format

src/MarkovChainMonteCarlo.jl

@@ -62,7 +62,7 @@ function to_decorrelated(data::AbstractVector{FT}, em::Emulator{FT}) where {FT <
     return [vec(out_data)]
 end
 
-function to_decorrelated(data::AVV, em::Emulator{FT}) where {AVV <: AbstractVector, FT <: AbstractFloat} 
+function to_decorrelated(data::AVV, em::Emulator{FT}) where {AVV <: AbstractVector, FT <: AbstractFloat}
     # method for vector of samples
     if isa(data[1], AbstractVector)
         return [vec(to_decorrelated(reshape(d, :, 1), em)) for d in data] # calls matrix version
@@ -159,27 +159,31 @@ $(DocStringExtensions.TYPEDSIGNATURES)
 
 Defines the internal log-density function over a vector of observation samples using an assumed conditionally indepedent likelihood, that is with a log-likelihood of `ℓ(y,θ) = sum^n_i log( p(y_i|θ) )`. 
 """
-function emulator_log_density_model(θ, prior::ParameterDistribution, em::Emulator{FT}, obs_vec::AV) where {FT <: AbstractFloat, AV <: AbstractVector}
-
+function emulator_log_density_model(
+    θ,
+    prior::ParameterDistribution,
+    em::Emulator{FT},
+    obs_vec::AV,
+) where {FT <: AbstractFloat, AV <: AbstractVector}
+
     # θ: model params we evaluate at; in original coords.
     # transform_to_real = false means g, g_cov, obs_sample are in decorrelated coords.
-    
+
     # Recall predict() written to return multiple N_samples: expects input to be a 
     # Matrix with N_samples columns. Returned g is likewise a Matrix, and g_cov is a
     # Vector of N_samples covariance matrices. For MH, N_samples is always 1, so we 
     # have to reshape()/re-cast input/output; simpler to do here than add a 
     # predict() method.
-    g, g_cov =
-        Emulators.predict(em, reshape(θ, :, 1), transform_to_real = false, vector_rf_unstandardize = false)
+    g, g_cov = Emulators.predict(em, reshape(θ, :, 1), transform_to_real = false, vector_rf_unstandardize = false)
     #TODO vector_rf will always unstandardize, but other methods will not, so we require this additional flag.
-    
+
     if isa(g_cov[1], Real)
-        
-        return 1.0/length(obs_vec)*sum([logpdf(MvNormal(obs, g_cov[1] * I), vec(g)) for obs in obs_vec]) + logpdf(prior, θ)
+
+        return 1.0 / length(obs_vec) * sum([logpdf(MvNormal(obs, g_cov[1] * I), vec(g)) for obs in obs_vec]) + logpdf(prior, θ)
     else
-        return 1.0/length(obs_vec)*sum([logpdf(MvNormal(obs, g_cov[1]), vec(g)) for obs in obs_vec]) + logpdf(prior, θ)
+        return 1.0 / length(obs_vec) * sum([logpdf(MvNormal(obs, g_cov[1]), vec(g)) for obs in obs_vec]) + logpdf(prior, θ)
     end
-    
+
 end
 
 """
@@ -196,9 +200,7 @@ function EmulatorPosteriorModel(
     obs_vec::AV,
 ) where {FT <: AbstractFloat, AV <: AbstractVector}
 
-    return AdvancedMH.DensityModel(
-        x -> emulator_log_density_model(x, prior, em, obs_vec)
-    )
+    return AdvancedMH.DensityModel(x -> emulator_log_density_model(x, prior, em, obs_vec))
 end
 
 # ------------------------------------------------------------------------------------------
@@ -228,7 +230,7 @@ end
 MCMCState(model::AdvancedMH.DensityModel, params, accepted = true) =
     MCMCState(params, logdensity(model, params), accepted)
 
-AdvancedMH.logdensity(model::AdvancedMH.DensityModel, t::MCMCState) = t.log_density 
+AdvancedMH.logdensity(model::AdvancedMH.DensityModel, t::MCMCState) = t.log_density
 
 # AdvancedMH.transition() is only called to create a new proposal, so create a MCMCState
 # with accepted = true since that object will only be used if proposal is accepted.
@@ -278,13 +280,13 @@ function AbstractMCMC.step(
     current_state::MCMCState;
     stepsize::FT = 1.0,
     kwargs...,
-    ) where {FT <: AbstractFloat}
+) where {FT <: AbstractFloat}
     # Generate a new proposal.
     new_params = AdvancedMH.propose(rng, sampler, model, current_state; stepsize = stepsize)
     # Calculate the log acceptance probability and the log density of the candidate.
-    new_log_density = AdvancedMH.logdensity(model, new_params) 
+    new_log_density = AdvancedMH.logdensity(model, new_params)
     log_α =
-        new_log_density - AdvancedMH.logdensity(model, current_state) + 
+        new_log_density - AdvancedMH.logdensity(model, current_state) +
         AdvancedMH.logratio_proposal_density(sampler, current_state, new_params)
 
     # Decide whether to return the previous params or the new one.
@@ -402,7 +404,7 @@ AbstractMCMC's terminology).
 # Fields
 $(DocStringExtensions.TYPEDFIELDS)
 """
-struct MCMCWrapper{ AMorAV <: Union{AbstractVector,AbstractMatrix}, AV <: AbstractVector }
+struct MCMCWrapper{AMorAV <: Union{AbstractVector, AbstractMatrix}, AV <: AbstractVector}
     "[`ParameterDistribution`](https://clima.github.io/EnsembleKalmanProcesses.jl/dev/parameter_distributions/) object describing the prior distribution on parameter values."
     prior::ParameterDistribution
     "A vector, matrix, or vector or vectors describing the observation(s) provided by the user."
@@ -450,10 +452,10 @@ function MCMCWrapper(
     init_params::AV,
     burnin::Int = 0,
     kwargs...,
-    ) where {AV <: AbstractVector,  AMorAV <: Union{AbstractVector,AbstractMatrix}}
+) where {AV <: AbstractVector, AMorAV <: Union{AbstractVector, AbstractMatrix}}
 
     # decorrelate observations into a vector
-    decorrelated_obs = to_decorrelated(observation, em) 
+    decorrelated_obs = to_decorrelated(observation, em)
 
     log_posterior_map = EmulatorPosteriorModel(prior, em, decorrelated_obs)
     mh_proposal_sampler = MetropolisHastingsSampler(mcmc_alg, prior)