]) - sqrt(p.N_pre)) - (f(p.M_pre, v.N_AA[t]) - v.pre_f)
+    v.F_CH4A[t] = p.ipcc_adj * (p.alpha_ch4 * (sqrt(v.M_AA[t]) - sqrt(p.M_pre)) - (f(v.M_AA[t], p.N_pre) - v.pre_f))
+    v.F_N2OA[t] = p.alpha_n2o * (sqrt(v.N_AA[t]) - sqrt(p.N_pre)) - (f(p.M_pre, v.N_AA[t]) - v.pre_f)
 
         # calculate the decadal forcing at the end
-        if is_last(t)
-            v.F_CH4[:] = mean(reshape(v.F_CH4A[:], 10, length(d.decades)), dims=1)
-            v.F_N2O[:] = mean(reshape(v.F_N2OA[:], 10, length(d.decades)), dims=1)
-        end
-
+    if is_last(t)
+        v.F_CH4[:] = mean(reshape(v.F_CH4A[:], 10, length(d.decades)), dims=1)
+        v.F_N2O[:] = mean(reshape(v.F_N2OA[:], 10, length(d.decades)), dims=1)
     end
+
+end
 end