Merge #129

129: Merge mse tables->1 file, print mse table post run r=charleskawczynski a=charleskawczynski This PR should be a nice quality of life improvement for updating the MSE tables. Previously, we had to click into each tab (for each case) and copy the table to the corresponding file With this PR: a new job prints the entire mse table file contents after all cases are complete--so we only need to copy these file contents into the single mse tables file. Co-authored-by: Charles Kawczynski <[email protected]>
CliMA · Aug 16, 2021 · 2fa9779 · 2fa9779
2 parents f034f07 + 267a438
commit 2fa9779
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diff --git a/.buildkite/pipeline.yml b/.buildkite/pipeline.yml
@@ -154,6 +154,18 @@ steps:
       queue: central
       slurm_ntasks: 1
 
+  - wait: ~
+    continue_on_failure: true
+
+  - label: ":robot_face: Print new mse tables"
+    key: "cpu_print_new_mse"
+    command:
+      - "julia --color=yes --project utils/print_new_mse.jl"
+    agents:
+      config: cpu
+      queue: central
+      slurm_ntasks: 1
+
   - wait
 
   - label: ":robot_face: Move main results"

diff --git a/integration_tests/ARM_SGP.jl b/integration_tests/ARM_SGP.jl
@@ -8,21 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["qt_mean"] = 3.7029179410890994e-01
-best_mse["updraft_area"] = 2.0066768291734027e+03
-best_mse["updraft_w"] = 3.3021026158842852e+02
-best_mse["updraft_qt"] = 1.3362770693863471e+01
-best_mse["updraft_thetal"] = 2.7682689602916721e+01
-best_mse["u_mean"] = 8.7998547277817892e+01
-best_mse["tke_mean"] = 6.5902888656341383e+02
-best_mse["temperature_mean"] = 1.4835874987504939e-04
-best_mse["ql_mean"] = 2.5289195067821601e+02
-best_mse["thetal_mean"] = 1.5194012840291993e-04
-best_mse["Hvar_mean"] = 3.6200709711739819e+03
-best_mse["QTvar_mean"] = 2.5763919693088642e+03
+best_mse = all_best_mse["ARM_SGP"]
 
 @testset "ARM_SGP" begin
     case_name = "ARM_SGP"
@@ -40,6 +29,10 @@ best_mse["QTvar_mean"] = 2.5763919693088642e+03
         t_stop = 11 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/Bomex.jl b/integration_tests/Bomex.jl
@@ -8,22 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["qt_mean"] = 9.7923185944396543e-02
-best_mse["updraft_area"] = 6.9825342418932712e+02
-best_mse["updraft_w"] = 3.2817058320329416e+01
-best_mse["updraft_qt"] = 4.2756945036338720e+00
-best_mse["updraft_thetal"] = 2.1546731002204076e+01
-best_mse["v_mean"] = 6.8320914112603603e+01
-best_mse["u_mean"] = 5.3308019185027945e+01
-best_mse["tke_mean"] = 4.2619460317296351e+01
-best_mse["temperature_mean"] = 4.2264960813453363e-05
-best_mse["ql_mean"] = 6.1078690857394591e+00
-best_mse["thetal_mean"] = 4.3075669150884208e-05
-best_mse["Hvar_mean"] = 1.4320193838595969e+03
-best_mse["QTvar_mean"] = 7.4620132655591669e+02
+best_mse = all_best_mse["Bomex"]
 
 @testset "Bomex" begin
     case_name = "Bomex"
@@ -41,6 +29,10 @@ best_mse["QTvar_mean"] = 7.4620132655591669e+02
         t_stop = 6 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/DYCOMS_RF01.jl b/integration_tests/DYCOMS_RF01.jl
@@ -8,22 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["qt_mean"] = 1.6511493474924487e-02
-best_mse["ql_mean"] = 5.2388152463600228e+00
-best_mse["updraft_area"] = 2.3937655332711191e+02
-best_mse["updraft_w"] = 4.2950818025166271e+00
-best_mse["updraft_qt"] = 1.1670622064912242e+00
-best_mse["updraft_thetal"] = 1.2740701334370282e+01
-best_mse["v_mean"] = 3.9746921720562241e+01
-best_mse["u_mean"] = 3.7046560343565211e+01
-best_mse["tke_mean"] = 1.4700070268008988e+01
-best_mse["temperature_mean"] = 2.1532443073348772e-05
-best_mse["thetal_mean"] = 2.2397858591617206e-05
-best_mse["Hvar_mean"] = 8.2677316059854074e+03
-best_mse["QTvar_mean"] = 6.0266525107346490e+02
+best_mse = all_best_mse["DYCOMS_RF01"]
 
 key = "Hvar_mean"
 @testset "DYCOMS_RF01" begin
@@ -42,6 +30,10 @@ key = "Hvar_mean"
         t_stop = 4 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/DryBubble.jl b/integration_tests/DryBubble.jl
@@ -8,17 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["updraft_area"] = 6.8552893703976156e+02
-best_mse["updraft_w"] = 1.6342412689086376e+02
-best_mse["updraft_thetal"] = 3.9780037295736014e-05
-best_mse["u_mean"] = 1.9502448099351233e-27
-best_mse["tke_mean"] = 1.9987696066076023e+05
-best_mse["temperature_mean"] = 3.2539779149902821e-05
-best_mse["thetal_mean"] = 2.5848228458179228e-05
-best_mse["Hvar_mean"] = 7.3771757968047757e+02
+best_mse = all_best_mse["DryBubble"]
 
