same opts as climaatmos

.JuliaFormatter.toml

@@ -1,3 +1,5 @@
+margin = 80
+indent = 4
 always_for_in = true
 whitespace_typedefs = true
-whitespace_ops_in_indices = true
+whitespace_ops_in_indices = true

.dev/up_deps.jl

@@ -4,7 +4,12 @@ files in all of our environments.
 =#
 
 root = dirname(@__DIR__)
-dirs = (root, joinpath(root, "test"), joinpath(root, "docs"), joinpath(root, "parcel"))
+dirs = (
+    root,
+    joinpath(root, "test"),
+    joinpath(root, "docs"),
+    joinpath(root, "parcel"),
+)
 
 cd(root) do
     for dir in dirs

box/Alpert_Knopf_2016_forward.jl

@@ -49,8 +49,12 @@ p_def = (; tps, A_aero, aerosol, cooling_rate, N₀)
 
 # run the box model without and with distribution sampling
 sol_cst = run_box(IC, t_0, t_end, (p_def..., const_dt = dt, flag = false))
-sol_var =
-    run_box(IC, t_0, t_end, (p_def..., const_dt = dt, flag = true, Aj_sorted = Aj_sorted))
+sol_var = run_box(
+    IC,
+    t_0,
+    t_end,
+    (p_def..., const_dt = dt, flag = true, Aj_sorted = Aj_sorted),
+)
 
 # Compute the immersion freezing rate for the no-sampling case
 Δa = @. FT(1) - CMO.a_w_ice(tps, sol_cst[1, :])

docs/make.jl

@@ -72,7 +72,12 @@ pages = Any[
 ]
 
 mathengine = MathJax(
-    Dict(:TeX => Dict(:equationNumbers => Dict(:autoNumber => "AMS"), :Macros => Dict())),
+    Dict(
+        :TeX => Dict(
+            :equationNumbers => Dict(:autoNumber => "AMS"),
+            :Macros => Dict(),
+        ),
+    ),
 )
 
 format = Documenter.HTML(

docs/src/guides/GettingStarted.jl

@@ -35,5 +35,6 @@ nothing #hide
 
 # Finally, we call `accretion`, which will return the accretion rates for
 # cloud and rain water specific humidities, as well as cloud and rain water number concentrations.
-(; dq_rai_dt, dq_liq_dt, dN_rai_dt, dN_liq_dt) = CM2.accretion(SB2006, qₗ, qᵣ, ρₐ, Nₗ)
+(; dq_rai_dt, dq_liq_dt, dN_rai_dt, dN_liq_dt) =
+    CM2.accretion(SB2006, qₗ, qᵣ, ρₐ, Nₗ)
 @info("Accretion rates: ", dq_rai_dt, dq_liq_dt, dN_rai_dt, dN_liq_dt)

docs/src/guides/Parameters.jl

@@ -46,8 +46,10 @@ nothing #hide
 # Overwriting the parameters can also be done using dictionaries instead of toml files.
 # As an example we create a dictionary were we define a different value of the
 # rain autoconversion time scale and create another parameter struct based on it.
-override_file =
-    Dict("rain_autoconversion_timescale" => Dict("value" => 13, "type" => "float"))
+override_file = Dict(
+    "rain_autoconversion_timescale" =>
+        Dict("value" => 13, "type" => "float"),
+)
 toml_dict2 = CP.create_toml_dict(FT; override_file)
 const overwrite2 = CMP.Rain(toml_dict2)
 

docs/src/plots/ARGplots.jl

@@ -48,7 +48,9 @@ function mass2vol(mass_mixing_ratios)
     else
         densities = (sulfate.ρ,)
     end
-    volfractions = (mass_mixing_ratios ./ densities) ./ sum(mass_mixing_ratios ./ densities)
+    volfractions =
+        (mass_mixing_ratios ./ densities) ./
+        sum(mass_mixing_ratios ./ densities)
     return volfractions
 end
 
@@ -160,9 +162,11 @@ function make_ARG_figX(X)
                 end
                 AD = AM.AerosolDistribution((paper_mode_1, paper_mode_2))
                 act_frac1[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[1] / N_1
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[1] /
+                    N_1
                 act_frac2[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[2] / N2i
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[2] /
+                    N2i
                 global it += 1
             end
 
@@ -211,9 +215,11 @@ function make_ARG_figX(X)
                 end
                 AD = AM.AerosolDistribution((paper_mode_1, paper_mode_2))
                 act_frac1[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[1] / N_1
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[1] /
+                    N_1
                 act_frac2[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[2] / N2i
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[2] /
+                    N2i
                 global it += 1
             end
 
