WIP

.buildkite/pipeline.yml

@@ -137,7 +137,10 @@ steps:
       - label: "Global Bucket on CPU (functional albedo)"
         key: "global_bucket_function_cpu"
         command: "julia --color=yes --project=.buildkite experiments/standalone/Bucket/global_bucket_function.jl"
-        artifact_paths: "experiments/standalone/Bucket/artifacts/*cpu*"
+        artifact_paths: 
+          - "experiments/standalone/Bucket/artifacts/*cpu*"
+          - "experiments/global_bucket_function/output_active/*.png"
+
 
       - label: "Global Bucket on CPU (static map albedo)"
         key: "global_bucket_staticmap_cpu"

experiments/standalone/Bucket/global_bucket_function.jl

@@ -158,19 +158,6 @@ prob = SciMLBase.ODEProblem(
     p,
 );
 
-saveat = collect(t0:(Δt * 3):tf);
-saved_values = (;
-    t = Array{Float64}(undef, length(saveat)),
-    saveval = Array{NamedTuple}(undef, length(saveat)),
-);
-saving_cb = ClimaLand.NonInterpSavingCallback(saved_values, saveat);
-updateat = copy(saveat)
-updatefunc = ClimaLand.make_update_drivers(bucket_atmos, bucket_rad)
-driver_cb = ClimaLand.DriverUpdateCallback(updateat, updatefunc)
-cb = SciMLBase.CallbackSet(driver_cb, saving_cb)
-
-
-
 # ClimaDiagnostics
 base_output_dir = "global_bucket_function/"
 output_dir =
@@ -187,13 +174,13 @@ diagnostic_handler =
 
 diag_cb = ClimaDiagnostics.DiagnosticsCallback(diagnostic_handler)
 
-sol_test = SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)
+sol = ClimaComms.@time ClimaComms.device() SciMLBase.solve(prob, ode_algo; dt = Δt, callback = diag_cb)
 
 #### ClimaAnalysis ####
 
 # all
 simdir = ClimaAnalysis.SimDir(output_dir)
-for short_name in [
+short_names_2D = [
     "alpha",
     "rn",
     "tsfc",
@@ -203,122 +190,43 @@ for short_name in [
     "shf",
     "vflux",
     "rhosfc",
-    #"tsoil", # 3D
     "wsoil",
     "wsfc",
     "ssfc",
 ]
+short_names_3D = ["tsoil"]
+for short_name in vcat(short_names_2D..., short_names_3D...)
     var = get(simdir; short_name)
-    fig = CairoMakie.Figure(size = (400, 600))
-    viz.plot!(fig, var, time = 1 * 86400)
+    fig = CairoMakie.Figure(size = (800, 600))
+    kwargs = short_name in short_names_2D ? Dict() : Dict(:z => 1)
+    viz.plot!(fig, var, lat = 0; kwargs...)
     CairoMakie.save(joinpath(output_dir, "$short_name.png"), fig)
 end
 
-# TO DO: this works, but all variables are constant in space. 
-# Should we use another Bucket experiment?
-###########
-
-sol = ClimaComms.@time ClimaComms.device() SciMLBase.solve(
-    prob,
-    ode_algo;
-    dt = Δt,
-    saveat = saveat,
-    callback = cb,
-);
-
 # Interpolate to grid
 space = axes(coords.surface)
 longpts = range(-180.0, 180.0, 21)
 latpts = range(-90.0, 90.0, 21)
 hcoords = [Geometry.LatLongPoint(lat, long) for long in longpts, lat in latpts]
 remapper = Remapping.Remapper(space, hcoords)
 
-W = [
-    Array(Remapping.interpolate(remapper, sol.u[k].bucket.W)) for
-    k in 1:length(sol.t)
-];
-Ws = [
-    Array(Remapping.interpolate(remapper, sol.u[k].bucket.Ws)) for
-    k in 1:length(sol.t)
-];
-σS = [
-    Array(Remapping.interpolate(remapper, sol.u[k].bucket.σS)) for
-    k in 1:length(sol.t)
-];
-T_sfc = [
-    Array(
-        Remapping.interpolate(remapper, saved_values.saveval[k].bucket.T_sfc),
-    ) for k in 1:length(sol.t)
-];
-evaporation = [
-    Array(
-        Remapping.interpolate(
-            remapper,
-            saved_values.saveval[k].bucket.turbulent_fluxes.vapor_flux,
-        ),
-    ) for k in 1:length(sol.t)
-];
-F_sfc = [
+W = 
+Array(Remapping.interpolate(remapper, sol.u[end].bucket.W))
+Ws = 
+    Array(Remapping.interpolate(remapper, sol.u[end].bucket.Ws))
+σS = 
+    Array(Remapping.interpolate(remapper, sol.u[end].bucket.σS))
+T_sfc = 
     Array(
-        Remapping.interpolate(
-            remapper,
-            saved_values.saveval[k].bucket.R_n .+
-            saved_values.saveval[k].bucket.turbulent_fluxes.lhf .+
-            saved_values.saveval[k].bucket.turbulent_fluxes.shf,
-        ),
-    ) for k in 1:length(sol.t)
-];
+          Remapping.interpolate(remapper, prob.p.bucket.T_sfc))
+
 
 # save prognostic state to CSV - for comparison between
 # GPU and CPU output
 device_suffix =
     typeof(ClimaComms.context().device) <: ClimaComms.CPUSingleThreaded ?
     "cpu" : "gpu"
 open(joinpath(outdir, "tf_state_$device_suffix.txt"), "w") do io
-    writedlm(io, hcat(T_sfc[end][:], W[end][:], Ws[end][:], σS[end][:]), ',')
+    writedlm(io, hcat(T_sfc[:], W[:], Ws[:], σS[:]), ',')
 end;
-# animation settings
-nframes = length(W)
-framerate = 2
-fig_ts = Figure(size = (1000, 1000))
-for (i, (field_ts, field_name)) in enumerate(
-    zip(
-        [W, σS, T_sfc, evaporation, F_sfc],
-        ["W", "σS", "T_sfc", "evaporation", "F_sfc"],
-    ),
-)
-    if anim_plots
-        fig = Figure(size = (1000, 1000))
-        ax = Axis(
-            fig[1, 1],
-            xlabel = "Longitude",
-            ylabel = "Latitude",
-            title = field_name,
-        )
-        clims = compute_extrema(field_ts)
-        CairoMakie.Colorbar(fig[:, end + 1], colorrange = clims)
-        outfile = joinpath(
-            outdir,
-            string("anim_$(device_suffix)_", field_name, ".mp4"),
-        )
-        record(fig, outfile, 1:nframes; framerate = framerate) do frame
-            CairoMakie.heatmap!(
-                longpts,
-                latpts,
-                field_ts[frame],
-                colorrange = clims,
-            )
-        end
-    end
-    # Plot the timeseries of the mean value as well.
-    xlabel = i == 5 ? "Time (days)" : ""
-    ax2 = Axis(
-        fig_ts[i, 1],
-        xlabel = xlabel,
-        ylabel = field_name,
-        title = "Global bucket with analytic albedo",
-    )
-    CairoMakie.lines!(ax2, sol.t ./ 3600 ./ 24, [mean(x) for x in field_ts])
-end
-outfile = joinpath(outdir, string("ts_$device_suffix.png"))
-CairoMakie.save(outfile, fig_ts)
+