almost...

experiments/calibration/minimal_working_example/minimal_working_example.jl

@@ -35,6 +35,7 @@ end
 
 # "truth" target data to calibrate on
 lhf_target = ClimaLand_lhf(141.0) # our default for Ozark, in sqrt(Pa). This is equal to 4.46 sqrt(kPa).
+lhf_target_Obs = EKP.Observation(lhf_target, EKP.I(length(lhf_target)), "lhf") # target data needs to be of type ::OS
 
 # parameters prior
 prior_g1 = EKP.constrained_gaussian("g1", 221, 100, 0, Inf) # mean of 221 sqrt(Pa) = 7 sqrt(kPa), std of 100 (3 kPa)
@@ -48,19 +49,19 @@ rng = Random.MersenneTwister(rng_seed) # Ollie: pseudo-random number?
 
 initial_ensemble = EKP.construct_initial_ensemble(rng, prior_g1, N_ensemble) # Ollie: what does this do? 5 random values of g1?
 
-ensemble_kalman_process = EKP.EnsembleKalmanProcess(initial_ensemble, lhf_target, EKP.Inversion(); rng = rng) # Ollie: observation_series (lhf_target) needs to be ::OS? how?, do we need Γ? Do we need rng?
+ensemble_kalman_process = EKP.EnsembleKalmanProcess(initial_ensemble, lhf_target_Obs, EKP.Inversion(); rng = rng) # Ollie: observation_series (lhf_target) needs to be ::OS? how?, do we need Γ? Do we need rng?
 
 # We are now ready to carry out the inversion. At each iteration, we get the ensemble from the last iteration, apply
 # ClimaLand_lhf(g1) to each ensemble member, and apply the Kalman update to the ensemble.
 
 for i in 1:N_iterations
-    params_i = get_ϕ_final(prior_g1, ensemble_kalman_process)
+    params_i = EKP.get_ϕ_final(prior_g1, ensemble_kalman_process) # 5 values of g1
 
-    ClimaLand_ens = hcat([ClimaLand_lhf(params_i[:, i]) for i in 1:N_ensemble]...)
+    ClimaLand_ens = hcat([ClimaLand_lhf(params_i[i]) for i in 1:N_ensemble]...) # adjusted for 1 param
 
     EKP.update_ensemble!(ensemble_kalman_process, ClimaLand_ens)
-end
+end # This runs the simulation a bunch of times, but eventually crashed... ERROR: LinearAlgebra.SingularException(2)
 
 # Done! Here are the parameters:
 
-final_ensemble = get_ϕ_final(prior_g1, ensemble_kalman_process)
+final_ensemble = EKP.get_ϕ_final(prior_g1, ensemble_kalman_process)