@@ -264,9 +270,11 @@ function make_ARG_figX(X)
                 end
                 AD = AM.AerosolDistribution((paper_mode_1, paper_mode_2))
                 act_frac1[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[1] / N_1
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[1] /
+                    N_1
                 act_frac2[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[2] / N_2
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[2] /
+                    N_2
                 global it += 1
             end
 
@@ -314,9 +322,11 @@ function make_ARG_figX(X)
                 end
                 AD = AM.AerosolDistribution((paper_mode_1, paper_mode_2))
                 act_frac1[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[1] / N_1
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[1] /
+                    N_1
                 act_frac2[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[2] / N_2
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, w, q)[2] /
+                    N_2
                 global it += 1
             end
 
@@ -366,9 +376,11 @@ function make_ARG_figX(X)
 
             for wi in w
                 act_frac1[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, wi, q)[1] / N_1
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, wi, q)[1] /
+                    N_1
                 act_frac2[it] =
-                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, wi, q)[2] / N_2
+                    AA.N_activated_per_mode(ap, AD, aip, tps, T, p, wi, q)[2] /
+                    N_2
                 global it += 1
             end
 

docs/src/plots/ARGplots_fig1.jl

@@ -68,7 +68,8 @@ for N_2 in N_2_range
         (sulfate.ρ,),
     )
     AD_B = AM.AerosolDistribution((paper_mode_1_B, paper_mode_2_B))
-    N_act_frac_B[it] = AA.N_activated_per_mode(ap, AD_B, aip, tps, T, p, w, q)[1] / N_1
+    N_act_frac_B[it] =
+        AA.N_activated_per_mode(ap, AD_B, aip, tps, T, p, w, q)[1] / N_1
     global it += 1
 end
 
@@ -83,7 +84,17 @@ PL.plot(
     xlabel = "Mode 2 aerosol number concentration [1/cm3]",
     ylabel = "Mode 1 number fraction activated",
 )
-PL.scatter!(Fig1_x_obs, Fig1_y_obs, markercolor = :black, label = "paper observations")
-PL.plot!(Fig1_x_param, Fig1_y_param, linecolor = :black, label = "paper parameterization")
+PL.scatter!(
+    Fig1_x_obs,
+    Fig1_y_obs,
+    markercolor = :black,
+    label = "paper observations",
+)
+PL.plot!(
+    Fig1_x_param,
+    Fig1_y_param,
+    linecolor = :black,
+    label = "paper parameterization",
+)
 
 PL.savefig("Abdul-Razzak_and_Ghan_fig_1.svg")

docs/src/plots/Baumgartner2022_fig5.jl

@@ -35,22 +35,27 @@ sol = RS.find_zero(fun, RS.SecantMethod(FT(0), FT(0.5)), RS.CompactSolution())
 a_w = Δa_crit .+ a_w_ice
 
 # various ways Baumgartner calculates vapor pressures
-Baumgartner_p_ice(T) = exp(9.550426 - 5723.265 / T + 3.53068 * log(T) - 0.00728332 * T)
+Baumgartner_p_ice(T) =
+    exp(9.550426 - 5723.265 / T + 3.53068 * log(T) - 0.00728332 * T)
 MK_p_liq(T) = exp(
     54.842763 - 6763.22 / T - 4.21 * log(T) +
     0.000367 * T +
-    tanh(0.0415 * (T - 218.8)) * (53.878 - 1331.22 / T - 9.44523 * log(T) + 0.014025 * T),
+    tanh(0.0415 * (T - 218.8)) *
+    (53.878 - 1331.22 / T - 9.44523 * log(T) + 0.014025 * T),
+)
+Tab_p_liq(T) = exp(
+    100 * (18.4524 - 3505.15788 / T - 330918.55 / (T^2) + 12725068.26 / (T^3)),
 )
-Tab_p_liq(T) =
-    exp(100 * (18.4524 - 3505.15788 / T - 330918.55 / (T^2) + 12725068.26 / (T^3)))
 Nach_p_liq(T) = exp(74.8727 - 7167.405 / T - 7.77107 * log(T) + 0.00505 * T)
 
 a_w_MK = [Δa_crit + (Baumgartner_p_ice(T)) / (MK_p_liq(T)) for T in T_range]
 a_w_Tab = [Δa_crit + (Baumgartner_p_ice(T)) / (Tab_p_liq(T)) for T in T_range]
 a_w_Nach = [Δa_crit + (Baumgartner_p_ice(T)) / (Nach_p_liq(T)) for T in T_range]
 a_w_Luo = [
     Δa_crit +
-    (Baumgartner_p_ice(T)) / (CMO.H2SO4_soln_saturation_vapor_pressure(H2SO4_prs, 0.0, T)) for T in T_range
+    (Baumgartner_p_ice(T)) /
+    (CMO.H2SO4_soln_saturation_vapor_pressure(H2SO4_prs, 0.0, T)) for
+    T in T_range
 ]
 
