format

src/Observations.jl

@@ -126,25 +126,25 @@ function Observation(obs_dict::Dict)
             snew = samples_tmp  # [[1,2,3]]
         end
     end
-    
+
     if !isa(covariances, AbstractVector) # [2 1;1 2] -> [[2 1;1 2]]
         ctmp = [covariances]
     else
         ctmp = covariances
     end
     # additionally provide a dimension for UniformScalings for covariances
     ctmp2 = []
-    for (id,c) in enumerate(ctmp)
-        if isa(c,UniformScaling)
-            push!(ctmp2, Diagonal(c.λ*ones(length(snew[id])))) # get dim from samples
+    for (id, c) in enumerate(ctmp)
+        if isa(c, UniformScaling)
+            push!(ctmp2, Diagonal(c.λ * ones(length(snew[id])))) # get dim from samples
         else
             push!(ctmp2, c)
         end
-    end 
+    end
     # then promote
     T = promote_type((typeof(c) for c in ctmp2)...)
     cnew = [convert(T, c) for c in ctmp2] # to re-infer eltype
-    
+
     if !("inv_covariances" ∈ collect(keys(obs_dict)))
         inv_covariances = []
         for c in cnew # ensures its a vector
@@ -159,17 +159,17 @@ function Observation(obs_dict::Dict)
         ictmp = inv_covariances
     end
     # additionally provide a dimension for UniformScalings
-    ictmp2=[]
-    for (id,c) in enumerate(ictmp)
-        if isa(c,UniformScaling)
-            push!(ictmp2, Diagonal(c.λ*ones(length(snew[id])))) # get dim from samples
+    ictmp2 = []
+    for (id, c) in enumerate(ictmp)
+        if isa(c, UniformScaling)
+            push!(ictmp2, Diagonal(c.λ * ones(length(snew[id])))) # get dim from samples
         else
             push!(ictmp2, c)
         end
-    end 
+    end
     T = promote_type((typeof(c) for c in ictmp2)...)
     icnew = [convert(T, c) for c in ictmp2] # to re-infer eltype
-    
+
     if !isa(names, AbstractVector) # "name" -> ["name"]
         nnew = [names]
     else
@@ -292,7 +292,7 @@ function get_obs_noise_cov_inv(o::Observation; build = true)
         inv_covs = get_inv_covs(o)
         inv_cov_full = zeros(maximum(indices[end]), maximum(indices[end]))
         for (idx, c) in zip(indices, inv_covs)
-            inv_cov_full[idx, idx] .= c            
+            inv_cov_full[idx, idx] .= c
         end
 
         return inv_cov_full