 @testset "DryBubble" begin
     case_name = "DryBubble"
@@ -30,6 +23,10 @@ best_mse["Hvar_mean"] = 7.3771757968047757e+02
     computed_mse =
         compute_mse_wrapper(case_name, best_mse, ds_tc_filename; plot_comparison = true, t_start = 900, t_stop = 1000)
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/GABLS.jl b/integration_tests/GABLS.jl
@@ -8,18 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-
-best_mse["updraft_thetal"] = 5.0248696023347037e+00
-best_mse["v_mean"] = 4.4593534457868529e+00
-best_mse["u_mean"] = 9.6414943665200035e+00
-best_mse["tke_mean"] = 2.4674095133951375e+00
-best_mse["temperature_mean"] = 8.8584843672667532e-06
-best_mse["thetal_mean"] = 8.7856734759460943e-06
-best_mse["Hvar_mean"] = 1.2892749042279126e+01
-best_mse["QTvar_mean"] = 4.4456710317999498e-01
+best_mse = all_best_mse["GABLS"]
 
 @testset "GABLS" begin
     case_name = "GABLS"
@@ -37,6 +29,10 @@ best_mse["QTvar_mean"] = 4.4456710317999498e-01
         t_stop = 9 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/Nieuwstadt.jl b/integration_tests/Nieuwstadt.jl
@@ -8,17 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["updraft_area"] = 5.9567286602931904e+02
-best_mse["updraft_w"] = 2.6450205443296568e+01
-best_mse["updraft_thetal"] = 3.0475209174359087e+01
-best_mse["u_mean"] = 1.5244498152508007e+02
-best_mse["tke_mean"] = 7.3585026092564277e+01
-best_mse["temperature_mean"] = 1.1872218655217143e-05
-best_mse["thetal_mean"] = 1.2035241147904184e-05
-best_mse["Hvar_mean"] = 1.8640506843913366e+02
+best_mse = all_best_mse["Nieuwstadt"]
 
 @testset "Nieuwstadt" begin
     case_name = "Nieuwstadt"
@@ -36,6 +29,10 @@ best_mse["Hvar_mean"] = 1.8640506843913366e+02
         t_stop = 8 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/Rico.jl b/integration_tests/Rico.jl
@@ -8,22 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["qt_mean"] = 3.6183738581707564e-01
-best_mse["updraft_area"] = 1.9158389580482219e+03
-best_mse["updraft_w"] = 1.7058718197542106e+02
-best_mse["updraft_qt"] = 1.5449827839901584e+01
-best_mse["updraft_thetal"] = 6.3602297256853468e+01
-best_mse["v_mean"] = 1.0630514621668171e+02
-best_mse["u_mean"] = 1.1443613156137732e+02
-best_mse["tke_mean"] = 3.1910880264617197e+02
-best_mse["temperature_mean"] = 1.8245777363727451e-04
-best_mse["ql_mean"] = 2.2808514972615623e+02
-best_mse["thetal_mean"] = 1.5458919462734390e-04
-best_mse["Hvar_mean"] = 1.0744099006973258e+04
-best_mse["QTvar_mean"] = 4.7454844707739849e+03
+best_mse = all_best_mse["Rico"]
 
 @testset "Rico" begin
     case_name = "Rico"
@@ -41,6 +29,10 @@ best_mse["QTvar_mean"] = 4.7454844707739849e+03
         t_stop = 24 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/SP.jl b/integration_tests/SP.jl
@@ -8,21 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["qt_mean"] = 3.5073036121827599e+00
-best_mse["updraft_area"] = 3.9071034998892715e+00
-best_mse["updraft_w"] = 9.3648209541424188e-01
-best_mse["updraft_qt"] = 1.3868858637940826e+00
-best_mse["updraft_thetal"] = 1.0515272505644156e-01
-best_mse["v_mean"] = 4.6000262200176228e-01
-best_mse["u_mean"] = 7.3724093159961937e-05
-best_mse["tke_mean"] = 4.7833665685836724e-01
-best_mse["temperature_mean"] = 6.8550010657332516e-07
-best_mse["thetal_mean"] = 5.1299556226337377e-07
-best_mse["Hvar_mean"] = 3.1719859098824500e+01
-best_mse["QTvar_mean"] = 3.9762684439302052e+00
+best_mse = all_best_mse["SP"]
 