 #! format: off

docs/src/plots/Cholette2019_fig1.jl

@@ -67,14 +67,20 @@ function fig1()
     F_liqs = (FT(0), FT(0.33), FT(0.67), FT(1))
     F_rs = (FT(0), FT(1 - eps(FT)))
 
-    labels = ["F_liq = 0", "F_liq = 0.33", "F_liq = 0.67", "F_liq = 1", "ϕᵢ = 1"]
+    labels =
+        ["F_liq = 0", "F_liq = 0.33", "F_liq = 0.67", "F_liq = 1", "ϕᵢ = 1"]
     colors = [:black, :blue, :red, :green]
     res = 50
 
     fig = Plt.Figure(size = (1200, 400))
 
     #F_r = 0
-    ax1 = Plt.Axis(fig[1:7, 1:9], title = "Fᵣ = 0", xlabel = "Dₚ (m)", ylabel = "V (m s⁻¹)")
+    ax1 = Plt.Axis(
+        fig[1:7, 1:9],
+        title = "Fᵣ = 0",
+        xlabel = "Dₚ (m)",
+        ylabel = "V (m s⁻¹)",
+    )
 
     for i in 1:4
         D_ps, V_0, V_0_ϕ = get_values(
@@ -87,12 +93,23 @@ function fig1()
             res,
         )
         Plt.lines!(ax1, D_ps, V_0_ϕ, color = colors[i], label = labels[i])
-        Plt.lines!(ax1, D_ps, V_0, color = colors[i], label = labels[i], linestyle = :dash)
+        Plt.lines!(
+            ax1,
+            D_ps,
+            V_0,
+            color = colors[i],
+            label = labels[i],
+            linestyle = :dash,
+        )
     end
 
     # F_r = 1
-    ax2 =
-        Plt.Axis(fig[1:7, 10:18], title = "Fᵣ = 1", xlabel = "Dₚ (m)", ylabel = "V (m s⁻¹)")
+    ax2 = Plt.Axis(
+        fig[1:7, 10:18],
+        title = "Fᵣ = 1",
+        xlabel = "Dₚ (m)",
+        ylabel = "V (m s⁻¹)",
+    )
     for i in 1:4
         D_ps, V_1, V_1_ϕ = get_values(
             p3,
@@ -104,7 +121,14 @@ function fig1()
             res,
         )
         Plt.lines!(ax2, D_ps, V_1_ϕ, color = colors[i], label = labels[i])
-        Plt.lines!(ax2, D_ps, V_1, color = colors[i], label = labels[i], linestyle = :dash)
+        Plt.lines!(
+            ax2,
+            D_ps,
+            V_1,
+            color = colors[i],
+            label = labels[i],
+            linestyle = :dash,
+        )
     end
 
     Plt.Legend(

docs/src/plots/CloudDiagnostics.jl

@@ -15,8 +15,13 @@ rain = CMP.Rain(FT)
 cloud_liquid = CMP.CloudLiquid(FT)
 cloud_ice = CMP.CloudIce(FT)
 
-override_file =
-    joinpath(pkgdir(CloudMicrophysics), "src", "parameters", "toml", "SB2006_limiters.toml")
+override_file = joinpath(
+    pkgdir(CloudMicrophysics),
+    "src",
+    "parameters",
+    "toml",
+    "SB2006_limiters.toml",
+)
 toml_dict = CP.create_toml_dict(FT; override_file)
 SB = CMP.SB2006(toml_dict)
 SB_no_limiters = CMP.SB2006(toml_dict, false)

docs/src/plots/Frostenberg_fig1.jl

@@ -13,8 +13,8 @@ INPC_range = 10.0 .^ (range(-5, stop = 7, length = 500)) #ice nucleating particl
 
 frequency = [
     IN.INP_concentration_frequency(ip, INPC, T) > 0.015 ?
-    IN.INP_concentration_frequency(ip, INPC, T) : missing for INPC in INPC_range,
-    T in T_range
+    IN.INP_concentration_frequency(ip, INPC, T) : missing for
+    INPC in INPC_range, T in T_range
 ]
 mu = [exp(IN.INP_concentration_mean(T)) for T in T_range]
 
@@ -33,7 +33,13 @@ PL.contourf(
     lw = 0,
 )
 
