format

CliMA · Sep 6, 2024 · e29c7d3 · e29c7d3
1 parent a113479
commit e29c7d3
Showing 1 changed file with 6 additions and 6 deletions.
diff --git a/examples/Darcy/emulate_sample.jl b/examples/Darcy/emulate_sample.jl
@@ -34,11 +34,11 @@ function main()
         println("case: ", case)
         min_iter = 1
         max_iter = 5 # number of EKP iterations to use data from is at most this
-        
+
         exp_name = "darcy"
         rng_seed = 940284
         rng = Random.MersenneTwister(rng_seed)
-        
+
         # loading relevant data
         homedir = pwd()
         println(homedir)
@@ -64,7 +64,7 @@ function main()
         N, L = 80, 1.0
         pts_per_dim = LinRange(0, L, N)
         obs_ΔN = 10
-        darcy = Setup_Param(pts_per_dim, obs_ΔN, truth_params_constrained )
+        darcy = Setup_Param(pts_per_dim, obs_ΔN, truth_params_constrained)
 
 
         n_params = length(truth_params) # "input dim"
@@ -104,7 +104,7 @@ function main()
             retained_svd_frac = retained_svd_frac,
             decorrelate = decorrelate,
         )
-        optimize_hyperparameters!(emulator, kernbounds = [fill(-1e2, n_params+1), fill(1e2, n_params+1)])
+        optimize_hyperparameters!(emulator, kernbounds = [fill(-1e2, n_params + 1), fill(1e2, n_params + 1)])
 
         # Check how well the Gaussian Process regression predicts on the
         # true parameters
@@ -134,7 +134,7 @@ function main()
 
         # First let's run a short chain to determine a good step size
         mcmc = MCMCWrapper(RWMHSampling(), truth_sample, prior, emulator; init_params = u0)
-#        mcmc = MCMCWrapper(pCNMHSampling(), truth_sample, prior, emulator; init_params = u0)
+        #        mcmc = MCMCWrapper(pCNMHSampling(), truth_sample, prior, emulator; init_params = u0)
         new_step = optimize_stepsize(mcmc; init_stepsize = 0.1, N = 2000, discard_initial = 0)
 
         # Now begin the actual MCMC
@@ -185,7 +185,7 @@ function main()
 
         h_2d = solve_Darcy_2D(darcy, κ_ens_mean)
         p3 = contour(pts_per_dim, pts_per_dim, h_2d', fill = true, levels = 15, title = "pressure", colorbar = true)
-        
+
         l = @layout [a b c]
         plt = plot(p1, p2, p3, layout = l)
         savefig(plt, joinpath(data_save_directory, "$(case)_posterior_pointwise_uq.png"))