re-organize

Author: lazarusA

.gitignore

@@ -3,4 +3,5 @@ tmp_*
 /Manifest.toml
 .vscode
 Manifest.toml
-.DS_Store
+.DS_Store
+*.csv

Project.toml

@@ -9,7 +9,6 @@ Chain = "8be319e6-bccf-4806-a6f7-6fae938471bc"
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 DataFrameMacros = "75880514-38bc-4a95-a458-c2aea5a3a702"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-DimensionalData = "0703355e-b756-11e9-17c0-8b28908087d0"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
@@ -27,9 +26,8 @@ Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 AxisKeys = "0.2"
 Chain = "0.6"
 ChainRulesCore = "1.25.1"
-DataFrames = "1"
 DataFrameMacros = "0.4"
-DimensionalData = "0.29"
+DataFrames = "1"
 Flux = "0.16"
 ForwardDiff = "1.0.1"
 Lux = "1.12.4"

projects/RbQ10/Project.toml

@@ -0,0 +1,17 @@
+[deps]
+AlgebraOfGraphics = "cbdf2221-f076-402e-a563-3d30da359d67"
+AxisKeys = "94b1ba4f-4ee9-5380-92f1-94cde586c3c5"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+EasyHybrid = "61bb816a-e6af-4913-ab9e-91bff2e122e3"
+GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
+LuxCore = "bb33d45b-7691-41d6-9220-0943567d0623"
+MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
+Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
+Optimization = "7f7a1694-90dd-40f0-9382-eb1efda571ba"
+OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
+Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"

examples/Q10.jl projects/RbQ10/Q10.jlexamples/Q10.jl renamed to projects/RbQ10/Q10.jl

@@ -37,8 +37,8 @@ end
 
 with_theme(theme_light()) do 
     fig = Figure(; size = (1200, 600))
-    ax_train = Axis(fig[1, 1], title = "training")
-    ax_val = Axis(fig[2, 1], title = "validation")
+    ax_train = Makie.Axis(fig[1, 1], title = "training")
+    ax_val = Makie.Axis(fig[2, 1], title = "validation")
     lines!(ax_train, out.ŷ_train[:], color=:orangered, label = "prediction")
     lines!(ax_train, out.y_train[:], color=:dodgerblue, label ="observation")
     # validation
@@ -51,7 +51,7 @@ end
 
 with_theme(theme_light()) do 
     fig = Figure(; size = (1200, 300))
-    ax = Axis(fig[1,1], title = "Loss",
+    ax = Makie.Axis(fig[1,1], title = "Loss",
         yscale=log10, xscale=log10
         )
     lines!(ax, out.train_history, color=:orangered, label = "train")
@@ -69,7 +69,7 @@ ŷ, RbQ10_st = LuxCore.apply(RbQ10, ds_k, out.ps, out.st)
 
 with_theme(theme_light()) do 
     fig = Figure(; size = (1200, 600))
-    ax_train = Axis(fig[1, 1], title = "full time series")
+    ax_train = Makie.Axis(fig[1, 1], title = "full time series")
     lines!(ax_train, ŷ[:], color=:orangered, label = "prediction")
     lines!(ax_train, yobs_all[:], color=:dodgerblue, label ="observation")
     axislegend(ax_train; position=:lt)

examples/Q10_data.jl projects/RbQ10/Q10_data.jlexamples/Q10_data.jl renamed to projects/RbQ10/Q10_data.jl

@@ -55,6 +55,7 @@ opt_prob = OptimizationProblem(opt_func, ps_ca, dataloader)
 
 epochs = 25
 res_adam = solve(opt_prob, Optimisers.Adam(0.001); callback, epochs)
+
 # res_shopia = solve(opt_prob, Optimization.Sophia(); callback, maxiters=epochs)
 
 # ! finetune a bit with L-BFGS