From 49df9330b6857b4e266d09c2e6f01891f0e2e5af Mon Sep 17 00:00:00 2001
From: Eviatar Bach <eviatarbach@protonmail.com>
Date: Mon, 12 Dec 2022 05:35:09 -0800
Subject: [PATCH] Delete test_lorenz63.jl

---
 test_lorenz63.jl | 89 ------------------------------------------------
 1 file changed, 89 deletions(-)
 delete mode 100644 test_lorenz63.jl

diff --git a/test_lorenz63.jl b/test_lorenz63.jl
deleted file mode 100644
index 04f1600..0000000
--- a/test_lorenz63.jl
+++ /dev/null
@@ -1,89 +0,0 @@
-using Statistics
-using LinearAlgebra
-using Random
-
-using Distributions
-
-include("etkf.jl")
-import .ETKF
-
-include("ens_forecast.jl")
-import .ens_forecast
-
-include("models.jl")
-import .Models
-
-include("integrators.jl")
-import .Integrators
-
-Random.seed!(1)
-
-models = [Models.lorenz63_err.func, Models.lorenz63_err3.func]
-model_true = Models.lorenz63_true.func
-n_models = length(models)
-D = 3
-obs_ops = [I(D), I(D)]
-H = I(D)
-ens_sizes = [40, 40]
-model_sizes = [D, D]
-integrator = Integrators.rk4
-x0 = rand(D)
-t0 = 0.0
-Δt = 0.05
-outfreq = 1
-window = 1
-transient = 2000
-x0 = integrator(models[1], x0, t0, transient*outfreq*Δt, Δt, inplace=false)
-R = Symmetric(diagm(0=>(0.1*std(x0, dims=1)[:]).^2))
-x0 = x0[end, :]
-#ensembles = [ens_forecast.init_ens(model=models[model], integrator=integrator,
-#                                   x0=x0, t0=t0, outfreq=outfreq, Δt=Δt,
-#                                   ens_size=ens_sizes[model]) for model=1:n_models]
-#x0 = ensembles[1][:, end]
-n_cycles = 1000
-ρ = 1e-3
-
-#model_errs = [ens_forecast.model_err(model_true=model_true, model_err=models[model],
-#                                     integrator=integrator, x0=x0, t0=t0,
-#                                     outfreq=outfreq, Δt=Δt, window=window,
-#                                     n_samples=100)[1] for model=1:n_models]
-
-infos = Vector(undef, n_models)
-for model=1:length(models)
-    model_errs = [0.1*diagm(0=>ones(D))]#Vector{Matrix{Float64}}(undef, 1)]
-    biases = [zeros(D)]
-
-    ensembles = [x0 .+ rand(MvNormal(R), ens_sizes[model])]
-
-    info, _, _ = ens_forecast.mmda(x0=x0, ensembles=ensembles,
-                         models=[models[model]],
-                         model_true=model_true,
-                         obs_ops=[obs_ops[model]], H=H,
-                         model_errs=model_errs, biases=biases, integrator=integrator,
-                         ens_sizes=[ens_sizes[model]], Δt=Δt, window=window,
-                         n_cycles=n_cycles, outfreq=outfreq,
-                         model_sizes=model_sizes, R=R, ρ=ρ)
-    infos[model] = info
-end
-
-#model_errs = [0.1*diagm(0=>ones(D)), 0.1*diagm(0=>ones(D))]
-# #biases = [nothing, nothing]
-biases = [zeros(3), zeros(3)]
-
-# #incs1 = hcat(info1.increments...)
-# #incs2 = hcat(info2.increments...)
-# #model_errs = [cov(incs1'), cov(incs2')]
-# #biases = [mean(incs1, dims=2)[:], mean(incs2, dims=2)[:]]
-# #biases = [zeros(3), zeros(3)]
-
-# #ensembles = [x0 .+ rand(MvNormal(R), ens_sizes[model]) for model=1:n_models]
-ensembles = [x0 .+ rand(MvNormal(R), ens_sizes[model]) for model=1:n_models]
-
-model_errs = [mean(infos[1].Q_hist[500:end]), mean(infos[2].Q_hist[500:end])]
-
-info_mm, _, _ = ens_forecast.mmda(x0=x0, ensembles=ensembles, models=models,
-                         model_true=model_true, obs_ops=obs_ops, H=H,
-                         model_errs=model_errs, biases=biases, integrator=integrator,
-                         ens_sizes=ens_sizes, Δt=Δt, window=window,
-                         n_cycles=n_cycles, outfreq=outfreq,
-                         model_sizes=model_sizes, R=R, ρ=ρ, fixed=false)
\ No newline at end of file