 @testset "SP" begin
     case_name = "SP"
@@ -34,6 +23,10 @@ best_mse["QTvar_mean"] = 3.9762684439302052e+00
     computed_mse =
         compute_mse_wrapper(case_name, best_mse, ds_tc_filename; plot_comparison = true, t_start = 0, t_stop = 2 * 3600)
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/Soares.jl b/integration_tests/Soares.jl
@@ -8,19 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["qt_mean"] = 1.4966760733610701e-01
-best_mse["updraft_area"] = 4.4752734228297282e+02
-best_mse["updraft_w"] = 2.1338748581172648e+01
-best_mse["updraft_qt"] = 1.1050242532811241e+01
-best_mse["updraft_thetal"] = 2.2394553996747693e+01
-best_mse["u_mean"] = 7.3068371493591656e+02
-best_mse["tke_mean"] = 5.9234734475094214e+01
-best_mse["temperature_mean"] = 1.1583915526092593e-05
-best_mse["thetal_mean"] = 1.2172922371309679e-05
-best_mse["Hvar_mean"] = 2.2601616515636465e+02
+best_mse = all_best_mse["Soares"]
 
 @testset "Soares" begin
     case_name = "Soares"
@@ -38,6 +29,10 @@ best_mse["Hvar_mean"] = 2.2601616515636465e+02
         t_stop = 8 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/TRMM_LBA.jl b/integration_tests/TRMM_LBA.jl
@@ -8,25 +8,13 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
 # Note: temperatures in this case become extremely low.
 CLIMAParameters.Planet.T_freeze(::EarthParameterSet) = 100.0
 
-best_mse = OrderedDict()
-best_mse["qt_mean"] = 2.1180570149373197e+00
-best_mse["updraft_area"] = 2.2911123097125790e+04
-best_mse["updraft_w"] = 9.9122631422628353e+02
-best_mse["updraft_qt"] = 3.0750107437154107e+01
-best_mse["updraft_thetal"] = 1.1001770046016014e+02
-best_mse["v_mean"] = 2.9250578870751696e+02
-best_mse["u_mean"] = 1.6873177041648653e+03
-best_mse["tke_mean"] = 9.3810901861977175e+02
-best_mse["temperature_mean"] = 8.1897112533893871e-04
-best_mse["ql_mean"] = 7.3150520783875129e+02
-best_mse["thetal_mean"] = 8.2746696205323478e-03
-best_mse["Hvar_mean"] = 3.5185010182273427e+03
-best_mse["QTvar_mean"] = 1.7745546315637387e+03
+best_mse = all_best_mse["TRMM_LBA"]
 
 @testset "TRMM_LBA" begin
     case_name = "TRMM_LBA"
@@ -44,6 +32,10 @@ best_mse["QTvar_mean"] = 1.7745546315637387e+03
         t_stop = 6 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end

diff --git a/integration_tests/life_cycle_Tan2018.jl b/integration_tests/life_cycle_Tan2018.jl
@@ -8,22 +8,10 @@ using Test
 include(joinpath("utils", "main.jl"))
 include(joinpath("utils", "generate_namelist.jl"))
 include(joinpath("utils", "compute_mse.jl"))
+include(joinpath("utils", "mse_tables.jl"))
 using .NameList
 
-best_mse = OrderedDict()
-best_mse["qt_mean"] = 5.2649429859732335e-03
-best_mse["ql_mean"] = 8.3701130817214975e-01
-best_mse["updraft_area"] = 7.0432677991444692e-01
-best_mse["updraft_w"] = 5.8558890484998805e-01
-best_mse["updraft_qt"] = 1.1615774284759468e-01
-best_mse["updraft_thetal"] = 6.2885863821116435e-05
-best_mse["v_mean"] = 2.4748668316753225e-01
-best_mse["u_mean"] = 7.1361729071471190e-04
-best_mse["tke_mean"] = 2.0664613818639557e-01
-best_mse["temperature_mean"] = 2.5719773912461363e-06
-best_mse["thetal_mean"] = 2.4566431564965449e-06
-best_mse["Hvar_mean"] = 2.1515252048343000e+03
-best_mse["QTvar_mean"] = 1.1458013034475746e+03
+best_mse = all_best_mse["life_cycle_Tan2018"]
 
 @testset "life_cycle_Tan2018" begin
     case_name = "life_cycle_Tan2018"
@@ -41,6 +29,10 @@ best_mse["QTvar_mean"] = 1.1458013034475746e+03
         t_stop = 6 * 3600,
     )
 
+    open("computed_mse_$case_name.json", "w") do io
+        JSON.print(io, computed_mse)
+    end
+
     for k in keys(best_mse)
         test_mse(computed_mse, best_mse, k)
     end