-PL.plot!(T_range, mu, label = "median INPC", legend = :topright, color = :darkred)
+PL.plot!(
+    T_range,
+    mu,
+    label = "median INPC",
+    legend = :topright,
+    color = :darkred,
+)
 
 PL.plot!(
     repeat([257], 50),

docs/src/plots/HomFreezingPlots.jl

@@ -18,9 +18,18 @@ T_range = range(229.0, stop = 234.5, length = 50)  # air temperature
 x_sulph = Vector{FT}([0.03, 0.04, 0.06])           # wt% sulphuric acid in droplets
 
 # Solving for Δa and J values
-Δa1 = [CMO.a_w_xT(H2SO4_prs, tps, x_sulph[1], T) - CMO.a_w_ice(tps, T) for T in T_range]
-Δa2 = [CMO.a_w_xT(H2SO4_prs, tps, x_sulph[2], T) - CMO.a_w_ice(tps, T) for T in T_range]
-Δa3 = [CMO.a_w_xT(H2SO4_prs, tps, x_sulph[3], T) - CMO.a_w_ice(tps, T) for T in T_range]
+Δa1 = [
+    CMO.a_w_xT(H2SO4_prs, tps, x_sulph[1], T) - CMO.a_w_ice(tps, T) for
+    T in T_range
+]
+Δa2 = [
+    CMO.a_w_xT(H2SO4_prs, tps, x_sulph[2], T) - CMO.a_w_ice(tps, T) for
+    T in T_range
+]
+Δa3 = [
+    CMO.a_w_xT(H2SO4_prs, tps, x_sulph[3], T) - CMO.a_w_ice(tps, T) for
+    T in T_range
+]
 
 J1 = @. CMI.homogeneous_J_cubic(ip.homogeneous, Δa1)
 J2 = @. CMI.homogeneous_J_cubic(ip.homogeneous, Δa2)

docs/src/plots/KnopfAlpert2013_fig5.jl

@@ -14,8 +14,16 @@ illite = CMP.Illite(FT)    # dust type
 
 # Knopf and Alpert 2013 Figure 5A: Cirrus
 # https://doi.org/10.1039/C3FD00035D
-temp =
-    [228.20357, 228.33571, 228.50357, 228.75000, 228.92143, 229.16786, 229.39643, 229.52143]
+temp = [
+    228.20357,
+    228.33571,
+    228.50357,
+    228.75000,
+    228.92143,
+    229.16786,
+    229.39643,
+    229.52143,
+]
 KA13_fig5A_J = [
     70170382.86704,
     12101528.74384,
@@ -43,7 +51,12 @@ J_ABIFM = @. CMI.ABIFM_J(illite, Δa_w) * 1e-4 # converted from SI units to cm^-
 
 # Plot results
 fig = MK.Figure(resolution = (800, 600))
-ax1 = MK.Axis(fig[1, 1], ylabel = "J_het [cm^-2 s^-1]", xlabel = "Temp [K]", yscale = log10)
+ax1 = MK.Axis(
+    fig[1, 1],
+    ylabel = "J_het [cm^-2 s^-1]",
+    xlabel = "Temp [K]",
+    yscale = log10,
+)
 
 MK.ylims!(ax1, FT(1), FT(1e8))
 MK.lines!(

docs/src/plots/MarshallPalmer_distribution.jl

@@ -36,10 +36,14 @@ qᵣ_3 = FT(1.0e-3)
 
 r_range = range(25e-6, stop = 1e-2, length = 1000)
 
-n_r_0 = [Marshall_Palmer_distribution(CMP.Rain(FT), qᵣ_0, ρ, r) for r in r_range]
-n_r_1 = [Marshall_Palmer_distribution(CMP.Rain(FT), qᵣ_1, ρ, r) for r in r_range]
-n_r_2 = [Marshall_Palmer_distribution(CMP.Rain(FT), qᵣ_2, ρ, r) for r in r_range]
-n_r_3 = [Marshall_Palmer_distribution(CMP.Rain(FT), qᵣ_3, ρ, r) for r in r_range]
+n_r_0 =
+    [Marshall_Palmer_distribution(CMP.Rain(FT), qᵣ_0, ρ, r) for r in r_range]
+n_r_1 =
+    [Marshall_Palmer_distribution(CMP.Rain(FT), qᵣ_1, ρ, r) for r in r_range]
+n_r_2 =
+    [Marshall_Palmer_distribution(CMP.Rain(FT), qᵣ_2, ρ, r) for r in r_range]
+n_r_3 =
+    [Marshall_Palmer_distribution(CMP.Rain(FT), qᵣ_3, ρ, r) for r in r_range]
 
 fig = MK.Figure(resolution = (1100, 600))
 ax1 = MK.Axis(